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Abstract Green manufacturing has become a necessary way to promote new industrialization and realize the high-quality development of China’s manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2022, this paper constructs a comprehensive evaluation index system for the green manufacturing development level and introduces the TOPSIS- Gray correlation method to comprehensively measure the green manufacturing development level of China as a whole and the four major regions in the eastern, central, western, and northeastern parts of the country. The regional differences, distribution dynamics and evolutionary trends of China's green manufacturing development level are also explored with the help of the Gini coefficient, kernel density estimation and Markov chain methods. Research Findings: (1) The green manufacturing development level in China is on an upward trend, with an overall spatial distribution pattern of “East is superior and West is inferior”. (2) There are regional differences in the green manufacturing development level in China, and the differences are widening, with interregional differences being the main reason for this overall difference. (3) The country as a whole, the central region and the western region are polarized to varying degrees, with the rest of the country showing an improvement in polarization. (4) Without considering spatial factors, the development of green manufacturing in each province experiences “club convergence” in the short term, and it is difficult to realize rapid development. Considering spatial factors, China's green manufacturing development level is generally characterized by “elevated in proximity to high levels and suppressed in proximity to low levels”, and in the long run, it shows a distribution trend toward the concentration of high values. The findings of this study can provide new ideas for promoting synergistic efficient development of green manufacturing in China.

Abstract To evaluate the predictive and prognostic value of fibroblast growth factor 21 (FGF21) levels in retinal artery occlusion (RAO) patients. In this case–control study, serum FGF21 levels were detected by using the ELISA method. Multivariable logistic regression analyses were performed to evaluate the significance of FGF21 in assessing the risk of developing RAO and its impact on vision and concurrent ischemic stroke. Compared with control group, serum FGF21 levels were significantly higher (median [IQR] = 230.90[167.40,332.20] pg/ml) in RAO patients. Multivariate logistic regression analysis showed that elevated serum FGF21 levels were associated with a higher risk of RAO occurrence (P = 0.025, OR [95%CI] = 9.672 [2.573, 36.359]) after adjustment for multiple confounding factors. Higher serum FGF21 levels were negatively associated with visual acuity improvement (P = 0.029, OR [95%CI] = 0.466[0.235, 0.925]) and positively correlated with concurrent ischemic stroke (P = 0.04, OR [95% CI] = 1.944[1.029, 3.672]) in RAO patients. Elevated serum FGF21 levels could promote the development of RAO and indicate worse visual prognosis and increase the risk of concurrent ischemic stroke, which might help clinicians early diagnose and treat RAO patients.

Abstract The Jishishan Ms 6.2 earthquake occurred at 23:59 on December 18, 2023 in Gansu Province, China. We conducted a field survey to assess the hazards and damages caused by the earthquake and its associated geo-activities. Subsequently, we organized a seminar to discuss the possible causes of the destruction of a prehistoric site—Lajia Settlement—dated back to four thousand years B.P. and located only several kilometers away from the epicenter of the Jishishan earthquake. The Jishishan earthquake was unique for its hazard and disaster process, which featured ground shaking and a series of complex geological and geomorphological activities: sediment and soil spray piles, liquefaction, collapse, landslide, and mudflow along water channels. We define this phenomenon as the Jishishan earthquake ripple hazard (JERH). The most recent evidence from the JERH suggests that a prehistoric earthquake similar to the JERH, instead of riverine floods or earthquake-induced landslide dam outburst flood, as previously hypothesized, destroyed the Lajia Settlement.

