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1. Impact of New-Onset Atrial Fibrillation on Mortality in Critically Ill Patients

Author

Zhang HD, Ding L, Mi LJ, Zhang AK, Liu YD, Li XN, Yan XX, Shen YJ, and Tang M

Subjects
atrial fibrillation, mortality, intensive care unit, Infectious and parasitic diseases, RC109-216
Abstract

Hong-Da Zhang,&ast; Lei Ding,&ast; Li-Jie Mi, Ai-Kai Zhang, Yuan-Dong Liu, Xiang-Nan Li, Xin-Xin Yan, Yu-Jing Shen, Min Tang State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100037, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Min Tang; Yu-Jing Shen, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, 167 Beilishi Road, Xicheng District, Beijing, 100037, People’s Republic of China, Email doctortangmin@yeah.net; shenyj0328@126.comBackground: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia in critically ill patients and significantly impacts mortality. This study sought to evaluate the impact of new-onset AF on mortality in a critically ill population.Methods: This study identified 48018 adult patients admitted to the ICU from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients were categorized as no AF, pre-existing AF, or new-onset AF. We analyzed mortality at 3 months, 6 months, and 1 year.Results: Overall, 31,562 (65.73%) patients had no AF, 4877 (10.16%) had pre-existing AF, and 11,579 (24.11%) had new-onset AF. Median ages were 61.47 years (no AF), 76.12 years (pre-existing AF), and 75.26 years (new-onset AF). New-onset AF was associated with the highest mortality rates: 25.16% at 3 months, 29.23% at 6 months, and 34.04% at 1 year, compared to 17.94%, 22.55%, and 28.52% for pre-existing AF, and 14.54%, 17.25%, and 20.69% for no AF respectively (p < 0.001 for all). Multivariate Cox regression indicated that new-onset AF significantly increased the risk of 1-year mortality by 15.5% compared to no AF (HR: 1.155, 95% CI: 1.101– 1.212; p < 0.001) and by 23.9% compared to pre-existing AF (HR: 1.239, 95% CI: 1.164– 1.318; p < 0.001). Kaplan-Meier analysis confirmed lower survival probabilities for new-onset AF over one year compared to the other groups (p < 0.001).Conclusion: In patients with critical illness, new-onset AF is associated with an increased risk of mortality compared with pre-existing AF or no AF.Keywords: atrial fibrillation, mortality, intensive care unit

Published
2024

2. Comparison of Genetic, Auditory Features, and Systemic Clinical Phenotype in 14 Families with Syndromic Hearing Loss

Author

Zheng Z, Yan L, Ding L, Zhang Y, Wang M, Yang Y, Wu J, Chen C, Tang M, and Li H

Subjects
syndromic hearing loss, whole exome sequencing, neonatal hearing screening, phenotype-genotype correlations, Medicine (General), R5-920, Genetics, QH426-470
Abstract

Zhoushu Zheng,1 Lulu Yan,2,3 Lu Ding,1 Yinghui Zhang,1 Meihong Wang,1 Yihui Yang,1 Junhua Wu,3 Changshui Chen,3 Ming Tang,1 Haibo Li2,3 1Department of Otolaryngology Head and Neck Surgery, The Affiliated Women and Children’s Hospital of Ningbo University, Ningbo, People’s Republic of China; 2The Central Laboratory of Birth Defects Prevention and Control, The Affiliated Women and Children’s Hospital of Ningbo University, Ningbo, People’s Republic of China; 3Ningbo Key Laboratory for the Prevention and Treatment of Embryogenic Diseases, The Affiliated Women and Children’s Hospital of Ningbo University, Ningbo, People’s Republic of ChinaCorrespondence: Haibo Li, The Central Laboratory of Birth Defects Prevention and Control, Women and Children’s Hospital of Ningbo University, No. 339, Liuting Road, Haishu District, Ningbo City, Zhejiang Province, People’s Republic of China, Email lihaibo-775@163.com Ming Tang, Department of Otolaryngology head and neck surgery, Ningbo Women and Children’s Hospital, No. 339, Liuting Road, Haishu District, Ningbo City, Zhejiang Province, People’s Republic of China, Email tmtm-50@163.comIntroduction: Syndromic hearing loss (SHL) is characterized by distinctive clinical phenotypes as well as genetic and phenotypic heterogeneity. More than 400 species of SHL have been described, the majority of which are autosomal dominant.Methods: 11 forms of SHL were obtained from 14 unrelated families with probands ranging in age from 5 to 78 months. The results of whole exome sequencing(WES), audiological characteristics, middle and inner ear radiological findings, and additional clinical phenotype characteristics were retrospectively analyzed.Results: Fourteen people with SHL were found. Two of them had Waardenburg syndrome, two had Branchio-Oto-Renal syndrome, two had CHARGE syndrome, and one had Treacher Collins syndrome, Kleefstra syndrome, Muenke syndrome, Osteopathia Striata with Cranial Sclerosis, Ayme-Gripp syndrome, Tatton-Brown-Rahman syndrome, Stickler syndrome, or Stapes Ankylosis with Broad Thumbs and Toes. In this investigation, ten variants were first reported.Discussion: The combination of a neonatal hearing screening and WES can diagnose syndrome-type hearing loss in infancy and childhood, according to our findings, expansion of the gene variant spectrum and phenotype for various age groups of SHL is essential and can provide valuable guidelines for clinical intervention decisions. It is imperative for medical practitioners to conduct diligent and prolonged patient monitoring due to the inherent variability in both the auditory impairment and the comprehensive clinical manifestation of SHL.Keywords: syndromic hearing loss, whole exome sequencing, neonatal hearing screening, phenotype-genotype correlations

Published
2024

3. Two Cases of Listeria monocytogenes-Induced Infective Endocarditis

Author

Tang M, Lu X, Li Y, Chen Y, and Xie Y

Subjects
infective endocarditis, listeria monocytogenes, heart valve disease, Infectious and parasitic diseases, RC109-216
Abstract

Mengli Tang,1,&ast; Xingbing Lu,1,&ast; Yuxiao Li,2 Yuzuo Chen,1 Yi Xie1 1Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 2West China School of Medicine, Sichuan University, Chengdu, Sichuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yi Xie, Department of Laboratory Medicine, West China Hospital, Sichuan University, Guoxue Lane37, Chengdu, Sichuan, People’s Republic of China, Tel +86-028-85422618, Email xieyi@scu.edu.cnAbstract: Listeria monocytogenes is a prevalent gram-positive intracellular zoonotic pathogen that is frequently associated with foodborne illnesses and opportunistic infections. This bacterium is responsible for causing various clinical manifestations, including bacteremia, meningitis, and encephalitis, and is primarily transmitted through contaminated food consumption. This study presents two cases of severe endocarditis in patients with heart valve disease caused by L. monocytogenes. Infection was confirmed by blood culture and pathogen culture of the valve pus. Early detection, clinical suspicion, and appropriate treatment are crucial for improving the prognosis of patients with listeriosis. The combination of ampicillin and aminoglycosides remains the most effective treatment for listeriosis.Keywords: infective endocarditis, Listeria monocytogenes, heart valve disease

Published
2024

4. Comprehensive Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Transcriptional Signatures of Macrophages in Intrahepatic Cholestasis of Pregnancy

Author

Tang M, Xiong L, Cai J, Gong X, Fan L, Zhou X, Xing S, and Yang X

Subjects
intrahepatic cholestasis of pregnancy, scrna-seq, bulk rna-seq, immune gene, macrophage, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Mi Tang,1,2,&ast; Liling Xiong,3,&ast; Jianghui Cai,2,4,&ast; Xuejia Gong,2 Li Fan,1 Xiaoyu Zhou,1 Shasha Xing,1 Xiao Yang3 1Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 2School of medicine, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China; 3Obstetrics Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China; 4Department of Pharmacy, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shasha Xing; Xiao Yang, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China, Email xingshasha1230@126.com; wcchgcptg@163.comPurpose: Intrahepatic cholestasis of pregnancy (ICP) is a disorder that characterized by maternal pruritus, abnormal liver function, and an elevation in total bile acid concentrations during pregnancy. Immune factors have been recognized as playing a vital role in the mechanism of ICP. However, the underlying mechanisms regulating dysfunctional immune cells and immune genes remain to be fully elucidated.Patients and Methods: Single-cell RNA sequencing and bulk RNA sequencing data of the placenta were downloaded from the SRA database. The AUCell package, Monocle package and SCENIC package were utilized to explored immune cell activity, cell trajectory and transcription factor, respectively. GO, KEGG, and GSEA were employed to explore potential biological mechanisms. Cell-cell communications were further investigated using the CellChat package. RT-PCR, and Western blot were used to verify the gene expression in placenta.Results: In placenta cells, macrophages were found to be significantly increased in ICP. Additionally, macrophages exhibited the highest immune gene score and were divided into four subclusters (MF1-4). Our analysis revealed significant elevations in MF2, associated with LPS response and antigen presentation, and MF4, associated with TNF and cytokine production. MF3 displayed an anti-inflammatory phenotype. MF1, closely related to ribosomes and proteins, exhibited a sharp decrease. Although ICP maintained an anti-inflammatory state, macrophage trajectories showed a gradual progression toward inflammation. Subsequently, we confirmed that cytokine- and chemokine-related signaling pathways were emphasized in macrophages. Within the CXCL signaling pathway, the increased expression of CXCL1 in macrophages can interact with CXCR2 in neutrophils, potentially inducing macrophage infiltration, stimulating neutrophil chemotaxis, and leading to an inflammatory response and cellular damage.Conclusion: In conclusion, we firstly revealed the transcriptional signatures of macrophages in ICP and discovered a tendency toward an inflammatory state. This study also provides new evidence that the CXCL1-CXCR2 axis may play an important role in the pathogenesis of ICP.Keywords: intrahepatic cholestasis of pregnancy, scRNA-Seq, bulk RNA-Seq, immune gene, macrophage

Published
2024

5. A Machine Learning Model Based on Counterfactual Theory for Treatment Decision of Hepatocellular Carcinoma Patients

Author

Wei X, Wang F, Liu Y, Li Z, Xue Z, Tang M, and Chen X

Subjects
radiomics, hepatocellular carcinoma, prognosis, hepatectomy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Xiaoqin Wei,1,&ast; Fang Wang,2,&ast; Ying Liu,3 Zeyong Li,4 Zhong Xue,2 Mingyue Tang,5 Xiaowen Chen1 1School of Medical Imaging, North Sichuan Medical College, Nanchong City, Sichuan Province, People’s Republic of China; 2Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, People’s Republic of China; 3Department of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu City, Sichuan Province, People’s Republic of China; 4Department of Radiology, Bishan Hospital of Chongqing Medical University, ChongQing, People’s Republic of China; 5Department of Physics, School of Basic Medicine, North Sichuan Medical College, Nanchong, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaowen Chen, School of Medical Imaging, North Sichuan Medical College, 234 Fujiang Road, Nanchong City, Sichuan Province, People’s Republic of China, 637001, Email 56833804@qq.comPurpose: To predict the efficacy of patients treated with hepatectomy and transarterial chemoembolization (TACE) based on machine learning models using clinical and radiomics features.Patients and Methods: Patients with HCC whose first treatment was hepatectomy or TACE from June 2016 to July 2021 were collected in the retrospective cohort study. To ensure a causal effect of treatment effect and treatment modality, perfectly matched patients were obtained according to the principle of propensity score matching and used as an independent test cohort. Inverse probability of treatment weighting was used to control bias for unmatched patients, and the weighted results were used as the training cohort. Clinical characteristics were selected by univariate and multivariate analysis of cox proportional hazards regression, and radiomics features were selected using correlation analysis and random survival forest. The machine learning models (Deathhepatectomy and DeathTACE) were constructed to predict the probability of patient death after treatment (hepatectomy and TACE) by combining clinical and radiomics features, and an optimal treatment regimen was recommended. In addition, a prognostic model was constructed to predict the survival time of all patients.Results: A total of 418 patients with HCC who received either hepatectomy (n=267, mean age, 58 years ± 11 [standard deviation]; 228 men) or TACE (n=151, mean age, 59 years ± 13 [standard deviation]; 127 men) were recruited. After constructing the machine learning models Deathhepatectomy and DeathTACE, patients were divided into the hepatectomy-preferred and TACE-preferred groups. In the hepatectomy-preferred group, hepatectomy had a significantly prolonged survival time than TACE (training cohort: P < 0.001; testing cohort: P < 0.001), and vise versa for the TACE-preferred group. In addition, the prognostic model yielded high predictive capability for overall survival.Conclusion: The machine learning models could predict the outcomes difference between hepatectomy and TACE, and prognostic models could predict the overall survival for HCC patients.Keywords: radiomics, hepatocellular carcinoma, prognosis, hepatectomy

Published
2024

6. Nomogram Development and Validation for Predicting Postoperative Recurrent Lumbar Disc Herniation Based on Paraspinal Muscle Parameters

Author

Tang M, Wang S, Wang Y, Zeng F, Chen M, Chang X, He M, Fang Q, and Yin S

Subjects
lumbar disc herniation, percutaneous endoscopic transforaminal discectomy, postoperative recurrent lumbar disc herniation, predictive model, Medicine (General), R5-920
Abstract