Abstract The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

Abstract Pou6f2 is a genetic connection between central corneal thickness (CCT) in the mouse and a risk factor for developing primary open-angle glaucoma. POU6F2 is also a risk factor for several conditions in humans, including glaucoma, myopia, and dyslexia. Recent findings demonstrate that POU6F2-positive retinal ganglion cells (RGCs) comprise a number of RGC subtypes in the mouse, some of which also co-stain for Cdh6 and Hoxd10. These POU6F2-positive RGCs appear to be novel of ON–OFF directionally selective ganglion cells (ooDSGCs) that do not co-stain with CART or SATB2 (typical ooDSGCs markers). These POU6F2-positive cells are sensitive to damage caused by elevated intraocular pressure. In the DBA/2J mouse glaucoma model, heavily-labeled POU6F2 RGCs decrease by 73% at 8 months of age compared to only 22% loss of total RGCs (labeled with RBPMS). Additionally, Pou6f2 −/− mice suffer a significant loss of acuity and spatial contrast sensitivity along with an 11.4% loss of total RGCs. In the rhesus macaque retina, POU6F2 labels the large parasol ganglion cells that form the magnocellular (M) pathway. The association of POU6F2 with the M-pathway may reveal in part its role in human glaucoma, myopia, and dyslexia.

Abstract The number of patents increases quickly, while more and more low-quality patents are emerging. It’s important to identify high-quality patents from massive data quickly and accurately for organizational R&D decision-making and patent layout. However, due to low percentage of high-quality patents, it is challenging to identify them efficiently. In order to solve above problem, we reconstruct the existing index system for identifying high-quality patents by adding 4 features from technological strength of patentees. Furthermore, we propose an improved model by integrating resampling technique and ensemble learning algorithm. First, generative adversarial networks (GAN) are used to expand minority samples. Second, Extreme Gradient Boosting algorithm (XGBoost) with Bayesian optimization (BO) is used to identify high-quality patents. For clarity, this model is called a GAN-BO-XGBoost model. To test the effectiveness of above model, we use patent data in field of lithography technology. Tenfold cross-validation is carried out to evaluate the performance between our proposed model and other models. The results show that GAN-BO-XGBoost model performs better and it’s more stable than other models.

Abstract We aimed to identify different trajectories of remnant cholesterol (RC) and investigate the association of RC trajectories with vascular endothelial function and atherosclerosis progression in a longitudinal cohort of the Chinese population. A total of 521 participants were included in the flow-mediated vasodilation (FMD) subcohort study, and 7775 participants were included in the brachial-ankle pulse wave velocity (baPWV) subcohort study. All participants had ≥ 3 medical examinations during the 10-year follow-up period. In the FMD subcohort study, three distinct RC trajectories were identified according to the RC range and changing pattern over time: “low” (57.58%), “moderate” (30.90%) and “high” (11.52%). The proportion of the three groups with vascular endothelial dysfunction (FMD 1400 cm/s) was 38.79%, 51.26% and 59.01% respectively. Taking the low group as a reference, participants in the moderate and high groups had over 1.46 and 2.16 times the odds of atherosclerosis (P

Abstract Homozygous familial hypercholesterolemia (HoFH) is an underdiagnosed and undertreated ultra-rare disease. We utilized claims data from the Komodo Healthcare Map database to develop a machine-learning model to identify potential HoFH patients. We tokenized patients enrolled in MyRARE (patient support program for those prescribed evinacumab-dgnb in the United States) and linked them with their Komodo claims. A true positive HoFH cohort (n = 331) was formed by including patients from MyRARE and patients with prescriptions for evinacumab-dgnb or lomitapide. The negative cohort (n = 1423) comprised patients with or at risk for cardiovascular disease. We divided the cohort into an 80% training and 20% testing set. Overall, 10,616 candidate features were investigated; 87 were selected due to clinical relevance and importance on prediction performance. Different machine-learning algorithms were explored, with fast interpretable greedy-tree sums selected as the final machine-learning tool. This selection was based on its satisfactory performance and its easily interpretable nature. The model identified four useful features and yielded precision (positive predicted value) of 0.98, recall (sensitivity) of 0.88, area under the receiver operating characteristic curve of 0.98, and accuracy of 0.97. The model performed well in identifying HoFH patients in the testing set, providing a useful tool to facilitate HoFH screening and diagnosis via healthcare claims data.

Abstract Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I–IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model’s clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance.