Ming Tang,1,2,&ast; Siyuan Wang,1,2,&ast; Yiwen Wang,1 Fanyi Zeng,1 Mianpeng Chen,1 Xindong Chang,1 Mingfei He,1 Qingqing Fang,1 Shiwu Yin1 1Department of Interventional Vascular Medicine, Hefei Hospital Affiliated to Anhui Medical University, The Second People’s Hospital of Hefei, Hefei City, Anhui Province, People’s Republic of China; 2The Fifth Clinical College of Medicine, Anhui Medical University, Hefei City, Anhui Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shiwu Yin, Department of Interventional Vascular Medicine, Hefei Hospital Affiliated to Anhui Medical University, The Second People’s Hospital of Hefei, 574 Changjiang East Road, Yaohai District, Hefei City, Anhui Province, 230011, People’s Republic of China, Email yinshiwu@126.comPurpose: Previous studies highlight paraspinal muscles’ significance in spinal stability. This study aims to assess paraspinal muscle predictiveness for postoperative recurrent lumbar disc herniation (PRLDH) after lumbar disc herniation patients undergo percutaneous endoscopic transforaminal discectomy (PETD).Patients and Methods: Retrospectively collected data from 232 patients undergoing PETD treatment at our institution between January 2020 and January 2023, randomly allocated into training (60%) and validation (40%) groups. Utilizing Lasso regression and multivariable logistic regression, independent risk factors were identified in the training set to construct a Nomogram model. Internal validation employed Enhanced Bootstrap, with Area Under the ROC Curve (AUC) assessing accuracy. Calibration was evaluated through calibration curves and the Hosmer-Lemeshow goodness-of-fit test. Decision curve analysis (DCA) and clinical impact curve (CIC) were employed for clinical utility analysis.Results: Diabetes, Modic changes, and ipsilesional multifidus muscle skeletal muscle index (SMI) were independent predictive factors for PRLDH following PETD (P< 0.05). Developed Nomogram model based on selected predictors, uploaded to a web page. AUC for training: 0.921 (95% CI 0.872– 0.970), validation: 0.900 (95% CI 0.828– 0.972), respectively. The Hosmer-Lemeshow test yielded χ2=5.638/6.259, P=0.688/0.618, and calibration curves exhibited good fit between observed and predicted values. DCA and CIC demonstrate clinical net benefit for both models at risk thresholds of 0.02– 1.00 and 0.02– 0.80.Conclusion: The Nomogram predictive model developed based on paraspinal muscle parameters in this study demonstrates excellent predictive capability and aids in personalized risk assessment for PRLDH following PETD.Keywords: lumbar disc herniation, percutaneous endoscopic transforaminal discectomy, postoperative recurrent lumbar disc herniation, predictive model

Published
2024

7. Compromised Dynamic Cerebral Autoregulation in Patients with Restless Legs Syndrome

Author

Zhang Y, Chen Q, Sun Q, Tang M, Yang Y, Guo ZN, and Wang Z

Subjects
restless legs syndrome, dynamic cerebral autoregulation, autonomic nerve dysfunction, dopaminergic, Psychiatry, RC435-571, Neurophysiology and neuropsychology, QP351-495
Abstract

Yanan Zhang,1 Qianqian Chen,1 Qingqing Sun,1 Mingyang Tang,1 Yi Yang,1,2 Zhen-Ni Guo,1,2 Zan Wang1,2 1Sleep Center, Department of Neurology, The First Hospital of Jilin University, Chang Chun, People’s Republic of China; 2Stroke Center, Department of Neurology, The First Hospital of Jilin University, Chang Chun, People’s Republic of ChinaCorrespondence: Zhen-Ni Guo, Stroke Center, Department of Neurology, The First Hospital of Jilin University, Xinmin Street 1&num;, Chang Chun, 130021, People’s Republic of China, Tel +86-431-88782378, Email zhen1ni2@jlu.edu.cn Zan Wang, Sleep Center, Department of Neurology, The First Hospital of Jilin University, Xinmin Street 1&num;, Chang Chun, 130021, People’s Republic of China, Tel +86-431-88782378, Email wangzan@jlu.edu.cnBackground: Restless legs syndrome (RLS) is a prevalent sensorimotor nervous system disorder in patients accompanied with insomnia, blood pressure fluctuation, and sympathetic dysfunction. These symptoms may disrupt cerebral hemodynamics. Dynamic cerebral autoregulation (dCA) describes the temporary response of cerebrovascular system to abrupt fluctuations in blood pressure, which keep cerebral blood flow stable and serve as a marker of cerebrovascular system ability.Objective: This research aimed to assess dCA in RLS patients.Methods: In this study, RLS patients were recruited and subsequently classified into four groups (mild, moderate, severe, and very severe) based on the International RLS Rating Scale (IRLS). Healthy controls matched for age and sex were enrolled. All participants were evaluated dCA by assessing phase difference (PD). A portion of patients with RLS was reassessed for dCA after one month of medication therapy (pramipexole [0.125 mg/day] and gabapentin [300 mg/day]).Results: There were altogether 120 patients with RLS and 30 controls completed the polysomnography and dCA assessment. PD was lower in the moderate, severe, and very severe RLS groups than that in the controls and mild RLS groups. Periodic limb movement index (PLMI), arousal index, and IRLS all showed a linear correlation with PD in RLS patients. Additionally, PD increased in RLS patients after therapy.Conclusion: The dCA was compromised in moderate, severe, and very severe RLS patients and was negatively correlated with the IRLS, arousal index, and PLMI. After 1 month of therapy, dCA improved in RLS patients.Keywords: restless legs syndrome, dynamic cerebral autoregulation, autonomic nerve dysfunction, dopaminergic

Published
2024

8. Development and Validation of a Nomogram Predicting Postoperative Recurrent Lumbar Disc Herniation Based on Activity Factors

Author

Tang M, Wang S, Wang Y, Chen M, Chang X, He M, Fang Q, and Yin S

Subjects
lumbar disc herniation, percutaneous endoscopic transforaminal discectomy, postoperative recurrent lumbar disc herniation, predictive model, Public aspects of medicine, RA1-1270
Abstract

Ming Tang,1,2,&ast; Siyuan Wang,1,&ast; Yiwen Wang,1 Mianpeng Chen,1 Xindong Chang,1 Mingfei He,1 Qingqing Fang,1 Shiwu Yin1 1Department of Interventional Vascular Medicine, Hefei Hospital Affiliated to Anhui Medical University, The Second People’s Hospital of Hefei, Hefei City, Anhui Province, People’s Republic of China; 2The Fifth Clinical College of Medicine, Anhui Medical University, Hefei City, Anhui Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Shiwu Yin, Department of Interventional Vascular Medicine, Hefei Hospital Affiliated to Anhui Medical University, The Second People’s Hospital of Hefei, 574 Changjiang East Road, Yaohai District, Hefei City, Anhui Province, 230011, People’s Republic of China, Email yinshiwu@126.comPurpose: To develop an individualized predictive model for postoperative recurrent lumbar disc herniation (PRLDH) in patients undergoing percutaneous endoscopic transforaminal discectomy (PETD) by considering postoperative activity factors.Patients and Methods: Retrospectively collected data from 612 LDH patients who underwent PETD in our institution from January 2017 to June 2023. They were divided into a training group (429 cases) and a validation group (183 cases). Lasso regression (Model 1) and random forest (Model 2) were applied for variable selection in the training group. The two models were compared in terms of discrimination (the area under curve, AUC), calibration (calibration curve), and clinical utility (decision curve analysis, DCA). Akaike information criterion (AIC) was used for model comparison, and internal validation employed 1000 times Bootstrap + 10-fold cross-validation. Finally, a Nomogram was constructed to display the results and uploaded to the web version.Results: Among 612 treated LDH patients, 66 (10.78%) developed PRLDH. Model 1, superior in AUC, calibration, DCA, and AIC over Model 2, was chosen as the predictive model. Logistic regression in the training group identified BMI, smoking, activity level score, time to first ambulation, diabetes, Modic change, and Pfirrmann grade as independent predictors of PRLDH. Model 1 exhibited a training group AUC of 0.813 (95% CI 0.753– 0.872) and a validation group AUC of 0.868 (95% CI 0.773– 0.962). At a Youden index of 0.50, sensitivity was 0.73, specificity was 0.77. Internal validation (1000 times Bootstrap + 10-fold cross-validation) for the training group showed accuracy of 0.889, kappa consistency of 0.112, and AUC of 0.757. The Hosmer-Lemeshow goodness-of-fit tests indicated good discriminative ability for Model 1 in both the training (χ2=2.895, P=0.941) and validation groups (χ2=8.197, P=0.414). The DCA and Nomogram are accessible at https://sofarnomogram.shinyapps.io/PRLDHNom/.Conclusion: The Nomogram predictive model, developed based on postoperative activity factors in this study, demonstrates excellent predictive capability, facilitating risk assessment for the occurrence of PRLDH after PETD.Keywords: Lumbar disc herniation, percutaneous endoscopic transforaminal discectomy, postoperative recurrent lumbar disc herniation, predictive model

Published
2024

9. Effects of Anticoagulant Therapy and Frailty in the Elderly Patients with Atrial Fibrillation

Author

Ding J, Sun Y, Zhang K, Huang W, Tang M, Zhang D, and Xing Y

Subjects
elderly, non-valvular atrial fibrillation, frailty, anticoagulation, Geriatrics, RC952-954.6
Abstract

Jiancao Ding,1 Ying Sun,2 Kan Zhang,2 Wei Huang,2 Mei Tang,2 Dai Zhang,2 Yunli Xing2 1Department of Geriatrics, First People’s Hospital of Zigong, Sichuan, People’s Republic of China; 2Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Yunli Xing, Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University, No. 95, Yongan Road, Xicheng District, Beijing, 100050, People’s Republic of China, Tel +86 13121181469, Fax +86 10 6313 8040, Email xingyunli1976@126.comObjective: This study explored whether anticoagulation is safe for frail and non-frail elderly patients who have nonvalvular atrial fibrillation (NVAF).Methods: At hospital discharge, the anticoagulant regimen and frailty status were recorded for 361 elderly patients (aged ≥ 75 y) with NVAF. The patients were followed for 12 months. The endpoints included occurrence of thrombosis; bleeding; all-cause death; and cardiovascular events.Results: At hospital discharge, frailty affected 50.42% of the population and the anticoagulation rate was 44.04%. At discharge, age (OR 0.948, P = 0.006), paroxysmal NVAF (OR 0.384, P < 0.001), and bleeding history (OR 0.396, P = 0.001) were associated with a decrease in rate of receiving anticoagulation, while thrombotic events during hospitalization (OR 2.281, P = 0.021) were associated with an increase. Relative to non-frail patients, those with frailty showed a higher rate of ischemic stroke (5.33% cf. 3.01%), bleeding (P = 0.006) events, and all-cause mortality (P = 0.001). Relative to the group without anticoagulation, in those with anticoagulation the rate of thrombotic events was lower (6.99 cf. 10.98%) and bleeding events were higher (20.98 cf. 12.72%), but the risk of major bleeding was comparable.Conclusion: In the elderly patients with NVAF, the decision toward anticoagulation therapy at hospital discharge was influenced by age, bleeding history, paroxysmal atrial fibrillation diagnosis, and absence of thrombosis. Frail patients were at greater risk of bleeding and all-cause mortality. Anticoagulation tended to reduce the risk of thrombotic events.Keywords: elderly, non-valvular atrial fibrillation, frailty, anticoagulation

Published
2024

10. Association Between Dietary Behaviors and Weight Gain During City-Wide Quarantine

Author

Tang M, Chen Z, Wang J, Song A, Wu IT, and Xu R

Subjects
body weight, physical activity, food consumption, quarantine, Medicine (General), R5-920
Abstract

Molian Tang,1,&ast; Zhiqi Chen,1,&ast; Jialu Wang,1 Anqi Song,1 I-Ting Wu,2 Renying Xu1,3 1Department of Clinical Nutrition, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Data & Research Cornerstone Whole Healthcare Organization (C-Who), Inc., Boise, Idaho, USA; 3Department of Nutrition, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Renying Xu, Department of Clinical Nutrition, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, People’s Republic of China, Tel +86-021-68383335, Email 721001735@shsmu.edu.cnBackground: The impact of quarantine-induced changes in dietary behavior on weight gain remained unclear. This study aimed to evaluate the association between changes in dietary behavior and body weight during quarantine and to identify the risk factors of weight gain.Methods: This was a pilot observational cross-sectional study. All the potential participants were those who underwent body weight management program in one teaching hospital in China from 26th April 2021 to 31st March 2022. An online self-reported questionnaire was sent to collect information on sex, age, self-reported body weight before and after quarantine, dietary quality, meal time, food consumption, physical activities, and sleep quality. Weight gain was defined as an increase of 1 kilogram or more. The study has been performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee (KY2020-204). The participants were informed about the objectives of the study and electronic informed consent was obtained from each participant.Results: Finally, 79 participants (22.8% male and 77.2% female, aged 33.3 ± 7.1 years) was included in the analysis. During quarantine, the mean body weight gain was 0.8 (interquartile range: − 1.0~3.0) kg. The proportion of weight gain among the participants was 45.6%. Increased cooked white rice (OR=16.93; 95% CI: 2.66– 108.00), convenient food (OR=11.69; 95% CI: 2.00– 68.26), and snack consumption (OR=5.56; 95% CI: 1.08– 28.56), delayed dinner time (OR=6.64; 95% CI: 1.20– 36.74) and house working time less than 30 minutes (OR=12.80; 95% CI: 2.01– 81.44) were risk factors for body weight gain.Conclusion: During the quarantine, weight gain was observed even in participants who were previously on body weight management. Increased consumption of cooked white rice, convenient food, and snack, as well as delayed dinner time and reduced house working time (less than 30 minutes), were found to be associated with body weight gain.Keywords: body weight, physical activity, food consumption, quarantine

Published
2023

11. Optimal Perioperative Antimicrobial Management Strategies of Kidney Transplant Recipients Guided by Metagenomic Next-Generation Sequencing of Deceased Donors’ Microbiology Samples

Author

Rao Z, Wang Z, Tang M, and Zhang K

Subjects
kidney transplantation, metagenomic next-generation sequencing, donor-derived infection, perioperative infection, antimicrobial strategy, Infectious and parasitic diseases, RC109-216
Abstract

Zhengsheng Rao, Zhiling Wang, Ming Tang, Keqin Zhang Urinary Nephropathy Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Keqin Zhang, Email mnsbzx2019@163.comBackground: There is no consensus on the optimal use of perioperative antibiotics prophylaxis after kidney transplantation, but there is a common trend to limit the duration of antibiotic use worldwide. Metagenomic next-generation sequencing (mNGS) has emerged as a novel technology for pathogen detection in clinical practice due to its noninvasive, rapid, precise and high susceptibility to detect infectious pathogens. However, data are lacking on whether mNGS analyses could be used to detect pathogens and guide anti-infection regimens in kidney transplant donors and recipients.Methods: We conducted a retrospective study to review all clinic data of mNGS and traditional laboratory methods (TMs) for pathogen detection in kidney transplant recipients and their corresponding deceased donors from August 1, 2021 to October 30, 2022 in our center.Results: A total of 57 donors and 112 of their corresponding recipients were included. The antimicrobial strategy mainly depended on mNGS results combined with traditional pathogen culture and clinical conditions. The percentages of positive pathogen detected by mNGS in blood, urine, bronchoalveolar lavage fluid (BALF) and preservation fluids (PFs) were 50.9% (29/57), 35.1% (20/57), 84.2% (48/57) and 54.4% (31/57) respectively, and were 24.6% (14/57), 15.8% (9/57), 57.9% (33/57) and 14.1% (8/57) respectively when using TMs. mNGS could detected all of pathogens which were detected by TMs. However, samples with negative TMs testing can be additionally detected as positive by mNGS (15/43 in blood, 11/48 in urine, 15/24 in BALF and 23/49 in PFs). Drug resistance genes were detected in 9 donors by mNGS,which were consistent with 6 donors by TMs. There was only one case of donor-derived infection in this study.Conclusion: This study showed that it is effective to combine mNGS with traditional pathogen detection methods and clinical features to develop optimal perioperative antimicrobial management strategies for deceased donor kidney transplantation.Keywords: kidney transplantation, metagenomic next-generation sequencing, donor-derived infection, perioperative infection, antimicrobial strategy

Published
2023

12. Variables Associated with 30-Day Mortality in Very Elderly COVID-19 Patients

Author

Xing Y, Sun Y, Tang M, Huang W, Luo J, and Ma Q

Subjects
covid-19, over-80-year-old, characteristic, mortality, Geriatrics, RC952-954.6
Abstract

Yunli Xing, Ying Sun, Mei Tang, Wei Huang, Jia Luo, Qing Ma Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Qing Ma, Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University, No. 95, Yongan Road, Xicheng District, Beijing, 100050, People’s Republic of China, Tel +86 13801019331, Fax +86 10 6313 8040, Email maqing3@163.comBackground: Advanced age increases the risk for severe COVID-19. However, the risk factors for mortality from COVID-19 in very elderly patients (≥ 80-years-old) are unknown.Objective: Investigate the relationship of mortality with the clinical characteristics of very elderly COVID-19 patients.Materials and Methods: Very elderly patients who were hospitalized with COVID-19 from December 3, 2022 to January 1, 2023 were retrospectively examined. Sociodemographic and clinical variables were recorded and survival was recorded after 30 days.Results: We examined 181 patients (median age: 90.84 years; 114 older than 90 years). The median Barthel index was 30.69, and 55.8% of patients had severe or critical COVID-19 pneumonia. Forty-two patients (33.2%) received a high-flow nasal cannula or non-invasive ventilation, and only 4.4% received mechanical ventilation. The overall mortality was 35.9%, and there was no significant difference in mortality for the 80 to 90-year-old group and the over 90-year-old group (37.7% vs 32.8%, P=0.508). A multivariate analysis showed that the Barthel index (OR, 0.975; 95% CI, 0.962– 0.989), serum creatinine (SCr) level (OR, 1.003; 95% CI, 1.000– 1.006), white blood cell (WBC) count (OR, 1.160; 95% CI, 1.056– 1.276), D-dimer level (OR, 1.060; 95% CI, 1.009– 1.113), and corticosteroid use (OR, 0.268; 95% CI, 0.124– 0.582) were significantly and independently related to 30-day mortality. A binary classification model based on the multivariate analysis had good predictive value (area under the curve, 0.794).Conclusion: Very elderly COVID-19 patients have a high risk for mortality. The Barthel index, SCr, WBC count, D-dimer level, and corticosteroid use were independently associated with mortality.Keywords: COVID-19, over-80-year-old, characteristic, mortality

Published
2023

13. Effect of Different Liver Resection Modalities on the Prognosis of Patients with Hepatocellular Carcinoma on the Left Lateral Lobe

Author

Liu J, Sun M, Fang K, Wang J, Ma B, Song L, Liu T, Tang M, Wang K, and Xia Y

Subjects
hepatocellular carcinoma, left lateral hepatic lobe, left lateral lobectomy, left hepatectomy, prognosis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Jianwei Liu,1,&ast; Minmin Sun,2,&ast; Kunpeng Fang,3,&ast; Jie Wang,1 Bowen Ma,1 Li Song,1 Ting Liu,1 Ming Tang,4 Kui Wang,1 Yong Xia4 1Department of Hepatic Surgery II, Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), Shanghai, 200438, People’s Republic of China; 2Department of Hepatic Surgery I, Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), Shanghai, 200438, People’s Republic of China; 3Department of Special Treatment I, Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), Shanghai, 200438, People’s Republic of China; 4Department of Hepatic Surgery IV, Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), Shanghai, 200438, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yong Xia, Department of Hepatic Surgery (IV), Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), &num;225 Changhai Road, Shanghai, 200438, People’s Republic of China, Tel +86-2181875005, Fax +86-2165562400, Email xia.yong@126.com Kui Wang, Department of Hepatic Surgery (II), Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), &num;225 Changhai Road, Shanghai, 200438, People’s Republic of China, Email wangkuiykl@163.comPurpose: To investigate the effect of different liver resection modalities on the prognosis of left lateral lobe hepatocellular carcinoma (HCC) patients.Methods: 315 patients with HCC on left lateral lobe were divided into open left lateral lobectomy (LLL) group (n=249) and open left hepatectomy (LH) group (n=66). The differences in long-term prognosis between two groups were compared.Results: The results showed that narrow resection margin (Hazard Ratio (HR):1.457, 95% Confidential Interval (CI): 1.038– 2.047; HR:1.415, 95% CI: 1.061– 1.887), tumor diameter > 5 cm (1.645, 1.161– 2.330; 1.488, 1.123– 1.971), multiple tumors (2.021, 1.330– 3.073; 1.987, 1.380– 2.861), and microvascular invasion (MVI) (1.753, 1.253– 2.452; 1.438, 1.087– 1.902) are independent risk factors for overall survival (OS) and tumor recurrence (TR), while liver resection modality is not. After propensity score matching, liver resection modality is not an independent risk factor for OS and TR. Further analysis revealed that wide resection margins were achieved in all patients in the LH group but only 59.0% patients in the LLL group. The OS and TR rates were not significantly different between wide patients with resection margins in LLL group and LH group (P=0.766 and 0.919, respectively), but significantly different between patients with narrow resection margins in LLL group and LH group (P=0.012 and 0.017, respectively).Conclusion: Liver resection modality is not an independent risk factor for the prognosis of patients with HCC on the left lateral lobe as long as wide margins are obtained. Nevertheless, with narrow margins, patients who underwent LH rather than LLL did better.Keywords: hepatocellular carcinoma, left lateral hepatic lobe, left lateral lobectomy, left hepatectomy, prognosis

Published
2023

14. Biosynthesis of Silver Nanoparticles Using the Biofilm Supernatant of Pseudomonas aeruginosa PA75 and Evaluation of Their Antibacterial, Antibiofilm, and Antitumor Activities

Author

Xia F, Tao X, Wang H, Shui J, Min C, Xia Y, Li J, Tang M, Liu Z, Hu Y, Luo H, and Zou M

Subjects
multidrug resistant, green synthesis, colistin, metallic silver, melanoma, Medicine (General), R5-920
Abstract

Fengjun Xia,1,2,&ast; Xiaoyan Tao,2,3,&ast; Haichen Wang,1,2 Jian Shui,4 Changhang Min,1,2 Yubing Xia,1,2 Jun Li,1,2 Mengli Tang,1,2 ZhaoJun Liu,1,2 Yongmei Hu,1,2 Huidan Luo,1,2 Mingxiang Zou1,2 1National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China; 2Department of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China; 3Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China; 4Department of Clinical Laboratory, Changsha Central Hospital, Changsha, 410008, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Mingxiang Zou, National Clinical Research Center for Geriatric Disorders, Department of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha, Hunan Province, 410008, People’s Republic of China, Tel/Fax +86-7384327440, Email zoumingxiang@csu.edu.cnPurpose: As an under-explored biomaterial, bacterial biofilms have a wide range of applications in the green synthesis of nanomaterials. The biofilm supernatant of Pseudomonas aeruginosa PA75 was used to synthesize novel silver nanoparticles (AgNPs). BF75-AgNPs were found to possess several biological properties.Methods: In this study, we biosynthesized BF75-AgNPs using biofilm supernatant as the reducing agent, stabilizer, and dispersant and investigated their biopotential in terms of antibacterial, antibiofilm, and antitumor activities.Results: The synthesized BF75-AgNPs demonstrated a typical face-centered cubic crystal structure; they were well dispersed; and they were spherical with a size of 13.899 ± 4.036 nm. The average zeta potential of the BF75-AgNPs was − 31.0 ± 8.1 mV. The BF75-AgNPs exhibited strong antibacterial activities against the methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum beta-lactamase Escherichia coli (ESBL-EC), extensively drug-resistant Klebsiella pneumoniae (XDR-KP), and carbapenem-resistant Pseudomonas aeruginosa (CR-PA). Moreover, the BF75-AgNPs had a strong bactericidal effect on XDR-KP at 1/2 × MIC, and the expression level of reactive oxygen species (ROS) in bacteria was significantly increased. A synergistic effect was observed when the BF75-AgNPs and colistin were used for the co-treatment of two colistin-resistant XDR-KP strains, with fractional inhibitory concentration index (FICI) values of 0.281 and 0.187, respectively. Furthermore, the BF75-AgNPs demonstrated a strong biofilm inhibition activity and mature biofilm bactericidal activity against XDR-KP. The BF75-AgNPs also exhibited a strong antitumor activity against melanoma cells and low cytotoxicity against normal epidermal cells. In addition, the BF75-AgNPs increased the proportion of apoptotic cells in two melanoma cell lines, and the proportion of late apoptotic cells increased with BF75-AgNP concentration.Conclusion: This study suggests that BF75-AgNPs synthesized from biofilm supernatant have broad prospects for antibacterial, antibiofilm, and antitumor applications.Graphical Abstract: Keywords: multidrug resistant, green synthesis, colistin, metallic silver, melanoma

Published
2023

15. Resveratrol Improves Paclitaxel-Induced Cognitive Impairment in Mice by Activating SIRT1/PGC-1α Pathway to Regulate Neuronal State and Microglia Cell Polarization

Author

Liu X, Tang M, He TY, Zhao S, Li HZ, Li Z, Guo YX, and Wang XL

Subjects
resveratrol, paclitaxel, sirt1/pgc-1α, microglia, m2 polarization, Therapeutics. Pharmacology, RM1-950
Abstract

Xin Liu,1,&ast; Miao Tang,2,&ast; Tian-Yi He,1 Shuang Zhao,1 Hui-Zhou Li,1 Zhao Li,1 Yue-Xian Guo,3 Xiu-Li Wang1 1Department of Anesthesiology, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050051, People’s Republic of China; 2Department of General Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, People’s Republic of China; 3Department of Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiu-Li Wang, Department of Anesthesiology, The Third Hospital of Hebei Medical University, No. 139 Ziqiang Road, Shijiazhuang, Hebei, 050051, People’s Republic of China, Tel +86-13313019968, Email wangxl311@126.comObjective: This study aimed to investigate the effect of resveratrol (Res) on paclitaxel (PTX)-induced cognitive impairment and elucidate the underlying molecular mechanisms.Methods: Morris Water Maze (MWM) test was used to evaluate the mice’s spatial learning and memory abilities. Western blotting was applied to detect protein expression of receptor-interacting protein (RIP3), mixed lineage kinase domain-like protein (MLKL), silencing information regulator 2 related enzyme 1 (SIRT1), peroxisome proliferator activated receptor coactivator-1 (PGC-1α), NADPH oxidase 2 (NOX2), NOX4, postsynaptic density zone 95 (PSD95), arginase-1 (Arg-1) and inducible nitric oxide synthase (iNOS). Immunofluorescence of RIP3, MLKL, Arg-1, Iba-1 and iNOS was conducted to observe the apoptosis of hippocampal cells and the polarization of microglia. qRT-PCR was performed to detect BDNF mRNA expressions. DHE staining was used to assess the level of oxidative stress response. Golgi-Cox staining and dendritic spine counting were applied to visualize synaptic structural plasticity. Postsynaptic density was performed by transmission electron microscope. ELISA was used to detect the contents of tumour necrosis factor alpha (TNF-α), IL-1β, IL-4, and IL-10.Results: PTX-induced cognitive impairment model was constructed after the application of PTX, represented as longer latency to platform and less platform crossing times over the whole period in PTX group. After Res treatment, the above indicators were reversed, indicating that cognitive function was improved. Moreover, Res reduced neuronal apoptosis and oxidative stress through SIRT1/PGC-1α pathway in mice, manifesting as down-regulated expression of RIP3, MLKL, NOX2 and NOX4. Meanwhile, Res increased the density of dendritic spines and the expression of PSD95 and BDNF, thereby ameliorating the PTX induced synaptic damage. Besides, M2 microglia was in the majority, eliciting the expression of anti-inflammatory cytokines IL-4 and IL-10 after Res treatment in PTX+Res group, while immunofluorescence images results demonstrated an decrease in the proportion of M2 microglia a following SIRT1 inhibitor EX-527.Conclusion: Res improves PTX-induced cognitive impairment in mice by activating SIRT1/PGC-1α pathways to regulate neuronal state and microglia cell polarization.Keywords: resveratrol, paclitaxel, SIRT1/PGC-1α, microglia, M2 polarization

Published
2023

16. Effect of Different Liver Resection Modalities on the Prognosis of Patients with Hepatocellular Carcinoma on the Left Lateral Lobe [Response to Letter]

Author

Liu J, Sun M, Fang K, Wang J, Ma B, Song L, Liu T, Tang M, Wang K, and Xia Y

Subjects
hepatocellular carcinoma left lateral lobe liver resection, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Jianwei Liu,1,&ast; Minmin Sun,2,&ast; Kunpeng Fang,3,&ast; Jie Wang,1,&ast; Bowen Ma,1 Li Song,1 Ting Liu,1 Ming Tang,4 Kui Wang,1 Yong Xia4 1Department of Hepatic Surgery II, Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), Shanghai, 200438, People’s Republic of China; 2Department of Hepatic Surgery I, Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), Shanghai, 200438, People’s Republic of China; 3Department of Special Treatment I, Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), Shanghai, 200438, People’s Republic of China; 4Department of Hepatic Surgery IV, Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), Shanghai, 200438, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yong Xia, Department of Hepatic Surgery (IV), Third Affiliated Hospital of Naval Medical University (Eastern Hepatobiliary Surgery Hospital), &num;225 Changhai Road, Shanghai, 200438, People’s Republic of China, Tel +86-2181875005, Fax +86-2165562400, Email xia.yong@126.com Kui Wang, Department of Hepatic Surgery (II), Eastern Hepatobiliary Surgery Hospital, Navy Medical University, &num;225 Changhai Road, Shanghai, 200438, People’s Republic of China, Tel\Fax +86-2181875514, Email wangkuiykl@163.com