Abstract Central retinal artery occlusion (CRAO) is an acute retinal ischaemic disease, but early diagnosis is challenging due to a lack of biomarkers. Blood samples were collected from CRAO patients and cataract patients. Gene expression profiles were distinct between arterial/venous CRAO blood (A–V group) and venous CRAO/control blood (V–C group) samples. Differentially expressed genes (DEGs) were subjected to GO and KEGG enrichment analyses. Hub genes were identified by Cytoscape and used to predict gene interactions via GeneMANIA. Immune cell infiltration was analysed by CIBERSORT. More than 1400 DEGs were identified in the A–V group and 112 DEGs in the V–C group compared to controls. The DEGs in both groups were enriched in the ribosome pathway, and those in the V–C group were also enriched in antigen processing/MHC pathways. Network analysis identified ribosomal proteins (RPS2 and RPS5) as the core genes of the A–V group and MHC genes (HLA-F) as the core genes of the V–C group. Coexpression networks showed ribosomal involvement in both groups, with additional immune responses in the V–C group. Immune cell analysis indicated increased numbers of neutrophils and T cells. Ribosomal and MHC-related genes were identified as potential CRAO biomarkers, providing research directions for prevention, diagnosis, treatment and prognosis.

Abstract Blood transfusions from convalescent Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected patients could be used to treat patients with severe infections or immunocompromised patients. However, it is necessary to select the optimal donors to maximize the utilization of resources. In this study, we investigated the associations among body mass index (BMI), tobacco smoking, exercise frequency and duration, and alcohol consumption with the SARS-CoV-2 immunoglobulin-G (IgG) antibody titer levels with in the Chinese convalescent blood donor population. Here we show that BMI, smoking habits, and exercise frequency appear to be predictive factors for IgG levels in convalescent male blood donors. However, these variables were not observed as predictive of IgG levels in female convalescent blood donors. The findings could be used to optimize the screening for potential blood donors to treat immunocompromised or severely ill COVID-19 patients.

Abstract We performed this cohort study to investigate whether the myocardial bridge (MB) affects the fat attenuation index (FAI) and to determine the optimal cardiac phase to measure the volume and the FAI of pericoronary adipose tissue (PCAT). The data of 300 patients who were diagnosed with MB of the left anterior descending (LAD) coronary artery were retrospectively analyzed. All of patients were divided into the MB group and the MB with atherosclerosis group. In addition, 104 patients with negative CCTA results were enrolled as the control group. There was no significant difference between FAI values measured in systole and diastole (P > 0.05). There was no significant difference in FAI among the MB group, the MB with atherosclerosis group, and the control group (P > 0.05). In MB with atherosclerosis group, LAD stenosis degree (

Abstract Global greenhouse gas emission, major factor driving climate change, has been increasing since nineteenth century. STIRPAT and CEVSA models were performed to estimate the carbon emission peaks and terrestrial ecosystem carbon sinks at the provincial level in China, respectively. Utilizing the growth characteristics and the peak time criteria for the period 1997–2019, the patterns of energy consumption and CO2 emissions from 30 Chinese provinces are categorized into four groups: (i) one-stage increase (5 provinces), (ii) two-stage increase (10 provinces), (iii) maximum around 2013 (13 provinces), and (iv) maximum around 2017 (2 provinces). According to the STIRPAT model, the anticipated time of peak CO2 emissions for Beijing from the third group is ~ 2025 in both business-as-usual and high-speed scenarios. For Xinjiang Uygur autonomous region from the first group and Zhejiang province from the second group, the expected peak time is 2025 to 2030. Shaanxi province from the fourth group is likely to reach carbon emission peak before 2030. The inventory-based estimate of China’s terrestrial carbon sink is ~ 266.2 Tg C/a during the period 1982–2015, offsetting 18.3% of contemporary CO2 emissions. The province-level CO2 emissions, peak emissions and terrestrial carbon sinks estimates presented here are significant for those concerned with carbon neutrality.