Published
2024

17. Exploring Prognosis, Tumor Microenvironment and Tumor Immune Infiltration in Hepatocellular Carcinoma Based on ATF/CREB Transcription Factor Family Gene-Related Model

Author

Shen H, Gu X, Li H, Tang M, Li X, Zhang Y, Su F, and Wang Z

Subjects
hepatocellular carcinoma, atf / creb transcription factors, immune checkpoints, prognostic model, nomogram, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Honghong Shen,1,&ast; Xianhua Gu,2,&ast; Huiyuan Li,1 Mingyue Tang,1 Xinwei Li,1 Yue Zhang,1 Fang Su,1 Zishu Wang1 1Department of Medical Oncology, First Affiliated Hospital of Bengbu Medical College, Bengbu, People’s Republic of China; 2Department of Gynecology Oncology, First Affiliated Hospital of Bengbu Medical College, Bengbu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Fang Su; Zishu Wang, Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Anhui Province Key Laboratory of Translational Cancer Research, Bengbu Medical College, 287 Changhuai Road, Bengbu, Anhui, 233004, People’s Republic of China, Tel +8613605523272 ; +86 18909620171, Email sufang2899@163.com; wzshahbb@163.comIntroduction: Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer. It is the fourth leading cause of cancer-related death worldwide. Deregulation of the ATF/CREB family is associated with the progression of metabolic homeostasis and cancer. Because the liver plays a central role in metabolic homeostasis, it is critical to assess the predictive value of the ATF/CREB family in the diagnosis and prognosis of HCC.Methods: Using data from The Cancer Genome Atlas (TCGA), this research evaluated the expression, copy number variations, and frequency of somatic mutations of 21 genes in the ATF/CREB family in HCC. A prognostic model based on the ATF/CREB gene family was developed via Lasso and Cox regression analyses, with the TCGA cohort serving as the training dataset and the International Cancer Genome Consortium (ICGC) cohort serving as the validation set. Kaplan-Meier and receiver operating characteristic analyses verified the accuracy of the prognostic model. Furthermore, the association among the prognostic model, immune checkpoints, and immune cells was examined.Results: High-risk patients exhibited an unfavorable outcome as opposed to those in the low-risk category. Multivariate Cox analysis revealed that the risk score calculated based on the prognostic model was an independent prognostic factor for HCC. Analysis of immune mechanisms revealed that the risk score had a positive link to the expression of immune checkpoints, particularly CD274, PDCD1, LAG3, and CTLA4. Differences in immune cells and immune-associated roles were found between the high- and low-risk patients, as determined by single-sample gene set enrichment analysis. The core genes ATF1, CREB1, and CREB3 in the prognostic model were shown to be upregulated in HCC tissues as opposed to adjoining normal tissues, and the 10-year overall survival (OS) rate was worse among patients with elevated expression levels of ATF1, CREB1, and CREB3. Elevated expression levels of ATF1, CREB1, and CREB3 in HCC tissues were confirmed by qRT-PCR and immunohistochemistry studies.Conclusion: According to the results of our training set and test set, the risk model based on the six ATF/CREB gene signatures predicting prognosis has certain predictive accuracy in predicting the survival of HCC patients. This study provides novel insights into the individualized treatment of patients with HCC.Keywords: hepatocellular carcinoma, ATF/CREB transcription factors, immune checkpoints, prognostic model, nomogram

Published
2023

18. PLXDC1 Can Be a Biomarker for Poor Prognosis and Immune Evasion in Gastric Cancer

Author

Li X, Fan Y, Tang M, Li H, Zhang Y, Mi J, Wang Y, Zhao M, Wang Z, and Su F

Subjects
plxdc1, biomarker, tumor microenvironment, immune evasion, prognosis, immunotherapy, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Xinwei Li,1,&ast; Yongfei Fan,2,&ast; Mingyue Tang,1 Huiyuan Li,1 Yue Zhang,1 Jiaqi Mi,1 Yanyan Wang,1 Menglin Zhao,1 Zishu Wang,1 Fang Su1 1Department of Medical Oncology, First Affiliated Hospital of Bengbu Medical College, Bengbu, 233004, People’s Republic of China; 2Department of Thoracic Surgery, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, 213003, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Fang Su; Zishu Wang, Department of Medical Oncology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, 233004, People’s Republic of China, Tel +86 13605523272; +86 13955254185, Email sufang@bbmc.edu.cn; wzshahbb@163.comBackground: Research has revealed that Plexin domain containing 1 (PLXDC1) is correlated with the prognosis of a variety of tumors, but its role in the tumor microenvironment (TME) of gastric cancer has not been reported.Methods: In this study, we analyzed PLXDC1 expression in gastric cancer using the Oncomine and the Cancer Genome Atlas (TCGA) databases and immunohistochemical staining experiments, and performed prognostic assessment with data from the TCGA and Kaplan–Meier Plotter databases. The immunomodulatory role of PLXDC1 in the gastric cancer TME was analyzed by signaling pathway enrichment, immune cell correlation analysis, immunomodulator risk model construction and immunohistochemical staining experiments of immune cells.Results: The results indicated that PLXDC1 was overexpressed in gastric cancer and that its overexpression was associated with poor prognosis. Multivariate Cox analysis revealed that PLXDC1 could be an independent biomarker of the risk of gastric cancer. Signaling pathway enrichment revealed that high PLXDC1 expression was involved in signaling pathways related to immune activation and stromal activation, and Tumor Immune Dysfunction and Exclusion (TIDE) assessment indicated that high PLXDC1 expression was associated with a significantly higher risk of immune evasion than low PLXDC1 expression. A Cox risk model based on PLXDC1-associated immunomodulators also presented poor prognosis, and immune evasion was significantly higher in the high-risk group than in the low-risk group. In addition, immunohistochemical staining of CD8/CD3/CD4+ T cells in the high and low PLXDC1 expression groups also observed immune cell distribution characteristics of immune evasion.Conclusion: This study analyzed PLXDC1 from multiple biological perspectives and revealed that PLXDC1 can be a biomarker for poor prognosis and immune evasion in gastric cancer.Graphical Abstract: Keywords: PLXDC1, biomarker, tumor microenvironment, immune evasion, prognosis, immunotherapy

Published
2022

19. Psychological Disturbances and Their Association with Sleep Disturbance in Patients Admitted for Arrhythmia Diseases

Author

Hu LX, Tang M, Hua W, Ren XQ, Jia YH, Chu JM, Zhang JT, and Liu XN

Subjects
anxiety, depression, arrhythmia, sleep disturbance, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Li-xing Hu, Min Tang, Wei Hua, Xiao-qing Ren, Yu-he Jia, Jian-min Chu, Jing-tao Zhang, Xiao-ning Liu Center of Arrhythmia, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 100037, People’s Republic of ChinaCorrespondence: Xiao-ning Liu, Center of Arrhythmia, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, People’s Republic of China, Tel/Fax +86 10 8839 6965, Email ning_christina@126.comObjectives: This study aimed to assess the depression and anxiety status and their association with sleep disturbance among one single center Chinese inpatients with arrhythmia and help cardiologists better identify patients who need psychological care.Methods: A cross-sectional survey was conducted among 495 inpatients with arrhythmia treated in Fuwai Hospital from October to December 2019. The psychological status and sleep quality were assessed using the Zung Self-Rating Anxiety Scale (SAS), the Zung Self-Rating Depression Scale (SDS) and the Pittsburgh Sleep Quality Index (PSQI). Multivariate logistic regression was used to identify the potential risk factors for anxiety and depression.Results: The mean age of the participants was 52.8 ± 14.4 years, and 58.0% were male. Approximately 18.3% were in an anxious state, and 33.5% were in a depressive state. In multivariate logistic regression, age from 50 to 59 (p = 0.03), unemployment (p = 0.026) and sleep disturbance (p < 0.001) were the risk factors for anxiety status. Cardiac implanted electronic devices (CIEDs) (p = 0.004) and sleep disturbance (p < 0.001) were the risk factors for depression status. A total of 150 patients (30.3%) were categorized as having poor sleep quality (PSQI > 7). The adjusted odds ratio (OR) of having poor sleep quality was 4.30-fold higher in patients with both anxiety and depression (OR: 4.30; 95% confidence interval [CI]: 2.52– 7.35); 2.67-fold higher in patients with depression (OR: 2.67; 95% CI: 1.78– 4.00); and 3.94-fold higher in patients with anxiety (OR: 3.94; 95% CI: 2.41– 6.44).Conclusions: Psychological intervention is critical for Chinese inpatients with arrhythmia, especially for patients aged 50– 59, unemployed, or those using CIEDs. Poor sleep quality could be an important risk factor linked to psychological disturbances.Keywords: anxiety, depression, arrhythmia, sleep disturbance

Published
2022

20. Genetic Characterization of Enterobacter hormaechei Co-Harboring blaNDM-1 and mcr-9 Causing Upper Respiratory Tract Infection

Author

Liu H, Wang D, Tang M, Jia P, Huo Y, Wei E, Xu H, Chi X, and Wang H

Subjects
e. hormaechei, new delhi metallo-β-lactamase, mcr-9, whole-genome sequencing, phylogenetic analysis., Infectious and parasitic diseases, RC109-216
Abstract

Huiqiong Liu,1 Dao Wang,1 Miaomiao Tang,1 Peisheng Jia,1 Yufeng Huo,1 Erhu Wei,1 Hao Xu,2 Xiaohui Chi,2 Huaili Wang1 1Department of Pediatric Intensive Care Unit, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People’s Republic of China; 2Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of ChinaCorrespondence: Huaili Wang, Department of Pediatric Intensive Care Unit, The First Affiliated Hospital of Zhengzhou University, No. 1 Longhu East Zhonghuan Road, Zhengzhou, 450052, People’s Republic of China, Tel +86-371-66271057, Email whlek6527@126.comPurpose: With the spread of multiple drug-resistant bacteria, blaNDM-1 and mcr-9 have been detected in various bacteria worldwide. However, the simultaneous detection of blaNDM-1 and mcr-9 in Enterobacter hormaechei has been rarely reported. This study identified an E. hormaechei strain carrying both blaNDM-1 and mcr-9. We investigated the genetic characteristics of these two resistance genes in detail, elucidating various potential mechanisms by which they may be transmitted.Methods: Bacterial genomic features and possible origins were assessed by whole-genome sequencing (WGS) with Illumina and PacBio platforms and phylogenetic analysis. Subsequent investigations were performed, including antimicrobial susceptibility testing and multilocus sequence typing (MLST).Results: We isolated an E. hormaechei strain DY1901 carrying both blaNDM-1 and mcr-9 from the sputum sample. Susceptibility testing showed that the isolate was multidrug-resistant. Multiple antibiotic resistance genes and virulence genes are widely distributed in DY1901. S1-PFGE, Southern blotting, and plasmid replicon typing showed that DY1901 carried four plasmids. The plasmid carrying mcr-9 was 259Kb in size and belonged to IncHI2, while the plasmid carrying blaNDM-1 was 45Kb in length and belonged to IncX3.Conclusion: The E. hormaechei strain isolated in this study has a broad antibiotic resistance spectrum, posing a challenge to clinical treatment. Plasmids carrying mcr-9 are fusion plasmids, and those taking NDM are widely disseminated in China, suggesting that we should conduct routine genomic surveillance on such plasmids to curb the spread of drug-resistant bacteria in the region.Keywords: E. hormaechei, New Delhi metallo-β-lactamase, mcr-9, whole-genome sequencing, phylogenetic analysis

Published
2022

21. Pharmacological Aspects of Natural Quercetin in Rheumatoid Arthritis

Author

Tang M, Zeng Y, Peng W, Xie X, Yang Y, Ji B, and Li F

Subjects
rheumatoid arthritis, quercetin, pharmacological., Therapeutics. Pharmacology, RM1-950
Abstract

Mengshi Tang,1,&ast; Yan Zeng,2,&ast; Weijun Peng,3 Xi Xie,1 Yongyu Yang,4 Biting Ji,5 Fen Li1 1Department of Rheumatology and Immunology, the Second Xiangya Hospital, Central South University, Changsha, 410011, People’s Republic of China; 2Department of Rheumatology, Yueyang Central Hospital, Yueyang, 414000, People’s Republic of China; 3Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, 410011, People’s Republic of China; 4Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, 410011, People’s Republic of China; 5Shanghai Jing’an District Dental Disease Prevention and Control Institute, Shanghai, 200040, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Fen Li, Department of Rheumatology and Immunology, the Second Xiangya Hospital, Central South University, No. 139, Renmin Middle Road, Changsha, 410011, People’s Republic of China, Email lifen0731@csu.edu.cnAbstract: Rheumatoid arthritis (RA) is a chronic autoimmune disease that can lead to severe joint damage, disability and mortality. Quercetin (QUE) is a natural flavonoid that is ubiquitous in fruits and vegetables. This article reviews the effect of QUE on articular and extra-articular manifestations of RA in vitro and in vivo. In general, for articular manifestations, QUE inhibited synovial membrane inflammation by reducing inflammatory cytokines and mediators, decreasing oxidative stress, inhibiting proliferation, migration and invasion, and promoting apoptosis of fibroblast-like synoviocytes (FLS), regulated autoimmune response through modulating Th17/Treg imbalance and Th17 cells differentiation, reducing autoantibodies levels and regulating ectonucleoside triphosphate diphosphohydrolase (E-NTPDase)/ectoadenosine deaminase (E-ADA) activities, reduced bony damage via lowering matrix metalloproteinase (MMP)-1, MMP-3, receptor activator of nuclear factor kappa B ligand (RANKL) expression and osteoclasts formation. For extra-articular manifestations, QUE could reverse the neurodegenerative processes of the enteric nervous system (ENS) and exhibited cytoprotective, genoprotective and hepatoprotective effects. In addition, we also summarize some contradictory experimental results and explore the possibility for these differences to form a sound basis for the clinical application of QUE for RA.Keywords: rheumatoid arthritis, quercetin, pharmacological

Published
2022

22. Prognostic Roles of Inflammation- and Nutrition-Based Indicators for Female Patients with Cancer

Author

Yang M, Zhang Q, Ge Y, Tang M, Hu C, Wang Z, Zhang X, Song M, Ruan G, Liu T, Xie H, Zhang H, Zhang K, Li Q, Li X, Liu X, Lin S, and Shi H

Subjects
female, cancer, prognosis, inflammation, nutrition, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Ming Yang,1– 3 Qi Zhang,1– 3 Yizhong Ge,1– 4 Meng Tang,1– 3 Chunlei Hu,1– 3 Ziwen Wang,1– 3 Xi Zhang,1– 3 Mengmeng Song,1– 3 Guotian Ruan,1– 3 Xiaowei Zhang,1– 3 Tong Liu,1– 3 Hailun Xie,1– 3 Heyang Zhang,1– 3 Kangping Zhang,1– 3 Qinqin Li,1– 3 Xiangrui Li,1– 3 Xiaoyue Liu,1– 3 Shiqi Lin,1– 4 Hanping Shi1– 3 1Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China; 2Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China; 3Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China; 4The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of ChinaCorrespondence: Hanping Shi, Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China, Tel/Fax +86 10 6392 6983, Email shihp@ccmu.edu.cnPurpose: The incidence, progression, and prognosis of cancer could be affected by inflammation and nutrition. Female patients have different inflammatory and nutritional states depending on their age and tumor types. It is important to screen for suitable prognostic indicators in female patients with cancer of different ages and tumor types.Patients and Methods: Baseline clinicopathologic and laboratory characteristics of 1502 female patients with cancer were obtained from a multicenter cohort study. Concordance indices (C-indices) were used to evaluate the prediction accuracy of following inflammation- and nutrition-based indicators: advanced lung cancer inflammation index (ALI), systemic immune inflammation index (SII), modified geriatric nutritional risk index (mGNRI), albumin-to-globulin ratio (AGR), prognostic nutritional index (PNI), lymphocyte-to-C-reactive protein ratio (LCR), controlling nutritional status score (CONUT), modified Glasgow prognostic score (mGPS), and lymphocyte-to-C-reactive protein score (LCS).Results: The most suitable indicators in different female populations with cancer had C-indices as follows: LCR (0.668; 95% CI, 0.644– 0.693) for all females; AGR (0.681; 95% CI, 0.619– 0.743) for young females; LCR (0.667; 95% CI, 0.628– 0.706) for middle-aged females; ALI (0.597; 95% CI, 0.574– 0.620) for elderly females; LCR (0.684; 95% CI, 0.621– 0.747) for females with reproductive system cancer; and ALI (0.652; 95% CI, 0.624– 0.680) for females with non-reproductive system cancer.Conclusion: The most suitable indicators for the different female populations with cancer are summarized as follows: LCR for all females, AGR for young females, LCR for middle-aged females, ALI for elderly females, LCR for females with reproductive system cancer, and ALI for females with non-reproductive system cancer.Keywords: female, cancer, prognosis, inflammation, nutrition

Published
2022

23. Decaprenyl Diphosphate Synthase Subunit 1 (PDSS1): A Potential Prognostic Biomarker and Immunotherapy-Target for Hepatocellular Carcinoma

Author

Yang Y, Li J, Tang M, Nie B, and Huang W

Subjects
decaprenyl diphosphate synthase subunit 1, hepatocellular carcinoma, prognosis, tumor microenvironment, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Yuping Yang,&ast; Jinying Li,&ast; Ming Tang, Biao Nie, Wei Huang Department of Gastroenterology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, Guangdong Province, 510630, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Wei Huang, Departments of Gastroenterology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510630, Guangdong Province, People’s Republic of China, Tel +86 020-38688931, Email thuangw@jnu.edu.cnPurpose: PDSS1 (decaprenyl diphosphate synthase subunit 1) plays an important role in the progression of several types of tumor. However, the biological functions of PDSS1 remain unclear in patients with hepatocellular carcinoma (HCC). In this study, We attempted to determine the role of PDSS1 in predicting the survival and efficacy of immunotherapy for HCC patients.Methods: We analyzed the expression of PDSS1 in pan-cancer by Tumor Immune Estimation Resource (TIMER) and UALCAN database. Next, we investigated the correlations between the expression and potential prognostic value of PDSS1 in pan-cancer by Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier plotter and further confirmed our finding in our study of 139 patients with HCC. Furthermore, we correlated expression patterns with the presence of immune cell infiltrates and immune regulatory molecules in HCC tissue by TIMER. Finally, its potential immune-related mechanism was explored by Gene Set Enrichment Analysis (GSEA).Results: Multiple datasets demonstrate that PDSS1 is up-regulated in HCC tissues compared with adjacent tissues, which was validated at mRNA (databases) and protein levels (our cohort). Patients with higher PDSS1 expression had shorter overall survival and relapse-free survival. In addition, PDSS1 expression was positively related to early recurrence and served as an independent poor prognostic factor for HCC. Patients with higher PDSS1 expression had lower CD8+ T cells in HCC tissue, and PDSS1 deteriorates T cell exhaustion by promoting T cell surface inhibitory receptors’ secretion and immunosuppressive cell proliferation. Furthermore, PDSS1 was positively correlated with the WNT, TGFβ, VEGF, and other signaling pathways in HCC.Conclusion: PDSS1 is a potential prognostic biomarker and immunotherapy target for hepatocellular carcinoma.Keywords: decaprenyl diphosphate synthase subunit 1, hepatocellular carcinoma, prognosis, tumor microenvironment

Published
2022

24. Sweet’s Syndrome Accompanied by Coinfection with Multiple Pathogens and Disseminated Mycobacterium phlei Infection Presenting with Osteolytic Destruction During 12 Years of Follow-Up: A Rare Case Report

Author

Tang M, Pan M, Qiu Y, Huang J, Zeng W, and Zhang J

Subjects
mycobacterium phlei, sweet’s syndrome, osteolytic destruction, anti-ifn-γ autoantibodies, Infectious and parasitic diseases, RC109-216
Abstract

Mengxin Tang,1,2,&ast; Mianluan Pan,1,&ast; Ye Qiu,1 Jie Huang,3 Wen Zeng,2 Jianquan Zhang1 1Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518000, People’s Republic of China; 2Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of China; 3Department of Tuberculosis Ward, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530021, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Wen Zeng, Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of China, Tel +8618589883694, Fax +86771-5350031, Email guigao0302@163.com Jianquan Zhang, Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, 518000, People’s Republic of China, Tel +8613978123845, Fax +86755-23482484, Email jqzhang2002@126.comBackground: Anti-IFN-γ autoantibodies (AIGAs) are closely related to the disseminated infection of multiple pathogens. Mycobacterium phlei (M. phlei) is a nonpathogenic nontuberculous mycobacteria (NTM), and M. phlei infection of the bone is extremely rare. We report a rare case of high-titer AIGAs presenting with Sweet’s syndrome (SS) accompanied by opportunistic coinfection with multiple pathogens during 12 years of follow-up. The patient in this case also developed disseminated M. phlei infection with osteolytic destruction after treatment for SS.Case Presentation: A 68-year-old Chinese woman was admitted to our hospital in August 2009 due to fever and cough with expectoration for 3 months. The patient was successively infected with Klebsiella pneumoniae, herpes zoster virus and Candida. Chest computed tomography (CT) showed recurrent consolidations in different lung fields. After 15 months of antimicrobial treatment, the patient experienced partial recovery. In September 2010, the patient was pathologically diagnosed with SS due to the presence of multiple rashes. After prednisone and thalidomide treatment, the rashes subsided, and the pulmonary lesions had completely absorbed. In May 2011, the patient was diagnosed with disseminated tuberculosis and was administered anti-tuberculosis therapy for 3 months without improvement. NTM was subsequently cultured from her sputum and chest wall pus, and she improved after 20 months of anti-NTM therapy. In March 2016, the patient developed osteolytic destruction of the C7-T2 vertebral bodies with a back abscess. NTM was eventually cultured from the dorsal abscess pus and further identified as M. phlei. High-titer AIGAs were detected in the patient’s serum. After another round of aggressive anti-NTM therapy, the patient was finally cured.Conclusion: Patients with AIGA-associated anti-cytokine autoantibody disease can present with multiple opportunistic infections and SS involving the lung. AIGA-associated immunodeficiency leads to infection with nonpathogenic M. phlei, which is refractory, can cause relapse, and even leads to osteolytic destruction.Keywords: Mycobacterium phlei, sweet’s syndrome, osteolytic destruction, anti-IFN-γ autoantibodies

Published
2022

25. Clinicopathological and Gene Mutation Analysis of 27 Cases with Extranodal Rosai–Dorfman Disease

Author

Tang M, Gu XZ, Wu PC, and Yang XT

Subjects
rosai–dorfman disease, clinical pathology, immunohistochemistry, diagnosis, identification, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Ming Tang,1,&ast; Xue-Zhong Gu,2,&ast; Peng-Chun Wu,3 Xuan-Tao Yang1 1Department of Pathology, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, People’s Republic of China; 2Department of Hematology, the First People’s Hospital of Yunnan Province, Yunnan Blood Disease Clinical Medical Research Center, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, People’s Republic of China; 3Department of Pathology, Kunming Kingmed Institute for Clinical Laboratory, Kunming, 650506, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xuan-Tao Yang, Department of Pathology, the First People’s Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, No. 157 of Jinbi Road, Xishan District, Kunming, 650032, People’s Republic of China, Tel +86 871 63638441, Email yangxt832@126.com Peng-Chun Wu, Department of Pathology, Kunming Kingmed Institute for Clinical Laboratory, K13-K14, Daliyuan Road, Chenggong District, Kunming, 650506, People’s Republic of China, Tel +86 871 68172222, Email wupengchunwpc70@126.comObjective: To investigate the clinicopathological features, and mutations of NRAS, KRAS, BRAF and MAP2K1 genes in extranodal Rosai–Dorfman disease (RDD).Methods: The clinic opathological features of 27 patients with extranodal RDD were retrospectively analyzed, and the NRAS, KRAS, BRAF and MAP2K1 genes mutation were detected by Sanger sequencing.Results: The male to female ratio was 1.7:1. The average age was 46.9 years. There were skin lesions in 12 cases (44.4%) and head and neck lesions in 8 cases (29.6%). Microscopically, those patients with skin RDD had lesions characterized by clear and dark intervals and obvious emperipolesis, while in other parts, the background was more complex. About 21.1% (4/19) had mutations, including 3 mutations in NRAS 2 exon and 1 mutation in KRAS 2 exon. Two of the three NRAS mutations were located in the skin, accounting for 20% (2/10) of skin RDD.Conclusion: Extranodal RDD was more common in males than in females, and might occur in all ages, with a greater incidence in skin, head, and neck. Besides the obvious microscopic characteristics in those with skin RDD, the background of other parts was complex and easily missed or misdiagnosed. Some RDD with gene mutations, mainly in NRAS 2 exon, especially in skin RDD, support partial RDD is a clonal disease.Keywords: Rosai–Dorfman disease, clinical pathology, gene mutation, NRAS, KRAS, BRAF, MAP2K1, diagnosis, identification

Published
2022

26. Acute Post-Cataract Endophthalmitis Due to Phialemoniopsis curvata: A Rare Case Report

Author

Tang M, Li J, Xia F, Min C, Liu Z, Hu Y, Wang H, Xu H, and Zou M

Subjects
phialemoniopsis curvatum, fungal endophthalmitis, cataract surgery, voriconazole, Infectious and parasitic diseases, RC109-216
Abstract

Mengli Tang,1,&ast; Jun Li,1,2,&ast; Fengjun Xia,1 Changhang Min,1 Zhaojun Liu,1 Yongmei Hu,1,2 Haichen Wang,1,2 Heping Xu,3 Mingxiang Zou1,2 1Department of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 2National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 3Department of Clinical Laboratory, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Mingxiang Zou, Department of Clinical Laboratory, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, 410008, Hunan, People’s Republic of China, Tel +86 13907496278, Email zoumingxiang@csu.edu.cnAbstract: Phialemonium species are a class of opportunistic pathogenic fungi widely present in the environment that cause invasive diseases in hosts with normal or weak immune functions. Common infections include peritonitis, endocarditis, osteomyelitis, and skin infections of wounds after burns, whereas endophthalmitis is rarely reported. Here, we report acute post-cataract endophthalmitis caused by Phialemoniopsis curvata in China. The isolated pathogen was identified using microscopy, culture, and sequencing. After vitrectomy, intraocular lens removal surgery, voriconazole injection, and topical voriconazole treatment, the patient’s symptoms were alleviated.Keywords: Phialemoniopsis curvata, fungal endophthalmitis, cataract surgery, voriconazole

Published
2022

27. Characterization of Silver Resistance and Coexistence of sil Operon with Antibiotic Resistance Genes Among Gram-Negative Pathogens Isolated from Wound Samples by Using Whole-Genome Sequencing

Author

Wang H, Li J, Min C, Xia F, Tang M, Hu Y, and Zou M

Subjects
silver resistance, gram negative pathogen, sil operon, antibiotic resistance gene, whole-genome sequencing, Infectious and parasitic diseases, RC109-216
Abstract

Haichen Wang,1,2 Jia Li,2,3 Changhang Min,1,2 Fengjun Xia,1,2 Mengli Tang,1,2 Jun Li,1,2 Yongmei Hu,1,2 Mingxiang Zou1,2 1National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan Province, People’s Republic of China; 2Department of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha, Hunan Province, People’s Republic of China; 3Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of ChinaCorrespondence: Mingxiang Zou, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 41008, People’s Republic of China, Tel/Fax +86 7384327440, Email zoumingxiang@csu.edu.cnPurpose: Due to the extensive consumption of silver-containing compound, silver resistance spreads among gram-negative pathogens and is regarded as a great public problem. In this study, we investigated silver resistance mechanisms and antibiotic resistance genes co-harbored with sil operon among gram-negative pathogens isolated from wound samples.Methods: A total of 193 strains of gram-negative pathogens were collected from wound samples between 2018 and 2020 in Xiangya hospital. Silver resistance was obtained by broth microdilution method. The silver resistance mechanisms and the prevalence, genetic environments, and coexistence with antibiotic resistance genes of sil operon were investigated by polymerase chain reaction (PCR) and whole genome sequencing (WGS).Results: Among 193 strains, nine strains (4.7%) were resistant to Ag+ and assigned to the following species: Klebsiella pneumoniae (n = 5) and Enterobacter hormaechei (n = 4). WGS confirmed that 24 strains carried the entire sil operon, including the four Ag+-resistant E. hormaechei and 20 Ag+-susceptible strains, while PCR failed to detect some sil genes, especially silE, due to sequence variations. In seven strains, Tn7 transposon was identified in the upstream of sil operon. Spontaneous mutants resistant to Ag+ were induced in 15 out of 20 Ag+-susceptible strains, including K. pneumoniae strains belonged to high-risk groups (ST11 and ST15). The sil-positive strains harbored various antibiotic resistance genes, including blaESBL and blaApmC. WGS revealed that a single mutation in cusS gene and loss of major porins conferred silver resistance in the five K. pneumoniae strains.Conclusion: Our findings emphasize the cryptic silver resistance is prevalent among Enterobacteriaceae with sil operon or with the combination of cus operon and major porin loss and increase the understanding of the prevalence of sil operon with antibiotic resistance genes, especially blaESBL and blaApmC.Keywords: silver resistance, gram negative pathogen, sil operon, antibiotic resistance gene, whole-genome sequencing

Published
2022

28. Combination of Nutritional Risk Index and Handgrip Strength on the Survival of Patients with Cancer Cachexia: A Multi- Center Cohort Study

Author

Xie H, Ruan G, Zhang Q, Ge Y, Song M, Zhang X, Liu X, Lin S, Li X, Zhang K, Yang M, Tang M, Cong M, and Shi H

Subjects
nutritional risk index, handgrip strength, cancer cachexia, prognostic, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Hailun Xie,1– 3,&ast; Guotian Ruan,1– 3,&ast; Qi Zhang,1– 3,&ast; Yizhong Ge,1– 3 Mengmeng Song,1– 3 Xi Zhang,1– 3 Xiaoyue Liu,1– 3 Shiqi Lin,1– 3 Xiaowei Zhang,1– 3 Xiangrui Li,1– 3 Kangping Zhang,1– 3 Ming Yang,1– 3 Meng Tang,1– 3 Minghua Cong,4 Hanping Shi1– 3 1Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China; 2Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China; 3Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, People’s Republic of China; 4Department of Comprehensive Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Hanping Shi, Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China, Email shihp@ccmu.edu.cnPurpose: The nutritional risk index (NRI) and handgrip strength (HGS) are useful indicators of nutritional and physical status, respectively, both of them can predict the prognosis of many cancers; however, the predictive accuracy of a single indicator is unsatisfactory. Whether the combination of NRI and HGS could enhance the stratification of the prognosis of patients with cancer cachexia.Patients and Methods: The study population was randomly divided into training and validation cohorts. We combined NRI and HGS and constructed the NRI–HGS score. Restricted cubic splines were used to assess the associations between NRI, HGS, and all-cause mortality. The Kaplan–Meier method was used to calculate the survival probability of the patients. The Cox proportional hazards risk model was used to analyze survival and prognostic factors.Results: Low NRI and low HGS were independent predictors of poor prognosis in patients with cancer cachexia. The NRI–HGS score showed a better prognostic stratification than either NRI or HGS. The co-occurrence of low NRI and low HGS was associated with an approximately 1.8-fold increased risk of mortality. The NRI–HGS score could effectively distinguish patients with a poor prognosis at different pathological stages. Furthermore, we constructed a novel prognostic nomogram based on NRI and HGS. The concordance index and calibration plot confirmed that the nomogram had good prognostic accuracy. The area under the receiver operating characteristic curve of the nomogram reached > 0.8, which was much higher than that of the traditional tumor–node–metastasis staging system. The nomogram provided better prognostic stratification for patients with cancer cachexia.Conclusion: Low NRI and low HGS are independent prognostic indicators in cancer cachexia. The combination of NRI and HGS improve prognostic stratification for patients with cancer cachexia. Our study suggests combining nutritional and physical status in future cachexia research.Keywords: nutritional risk index, handgrip strength, cancer cachexia, prognostic

Published
2022

29. Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese Cohort

Author

Tang M, Gao L, He B, and Yang Y

Subjects
colon cancer, machine learning, extreme gradient boosting, prognostic prediction models, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Mo Tang,1 Lihao Gao,2 Bin He,1 Yufei Yang1 1Oncology Department, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China; 2Smart City Business Unit, Baidu Inc., Beijing, People’s Republic of ChinaCorrespondence: Yufei YangXiyuan Hospital of China Academy of Chinese Medical Sciences, No. 1 Xiyuan Caochang Road, Haidian District, Beijing, 100091, People’s Republic of ChinaEmail yyf93@vip.sina.comLihao GaoSmart City Business Unit, Baidu Inc., No. 51 Dezhen Road, Haidian District, Beijing, 100091, People’s Republic of ChinaEmail gaolihao@baidu.comPurpose: The present study aimed to develop prognostic prediction models based on machine learning (ML) for non-metastatic colon cancer (CRC), which can provide a precise quantitative risk assessment and serve as an assistive method for treatment strategy development. The possibility of improving prediction accuracy using nonlinear methods compared to linear methods was investigated.Patients and Methods: A cancer-specific survival (CSS) model constructed using logistic regression, extreme gradient boosting (XGBoost), and random forest algorithms was trained on the Surveillance, Epidemiology, and End Results datasets for 15,254 patients with non-metastatic CRC (split into training [70%] and internal validation [30%] datasets) and externally validated with an outpatient cohort of 311 cases from Xiyuan Hospital in China. A Chinese cohort was also used to develop recurrence and metastasis (R&M) models for CRC patients. The experiments for each model were performed 100 times to obtain average scores and 95% confidence intervals. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) values.Results: The XGBoost approach showed the highest AUC values of 0.86 (0.84– 0.88), 0.82 (0.81– 0.83), and 0.81 (0.79– 0.82) for one-, three-, and five-year CSS cohorts, respectively, along with a relatively high generalization ability. The XGBoost approach also performed best for the R&M model, with the AUC values of 0.71 (0.64– 0.79), 0.79 (0.74– 0.86), and 0.89 (0.82– 0.95) for one-, three-, and five-year R&M cohorts, respectively. The rankings of predictor importance for the CSS and R&M models were different, and the higher model accuracy was associated with more prognostic predictors.Conclusion: Three different ML algorithms for developing prognostic prediction models for non-metastatic CRC were compared. The predictive performance results showed that the nonlinear XGBoost approach performed best, suggesting that it can be used for quantifying the prognostic risk. It was also demonstrated that the model performance can be improved when more prognostic predictors are considered.Keywords: colon cancer, machine learning, extreme gradient boosting, prognostic prediction models

Published
2022

30. Retrospective Analysis of 10 Cases of Disseminated Nontuberculous Mycobacterial Disease with Osteolytic Lesions

Author

Tang M, Huang J, Zeng W, Huang Y, Lei Y, Qiu Y, and Zhang J

Subjects
hiv-negative, nontuberculous mycobacteria, osteolytic lesion, anti-ifn-γ autoantibodies, Infectious and parasitic diseases, RC109-216
Abstract

Mengxin Tang,1,2,&ast; Jie Huang,3,&ast; Wen Zeng,1,2 Yanmei Huang,4 Yaoqiang Lei,5 Ye Qiu,6 Jianquan Zhang1,2,4 1Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of China; 2Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of China; 3Department of Tuberculosis Ward, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530021, People’s Republic of China; 4Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong, 518000, People’s Republic of China; 5Department of Infectious Diseases, Yongning District People’s Hospital, Nanning, Guangxi, 530021, People’s Republic of China; 6Department of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ye QiuDepartment of Comprehensive Internal Medicine, The Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of ChinaTel +8615676192180Fax +86771– 5719573Email yeqiu2013graduated@163.comJianquan ZhangDepartment of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, People’s Republic of ChinaTel +8613978123845Fax +86755-23482484Email jqzhang2002@126.comPurpose: Disseminated nontuberculous mycobacterial (DNTM) infection can involve multiple organs, including the lungs, skin and soft tissues and lymph nodes. However, NTM infection leading to osteolysis has been rarely reported. Here, we analyzed the clinical features, osteolytic mechanisms, treatment and prognosis of patients with DNTM disease with osteolytic lesions.Patients and Methods: This retrospective study was conducted between January 1, 2011, and December 31, 2020, at the First Affiliated Hospital of Guangxi Medical University and the Fourth People’s Hospital of Nanning City. Patients who had culture and/or histopathological proof of DNTM disease with osteolytic lesions were included.Results: Ten HIV-negative patients with DNTM disease with osteolytic lesions were enrolled. Five of these patients had underlying diseases. Seven and three of the patients were positive and negative for anti-interferon-γ autoantibodies (AIGAs), respectively. The AIGA positivity rate was 70% (7/10). Ostealgia and anemia were the most common symptoms, followed by fever, emaciation, cough, expectoration, anorexia, subcutaneous abscesses and lymphadenopathy. Leukocyte and neutrophil counts were increased. The most common sites were the vertebrae, sternum, clavicle and ribs, although the femur, ilium, humerus, and scapula were also involved. Radiography and computed tomography (CT) showed moth-eaten or irregular destruction of bone, bone defects, pathological fracture, periosteal proliferation and surrounding abscesses. Emission CT (ECT) bone scans showed significantly increased uptake in many skeletal regions. Positron emission tomography(PET)/CT showed metabolic activity in multiple bones. All patients received anti-nontuberculous therapy, and five underwent surgery. Two died during treatment.Conclusion: DNTM infection of bone and leading to osteolysis usually occurs in patients with AIGA-positive antibodies. DNTM disease with osteolysis is characterized by increased leukocytes and neutrophil counts, focal suppurative granulomas, and multiple areas with moth-eaten or irregular destruction of bone with increased radioactive concentrations. Early diagnosis and timely, effective combination anti-NTM therapy can improve the prognosis.Keywords: HIV-negative, nontuberculous mycobacteria, osteolytic lesion, anti-IFN-γ autoantibodies

Published
2021

31. Clinical and Prognostic Implications of 1p/19q, IDH, BRAF, MGMT Promoter, and TERT Promoter Alterations, and Expression of Ki-67 and p53 in Human Gliomas

Author

Yang Z, Ling F, Ruan S, Hu J, Tang M, Sun X, and Long W

Subjects
human glioma, biomarker, correlation analysis, genetic alteration, survival rate, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Zixi Yang,1 Feng Ling,1 Sibei Ruan,1 Jiajia Hu,2 Mingxi Tang,1 Xingwang Sun,1 Wenbo Long1 1Pathology Department of the First Affiliated Hospital, Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 2School of Basic Medicine, Southwest Medical University, Luzhou, Sichuan, People’s Republic of ChinaCorrespondence: Wenbo Long Email wenbolong@swmu.edu.cnBackground and Objective: Genetic alterations, including IDH, BRAF, and TERT promoter mutations (IDH-mu, BRAF-mu, TERTp-mu, respectively), 1p/19q co-deletion (1p/19q-codel), and MGMT promoter methylation (MGMTp-M), are correlated with glioma tumor development. Therefore, these genetic alterations could serve as biomarkers for the diagnosis, prognosis, and classification of gliomas, combined with the immunohistochemical markers Ki-67 and p53. However, the correlation between these alterations and the expression of Ki-67 and p53 is poorly understood.Methods: We analyzed the prevalence and prognosis of these five alterations, as well as Ki-67 and p53 expression, in 103 primary grade II–IV gliomas via fluorescence qPCR, Sanger sequencing, fluorescence in situ hybridization, and immunohistochemistry.Results: In the 103 cases, MGMTp-M was the most common alteration (70.9%), followed by TERTp-mu (58.3%), IDH-mu (46.6%), 1p/19q-codel (34.0%), and BRAF-mu (5.8%). No cases showed quintuple-positive alterations, but 26 cases (25.2%) showed quadruple-positive alterations (IDH-mu/TERTp-mu/MGMTp-M/1p/19q-codel). The percentage of TERTp-mu and 1p/19q-codel cases decreased with p53 expression, and the percentage of IDH-mu and 1p/19q-codel cases decreased with Ki-67 expression. IDH-mu, MGMTp-M, and 1p/19q-codel were positive factors for survival rates in glioma patients, while TERTp-mu, p53, and Ki-67 positivity were negative factors. Old age, histological grade IV, IDH-mu, 1p/19q-codel, Ki-67+, and p53+/Ki-67+ were significantly correlated with overall survival (OS). However, only p53+/Ki-67+ was an independent prognostic factor for OS in the multivariate Cox-model analysis.Conclusion: IDH-mu only and quadruple-positivity were associated with good OS in glioma patients, while TERTp-mu only, TERTp-mu/MGMTp-M and p53+/Ki-67+ were associated with poor prognosis. Combining these genomic alterations and Ki-67/p53 expression should have clinical value in gliomas.Keywords: human glioma, biomarker, correlation analysis, genetic alteration, survival rate

Published
2021

32. Whole-Genomic Analysis of NDM-5-Producing Enterobacteriaceae Recovered from an Urban River in China

Author

Li Y, Tang M, Dai X, Zhou Y, Zhang Z, Qiu Y, Li C, and Zhang L

Subjects
carbapenemase, antimicrobial resistance, aquatic environment, river water, Infectious and parasitic diseases, RC109-216
Abstract

Ying Li,1,2,&ast; Min Tang,3,&ast; Xiaoyi Dai,4,5 Yingshun Zhou,4,5 Zhikun Zhang,4,5 Yichuan Qiu,4,5 Chengwen Li,1,2 Luhua Zhang4,5 1Department of Immunology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 2Immunological Technology Platform, Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 3Department of Laboratory Medicine, the Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 4Department of Pathogenic Biology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 5Pathogen Biology Platform, Southwest Medical University, Luzhou, Sichuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Luhua Zhang; Chengwen Li Xiang Lin Road, Longmatan District, Luzhou City, Sichuan Province, 646000, People’s Republic of ChinaTel/Fax +86-0830-3160073Email zhluhua@swmu.edu.cn; Lichw110119@163.comPurpose: Three NDM-5-producing Enterobacteriaceae (Escherichia coli, Klebsiella pneumoniae, and Citrobacter braakii, one each) were isolated during a screening study for the presence of carbapenemase-producing Enterobacteriaceae (CPE) strains in urban rivers in China. The aim of the present study was to characterize these NDM-5-producing isolates by using whole-genome analysis.Methods: In vitro susceptibility testing was performed using the broth microdilution method. Conjugation assay was carried out to investigate the transferability of blaNDM-5-harboring plasmids. Whole-genome sequencing was performed using an Illumina HiSeq combined with the PacBio RSII system. The genetic characteristics of the blaNDM-5-harboring plasmids were analyzed. Antimicrobial resistance genes and virulence genes were identified from the genome sequences. Phylogenetic analysis was performed based on core genome.Results: Antimicrobial susceptibility testing showed that all three isolates were resistant to carbapenems, cephalosporins, quinolones, and aminoglycosides, and susceptible to colistin. Whole-genome sequencing showed that each isolate carried multiple antibiotic resistance genes mediating multidrug resistance, and harbored numerous virulence genes, some of which were located on plasmids. In these isolates, blaNDM-5 was carried by an IncX3 plasmid in K. pneumoniae and C. braakii, and on an IncR/IncX1 plasmid in E. coli. Conjugation experiments showed that these blaNDM-5-haboring plasmids were successfully transferred to E. coli J53. Phylogenetic analysis revealed that E. coli SCLZR49 was present in a cluster with isolates of different origin, K. pneumoniae SCLZR50 was mainly clustered with clinical isolates, and C. braakii SCLZR53 had closely genetic relationship with environmental isolates.Conclusion: This study revealed contamination of the urban river ecosystems by clinically significant carbapenemase gene blaNDM-5, raising the possibility of plasmid transmission into the environmental from humans and highlighting the need for a constant surveillance of CPE in the environment under the “One-Health” perspective.Keywords: carbapenemase, antimicrobial resistance, aquatic environment, river water

Published
2021

33. Association of Systemic Inflammation and Overall Survival in Elderly Patients with Cancer Cachexia – Results from a Multicenter Study

Author

Ruan GT, Yang M, Zhang XW, Song MM, Hu CL, Ge YZ, Xie HL, Liu T, Tang M, Zhang Q, Zhang X, Zhang KP, Li XR, Li QQ, Chen YB, Yu KY, Cong MH, Wang KH, and Shi HP

Subjects
ali, systemic inflammation, cancer cachexia, elderly, overall survival, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Guo-Tian Ruan,1– 3,&ast; Ming Yang,1– 3,&ast; Xiao-Wei Zhang,1– 3,&ast; Meng-Meng Song,1– 3 Chun-Lei Hu,1– 3 Yi-Zhong Ge,1– 3 Hai-Lun Xie,1– 3 Tong Liu,1– 3 Meng Tang,1– 3 Qi Zhang,1– 3 Xi Zhang,1– 3 Kang-Ping Zhang,1– 3 Xiang-Rui Li,1– 3 Qin-Qin Li,1– 3 Yong-Bing Chen,1– 3 Kai-Ying Yu,1– 3 Ming-Hua Cong,4 Kun-Hua Wang,5 Han-Ping Shi1– 3 1Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China; 2Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, People’s Republic of China; 3Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, People’s Republic of China; 4Comprehensive Oncology Department, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100038, People’s Republic of China; 5Department of Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, 650000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Han-Ping ShiDepartment of Gastrointestinal Surgery and Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, People’s Republic of ChinaTel +86-10- 6392 6985Fax +86-10 -6392 6325Email shihp@ccmu.edu.cnBackground: Systemic inflammation and cachexia are associated with adverse clinical outcomes in elderly patients with cancer. The survival outcomes of elderly patients with cancer cachexia (EPCC) with high inflammation and a high risk of mortality are unknown. This study aimed to investigate the impact of high inflammation on the prognosis of EPCC patients with high mortality.Patients and Methods: This multicenter cohort study included 746 EPCC (age > 65 years) with a mean age of 72.00 ± 5.24 years, of whom 489 (65.5%) were male. The cut-off value for the inflammation index was obtained using the optimal survival curve. The different inflammatory indicators were assessed using the concordance index (C-index), decision curve analysis (DCA), and prognostic receiver operating characteristic (ROC). The high mortality risk group of EPCC was defined by the 2011 Fearon Cancer Diagnostic Consensus. EPCC were divided into the high-risk group, which satisfies three diagnostic criteria, and a low-risk group, which satisfies only one or two diagnostic criteria.Results: The C-index, DCA, and prognostic ROC indicated the superiority of advanced lung cancer inflammation index (ALI) compared with other indicators, including neutrophil–lymphocyte ratio (NLR), prognostic nutritional index (PNI), systemic immune-inflammation index (SII), and platelet–lymphocyte ratio (PLR). Whether ALI was used as a continuous or a categorical variable, ALI had a better prognostic value in EPCC compared with other inflammatory indicators. In particular, patients with low ALI (< 25.03) had a worse overall survival (OS) than patients with high ALI (≥ 25.03) (P < 0.001, HR [95% CI] = 2.092 [1.590– 2.751]). The combination effect analysis showed that the risk of mortality of the patients in the low-ALI and high-risk groups was 3.095-fold higher than that of patients in the high-ALI and low-risk groups.Conclusion: The prognostic and discriminative value of the inflammatory indicator ALI was better than that of NLR, PNI, SII, and PLR in EPCC. The high-risk group of EPCC with a low ALI would increase the death risk of OS.Keywords: ALI, systemic inflammation, cancer cachexia, elderly, overall survival

Published
2021

34. Study of the light scalar $a_{0}(980)$ through the decay $D^{0} \to a_{0}(980)^-e^{+} \nu_{e}$ with $a_{0}(980)^- \to \eta \pi^-$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using 7.93 ${\rm fb^{-1}}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773 ${\rm GeV}$ with the BESIII detector, we present an analysis of the decay $D^{0} \to \eta \pi^- e^+ \nu_{e}$. The branching fraction of the decay $D^{0} \to a_{0}(980)^{-} e^+ \nu_{e}$ with $a_{0}(980)^{-} \to \eta \pi^{-}$ is measured to be $(0.86\pm0.17_{\text{stat}}\pm0.05_{\text{syst}})\times 10^{-4}$. The decay dynamics of this process is studied with a single-pole parameterization of the hadronic form factor and the Flatt\'e formula describing the $a_0(980)$ line shape in the differential decay rate. The product of the form factor $f^{ a_0}_{+}(0)$ and the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ is determined for the first time with the result $f^{ a_0}_+(0)|V_{cd}|=0.126\pm0.013_{\rm stat}\pm0.003_{\rm syst}$.

Published
2024

35. Search for $\Lambda$-$\bar{\Lambda} $ oscillation in $J/\psi\rightarrow\Lambda\bar{\Lambda}$ decay

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using $(10087\pm44)\times 10^{6}$ $J/\psi$ decays collected by the BESIII detector at the BEPCII collider, we search for baryon number violation via $\Lambda-\bar{\Lambda}$ oscillation in the decay $J/\psi \to \Lambda \bar{\Lambda}$. No evidence for $\Lambda-\bar\Lambda$ oscillation is observed. The upper limit on the time-integrated probability of $\Lambda-\bar{\Lambda}$ oscillation is estimated to be $1.4\times 10^{-6}$, corresponding to an oscillation parameter less than $2.1\times 10^{-18}~\mathrm{GeV}$ at $90\%$ confidence level., Comment: 8 pages, 2 figures

Published
2024

36. Measurement of the branching fraction of $D^+ \to \tau^+\nu_{\tau}$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Tian, J. Y., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

By analyzing $e^{+}e^{-}$ collision data with an integrated luminosity of 7.9~fb$^{-1}$ collected with the BESIII detector at the center-of-mass energy of 3.773~GeV, the branching fraction of $D^+\to\tau^+\nu_{\tau}$ is determined as $\mathcal{B}=(9.9\pm 1.1_\mathrm{stat}\pm 0.5_\mathrm{syst})\times10^{-4}$. Taking the most precise result $\mathcal{B}(D^+\to\mu^+\nu_{\mu})=(3.981\pm 0.079_\mathrm{stat}\pm0.040_\mathrm{syst})\times10^{-4}$, we determine $R_{\tau/\mu} = \Gamma(D^+\to\tau^+\nu_{\tau})/\Gamma(D^+\to\mu^+\nu_{\mu})= 2.49\pm0.31$, achieving a factor of two improvement in precision compared to the previous BESIII result. This measurement is in agreement with the standard model prediction of lepton flavor universality within one standard deviation.

Published
2024

37. Search for $\eta_c(2S)\to p\bar{p}$ and branching fraction measurements of $\chi_{cJ} \to p\bar{p}$ via $\psi(2S)$ radiative decays

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De~Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Y. Z., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using $(27.12\pm0.14) \times 10^{8}$ $\psi(2S)$ events collected by the BESIII detector operating at BEPCII, we search for the decay $\eta_c(2S)\to p\bar{p}$ via the process $\psi(2S)\to \gamma\eta_c(2S)$, and only find a signal with a significance of $1.7\,\sigma$. The upper limit of the product branching fraction at the 90% confidence level is determined to be $\mathcal{B}(\psi(2S)\to \gamma\eta_c(2S))\times \mathcal{B}(\eta_c(2S)\to p\bar{p})<2.4\times 10^{-7}$. The branching fractions of $\chi_{cJ}\to p\bar{p}~(J=0,1,2)$ are also measured to be $\mathcal{B}(\chi_{c0}\to p\bar{p})=(2.51\pm0.02\pm0.08)\times 10^{-4}$, $\mathcal{B}(\chi_{c1}\to p\bar{p})=(8.16\pm0.09\pm0.25)\times 10^{-4}$, and $\mathcal{B}(\chi_{c2}\to p\bar{p})=(8.33\pm0.09\pm0.22)\times 10^{-4}$, where the first uncertainty is statistical and the second systematic.

Published
2024

38. Measurement of the branching fractions of the decays $\Lambda_{c}^{+}\rightarrow\Lambda K_{S}^{0}K^{+}$, $\Lambda_{c}^{+}\rightarrow\Lambda K_{S}^{0}\pi^{+}$ and $\Lambda_{c}^{+}\rightarrow\Lambda K^{*+}$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Studies are performed of the Cabibbo-favored decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}K^+$ and the singly Cabibbo-suppressed decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$, based on a sample of $e^{+}e^{-}$ collision data, corresponding to an integrated luminosity of 4.5 fb$^{-1}$, accumulated at center-of-mass energies between $4599.53$ MeV and $4698.82$ MeV with the BESIII detector. The decay $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ is observed for the first time. The branching fractions of $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}K^+$ and $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ are measured to be $(3.04\pm0.30\pm0.16)\times 10^{-3}$ and $(1.73\pm0.27\pm0.10)\times 10^{-3}$, respectively, where the first uncertainties are statistical and the second are systematic. These results correspond to the most precise measurement of these quantities for both decays. Evidence of a $K^{*+}$ contribution in the $\Lambda_{c}^{+}\to\Lambda K_{S}^{0}\pi^+$ decay is found with a statistical significance of $4.7\sigma$. The branching fraction of $\Lambda_{c}^{+}\to\Lambda K^{*+}$ is calculated under three possible interference scenarios.

Published
2024

39. Observation of a rare beta decay of the charmed baryon with a Graph Neural Network

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Larin, P., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. Z., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Patteri, P., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. P., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, S. Q., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment, High Energy Physics - Lattice, High Energy Physics - Phenomenology, Nuclear Experiment
Abstract

The study of beta decay of the charmed baryon provides unique insights into the fundamental mechanism of the strong and electro-weak interactions. The $\Lambda_c^+$, being the lightest charmed baryon, undergoes disintegration solely through the charm quark weak decay. Its beta decay provides an ideal laboratory for investigating non-perturbative effects in quantum chromodynamics and for constraining the fundamental parameters of the Cabibbo-Kobayashi-Maskawa matrix in weak interaction theory. This article presents the first observation of the Cabibbo-suppressed $\Lambda_c^+$ beta decay into a neutron $\Lambda_c^+ \rightarrow n e^+ \nu_{e}$, based on $4.5~\mathrm{fb}^{-1}$ of electron-positron annihilation data collected with the BESIII detector in the energy region above the $\Lambda^+_c\bar{\Lambda}^-_c$ threshold. A novel machine learning technique, leveraging Graph Neural Networks, has been utilized to effectively separate signals from dominant backgrounds, particularly $\Lambda_c^+ \rightarrow \Lambda e^+ \nu_{e}$. This approach has yielded a statistical significance of more than $10\sigma$. The absolute branching fraction of $\Lambda_c^+ \rightarrow n e^+ \nu_{e}$ is measured to be $(3.57\pm0.34_{\mathrm{stat}}\pm0.14_{\mathrm{syst}})\times 10^{-3}$. For the first time, the CKM matrix element $\left|V_{cd}\right|$ is extracted via a charmed baryon decay to be $0.208\pm0.011_{\rm exp.}\pm0.007_{\rm LQCD}\pm0.001_{\tau_{\Lambda_c^+}}$. This study provides a new probe to further understand fundamental interactions in the charmed baryon sector, and demonstrates the power of modern machine learning techniques in enhancing experimental capability in high energy physics research., Comment: 28 pages, 6 figures

Published
2024

40. Observation of $\chi_{c0}\to\Sigma^{+}\bar{\Sigma}^{-}\eta$ and evidence for $\chi_{c1,2}\to\Sigma^{+}\bar{\Sigma}^{-}\eta$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using $(27.12\pm 0.14)\times10^{8}$ $\psi(3686)$ events collected with the BESIII detector, the decay $\chi_{c0}\to\Sigma^{+}\bar{\Sigma}^{-}\eta$ is observed for the first time with a statistical significance of $7.0\sigma$, and evidence for $\chi_{c1}\to\Sigma^{+}\bar{\Sigma}^{-}\eta$ and $\chi_{c2}\to\Sigma^{+}\bar{\Sigma}^{-}\eta$ is found with statistical significances of $4.3\sigma$ and $4.6\sigma$, respectively. The branching fractions are determined to be $\mathcal{B}(\chi_{c0}\to\Sigma^{+}\bar{\Sigma}^{-}\eta)=({1.26 \pm 0.20 \pm 0.13}) \times 10^{-4}, ~\mathcal{B}(\chi_{c1}\to\Sigma^{+}\bar{\Sigma}^{-}\eta)=({5.10 \pm 1.21 \pm 0.67}) \times 10^{-5}$, and $\mathcal{B}(\chi_{c2}\to\Sigma^{+}\bar{\Sigma}^{-}\eta)=({5.46 \pm 1.18 \pm 0.50}) \times 10^{-5}$, where the first uncertainties are statistical, and the second ones are systematic.

Published
2024

41. Observation of the Singly Cabibbo-Suppressed Decay $\Lambda_c^{+}\to p\pi^0$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

Utilizing 4.5${~\rm{fb}}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at center-of-mass energies between 4.600 and 4.699 GeV, the first observation of the singly Cabibbo-suppressed decay $\Lambda_c^{+}\to p\pi^0$ is presented, with a statistical significance of $5.4\sigma$. The ratio of the branching fractions of $\Lambda_c^{+}\to p\pi^0$ and $\Lambda_c^{+}\to p\eta$ is measured as $\mathcal{B}(\Lambda_c^{+}\to p\pi^0)/\mathcal{B}(\Lambda_c^{+}\to p\eta)=(0.120\pm0.026_{\rm stat.}\pm0.007_{\rm syst.})$. This result resolves the longstanding discrepancy between earlier experimental searches, providing both a decisive conclusion and valuable input for QCD-inspired theoretical models. A sophisticated deep learning approach using a Transformer-based architecture is employed to distinguish the signal from the prevalent hadronic backgrounds, complemented by thorough validation and systematic uncertainty quantification., Comment: 9 pages, 4 figures

Published
2024

42. Search for $e^{+}e^{-} \to \phi \chi_{c0}$ and $\phi\eta_{c2}(1D)$ at center-of-mass energies from 4.47 to 4.95 GeV

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Utilizing a data set of $6.7$ fb$^{-1}$ from electron-positron collisions recorded by the BESIII detector at the BEPCII storage ring, a search is conducted for the processes $e^{+}e^{-} \to \phi \chi_{c0}$ and $\phi\eta_{c2}(1D)$ across center-of-mass energies from 4.47 to 4.95 GeV. In the absence of any significant signals, upper limits are set. These include limits on the Born cross sections for $e^{+}e^{-} \to \phi \chi_{c0}$, as well as the product of the Born cross section for $e^{+}e^{-} \to \phi\eta_{c2}(1D)$ and a sum of five branching fractions. Furthermore, the product of the electronic width of $Y(4660)$ and the branching fraction of the $Y(4660) \to \phi\chi_{c0}$, denoted as $\Gamma^{Y(4660)}_{e^{+}e^{-}} \mathcal{B}_{Y(4660) \to \phi\chi_{c0}}$, is determined to be $< 0.40$ eV at the 90\% confidence level., Comment: 14 pages, 6 figures

Published
2024

43. Observation of $\chi_{cJ}\to p \bar p K^0_S K^- \pi^+ + c.c.$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

By analyzing $(27.12\pm0.14)\times10^8$ $\psi(3686)$ events collected with the BESIII detector operating at the BEPCII collider, the decays of $\chi_{cJ} \to p \bar{p} K^0_S K^- \pi^+ +c.c.(J=0, 1, 2)$ are observed for the first time with statistical significances greater than $10\sigma$. The branching fractions of these decays are determined to be $\mathcal{B}(\chi_{c0}\to p \bar p K^{0}_{S} K^- \pi^+ + c.c.)=(2.61\pm0.27\pm0.32)\times10^{-5},$ $\mathcal{B}(\chi_{c1}\to p \bar p K^{0}_{S} K^- \pi^+ + c.c.)=(4.16\pm0.24\pm0.46)\times10^{-5},$ and $\mathcal{B}(\chi_{c2}\to p \bar p K^{0}_{S} K^- \pi^+ + c.c.)=(5.63\pm0.28\pm0.46)\times10^{-5}$, respectively. The processes $\chi_{c1,2} \to \bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.$ are also observed, with statistical significances of 5.7$\sigma$ and 7.0$\sigma$, respectively. Evidence for $\chi_{c0} \to\bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.$ is found with statistical significances of 3.3$\sigma$ each. The corresponding branching fractions are determined to be $\mathcal{B}(\chi_{c0}\to \bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.) =(1.61^{+0.68}_{-0.64}\pm0.23)\times10^{-5}$, $\mathcal{B}(\chi_{c1}\to \bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.)=(4.06^{+0.80}_{-0.76}\pm0.52)\times10^{-5}$, and $\mathcal{B}(\chi_{c2}\to \bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.)=(4.09^{+0.87}_{-0.84}\pm0.42)\times10^{-5}$. Here, the first uncertainties are statistical and the second ones are systematic., Comment: 12 pages, 5 figures

Published
2024

44. Observation of $D^+\to\eta^\prime\mu^+\nu_\mu$ and First Study of $D^+\to \eta^\prime \ell^+\nu_\ell$ Decay Dynamics

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using $20.3\,\rm fb^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy 3.773\,GeV with the BESIII detector, we report the first observation of the semileptonic decay $D^+\to \eta^\prime \mu^+\nu_\mu$ with significance of $8.6\sigma$ including systematic uncertainties, and an improved measurement of $D^+\to \eta^\prime e^+\nu_e$. The branching fractions of $D^+\to \eta^\prime \mu^+\nu_\mu$ and $D^+\to \eta^\prime e^+\nu_e$ are determined to be $(1.92\pm0.28_{\rm stat}\pm 0.08_{\rm syst})\times 10^{-4}$ and $(1.79\pm0.19_{\rm stat}\pm 0.07_{\rm syst})\times 10^{-4}$, respectively. From an analysis of the $D^+\to \eta^\prime \ell^+\nu_\ell$ decay dynamics, the product of the hadronic form factor $f_+^{\eta^{\prime}}(0)$ and the CKM matrix element $|V_{cd}|$ is measured for the first time, giving $f^{\eta^\prime}_+(0)|V_{cd}| = (5.92\pm0.56_{\rm stat}\pm0.13_{\rm syst})\times 10^{-2}$. No evidence for violation of $\mu-e$ lepton-flavor universality is found in both the full range and several bins of $\ell^+\nu_\ell$ four-momentum transfer. The $\eta-\eta^\prime$ mixing angle in the quark flavor basis is determined to be $\phi_{\rm P} =(39.8\pm0.8_{\rm stat}\pm0.3_{\rm syst})^\circ$.

Published
2024

45. Precision Measurement of the Branching Fraction of $D^{+}\to \mu^{+}\nu_{\mu}$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using $20.3~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data collected at a center-of-mass energy of $E_{\rm cm}=3.773$ GeV with the BESIII detector operating at the BEPCII collider, we determine the branching fraction of the leptonic decay $D^+\to\mu^+\nu_\mu$ to be $(3.981\pm0.079_{\rm stat}\pm0.040_{\rm syst})\times10^{-4}$. Interpreting our measurement with knowledge of the Fermi coupling constant $G_F$, the masses of the $D^+$ and $\mu^+$ as well as the lifetime of the $D^+$, we determine $f_{D^+}|V_{cd}|=(47.53\pm0.48_{\rm stat}\pm0.24_{\rm syst}\pm0.12_{\rm input})~\mathrm{MeV}$. This result is a factor of 2.3 more precise than the previous best measurement. Using the value of the magnitude of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ given by the global standard model fit, we obtain the $D^+$ decay constant $f_{D^+}=(211.5\pm2.3_{\rm stat}\pm1.1_{\rm syst}\pm0.8_{\rm input})$ MeV. Alternatively, using the value of $f_{D^+}$ from a precise lattice quantum chromodynamics calculation, we extract $|V_{cd}|=0.2242\pm0.0023_{\rm stat}\pm0.0011_{\rm syst}\pm0.0009_{\rm input}$., Comment: 9 pages, 2 figures

Published
2024

46. Search for the radiative decays $D^+\to\gamma\rho^+$ and $D^+\to\gamma K^{*+}$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

We search for the radiative decays $D^{+} \to \gamma \rho^+$ and $D^{+} \to \gamma K^{*+}$ using 20.3~fb$^{-1}$ of $e^+e^-$ annihilation data collected at the center-of-mass energy $\sqrt{s}=3.773$ GeV by the BESIII detector operating at the BEPCII collider. No significant signals are observed, and the upper limits on the branching fractions of $D^{+} \to \gamma \rho^+$ and $D^{+} \to \gamma K^{*+}$ at 90\% confidence level are set to be $1.3\times10^{-5}$ and $1.8\times10^{-5}$, respectively.

Published
2024

47. Search for lepton number violating decays of $D_s^+\to h^-h^0e^+e^+$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Huang, Z. Y., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, X. Y., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Based on 7.33 fb$^{-1}$ of $e^+e^-$ collision data collected by the BESIII detector operating at the BEPCII collider at center-of-mass energies from 4.128 to 4.226 GeV, a search for the Majorana neutrino $\nu_m$ is conducted in the lepton-number-violating decays of $D_s^+\to h^-h^0e^+e^+$. Here, $h^-$ represents a $K^-$ or $\pi^-$, and $h^0$ represents a $\pi^0$, $K_S^0$ or $\phi$. No significant signal is observed, and the upper limits of their branching fractions at the 90\% confidence level are determined to be $\mathcal{B}(D_s^+\to \phi\pi^-e^+e^+) < 6.9 \times 10^{-5}$, $\mathcal{B}(D_s^+\to \phi K^-e^+e^+) < 9.9 \times 10^{-5}$, $\mathcal{B}(D_s^+\to K_S^0\pi^-e^+e^+) < 1.3 \times 10^{-5}$, $\mathcal{B}(D_s^+\to K_S^0K^-e^+e^+) < 2.9 \times 10^{-5}$, $\mathcal{B}(D_s^+\to \pi^-\pi^0e^+e^+) < 2.9 \times 10^{-5}$ and $\mathcal{B}(D_s^+\to K^-\pi^0e^+e^+) < 3.4 \times 10^{-5}$. The Majorana neutrino is searched for with different mass assumptions within the range [0.20, 0.80] GeV$/c^2$ in the decay of $D_s^+\to\phi e^+\nu_m$ with $\nu_m\to\pi^-e^+$, and the upper limits of the branching fractions at the 90\% confidence level are at the level of $10^{-5}-10^{-2}$, depending on the mass of the Majorana neutrino.

Published
2024

48. Search for $D^0\to K^-\eta e^+\nu_e$, $D^+\to K_S^0 \eta e^+\nu_e$ and $D^+\to \eta\eta e^+\nu_e$ decays

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

By analyzing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 7.93 fb$^{-1}$, collected at the center-of-mass energy of 3.773 GeV with the BESIII detector, we search for the semileptonic decays $D^0\to K^-\eta e^+\nu_e$, $D^+\to K_S^0 \eta e^+\nu_e$ and $D^+\to \eta\eta e^+\nu_e$ for the first time. We present evidence for $D^0\to K^-\eta e^+\nu_e$ with a significance of $3.3\sigma$. The branching fraction of $D^0\to K^-\eta e^+\nu_e$ is measured to be $(0.84_{-0.34}^{+0.29}\pm0.22)\times 10^{-4}$. Here, the first uncertainties are statistical and the second ones are systematic. No significant signals are observed for the decays $D^+\to K_S^0 \eta e^+\nu_e$ and $D^+\to \eta\eta e^+\nu_e$ and we set the upper limits on their branching fractions., Comment: 10 pages,4 figures

Published
2024

49. Measurements of the $CP$-even fractions of $D^0\to\pi^{+}\pi^{-}\pi^{0}$ and $D^0\to K^{+}K^{-}\pi^{0}$ at BESIII

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

The $CP$-even fractions ($F_{+}$) of the decays $D^0\to\pi^{+}\pi^{-}\pi^{0}$ and $D^0\to K^{+}K^{-}\pi^{0}$ are measured with a quantum-correlated $\psi(3770)\to D\bar{D}$ data sample collected by the BESIII experiment corresponding to an integrated luminosity of 7.93 $\mathrm{fb}^{-1}$. The results are $F_{+}^{\pi^{+}\pi^{-}\pi^{0}}=0.9406\pm0.0036\pm0.0021$ and $F_{+}^{K^{+}K^{-}\pi^{0}}=0.631\pm0.014\pm0.011$, where the first uncertainties are statistical and the second systematic. These measurements are consistent with the previous determinations, and the uncertainties for $F_{+}^{\pi^{+}\pi^{-}\pi^{0}}$ and $F_{+}^{K^{+}K^{-}\pi^{0}}$ are reduced by factors of 3.9 and 2.6, respectively. The reported results provide important inputs for the precise measurement of the angle $\gamma$ of the Cabibbo-Kobayashi-Maskawa matrix and indirect $CP$ violation in charm mixing., Comment: 19 pages, 8 figures

Published
2024

50. Study of the decay $D^0\rightarrow \rho(770)^-e^+\nu_e$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

We present a study of the semileptonic decay $D^0\rightarrow \pi^-\pi^0e^{+}\nu_{e}$ using an $e^+e^-$ annihilation data sample of $7.93~\mathrm{fb}^{-1}$ collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The branching fraction of $D^0\to \rho(770)^-e^+\nu_e$ is measured to be $(1.439 \pm 0.033(\rm stat.) \pm 0.027(\rm syst.)) \times10^{-3}$, which is a factor 1.6 more precise than previous measurements. By performing an amplitude analysis, we measure the hadronic form-factor ratios of $D^0\to \rho(770)^-e^+\nu_e$ at $q^2=0$ assuming the single-pole-dominance parametrization: $r_{V}=V(0)/A_1(0)=1.548\pm0.079(\rm stat.)\pm0.041(\rm syst.)$ and $r_{2}=A_2(0)/A_1(0)=0.823\pm0.056(\rm stat.)\pm0.026(\rm syst.)$., Comment: 12 pages, 3 figures

Published
2024

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