Abstract In head and neck squamous cell carcinoma (HNSC), chemoresistance is a major reason for poor prognosis. Nevertheless, there is a lack of validated biomarkers to screen for patients for categorical chemotherapy. Fc gamma binding protein (FCGBP) is a mucus protein associated with mucosal epithelial cells and has immunological functions that protect against tumors and metastasis. However, the effect of FCGBP on HNSC is unclear. In pan-cancer tissues, the expression of FCGBP and the survival status of patients were analyzed using information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Correlation analysis and Cox regression analysis were conducted to confirm the relationship and survival outcome. Bioinformatics analysis was utilized to predict the probable upstream non-coding RNA. FCGBP functioned as a potential tumor suppressor gene in HNSC. Notably, FCGBP expression was negatively correlated with enriched tumor-infiltrating macrophages and paclitaxel resistance. Cox regression with gene, clinical, and immune factors showed that FCGBP was a risk factor acting in an independent manner. In HNSC, the utmost possibly upstream non-coding RNA-related pathway of FCGBP was also discovered to be the PART1/AC007728.2/LINC00885/hsa-miR-877-5p/FCGBP axis. According to the present study, non-coding RNA-related low levels of FCGBP are a prognostic indicator and are linked to an HNSC-related immunosuppressive state.

Abstract High altitude exposure increases the risk of myocardial ischemia (MI) and subsequent cardiovascular death. Machine learning techniques have been used to develop cardiovascular disease prediction models, but no reports exist for high altitude induced myocardial ischemia. Our objective was to establish a machine learning-based MI prediction model and identify key risk factors. Using a prospective cohort study, a predictive model was developed and validated for high-altitude MI. We consolidated the health examination and self-reported electronic questionnaire data (collected between January and June 2022 in 920th Joint Logistic Support Force Hospital of china) of soldiers undergoing high-altitude training, along with the health examination and second self-reported electronic questionnaire data (collected between December 2022 and January 2023) subsequent to their completion on the plateau, into a unified dataset. Participants were subsequently allocated to either the training or test dataset in a 3:1 ratio using random assignment. A predictive model based on clinical features, physical examination, and laboratory results was designed using the training dataset, and the model's performance was evaluated using the area under the receiver operating characteristic curve score (AUC) in the test dataset. Using the training dataset (n = 2141), we developed a myocardial ischemia prediction model with high accuracy (AUC = 0.86) when validated on the test dataset (n = 714). The model was based on five laboratory results: Eosinophils percentage (Eos.Per), Globulin (G), Ca, Glucose (GLU), and Aspartate aminotransferase (AST). Our concise and accurate high-altitude myocardial ischemia incidence prediction model, based on five laboratory results, may be used to identify risks in advance and help individuals and groups prepare before entering high-altitude areas. Further external validation, including female and different age groups, is necessary.

Abstract Cerebral ischaemia/reperfusion (I/R) injury induces irreversible brain injury and causes functional impairment. Ubiquitination plays a crucial role in protein degradation, but its role in cerebral I/R injury remains unclear. Differentially expressed genes in stroke were identified by analysing the microarray dataset GSE119121. Cerebral I/R was simulated in vitro by treating human microglial HMC3 cells with oxygen–glucose deprivation/reperfusion (OGD/R). Cell viability was tested by Cell Counting Kit 8 (CCK-8) assays, and pyroptosis was examined by flow cytometry. Lactate dehydrogenase (LDH) and inflammatory cytokine secretion were measured by LDH cytotoxicity assays and enzyme-linked immunosorbent assay (ELISA), respectively. The cerebral I/R animal model was established by middle cerebral artery occlusion (MCAO) surgery in rats. Bioinformatic analysis indicated that tripartite motif-containing protein 59 (TRIM59) is downregulated in stroke, which was verified in cerebral I/R models. The upregulation of TRIM59 promoted viability and inhibited pyroptosis in OGD/R-treated microglia and alleviated cerebral I/R injury in vivo. TRIM59 attenuated NOD-like receptor family pyrin domain containing 3 (NLRP3) protein expression through ubiquitination, thus degrading NLRP3 and alleviating OGD/R-induced injury. TRIM59 relieves cerebral I/R injury in vivo and in vivo. Mechanistically, TRIM59 directly interacts with NLRP3 and inhibits NLRP3 through ubiquitination. Targeting the TRIM59/NLRP3 signalling axis may be an effective therapeutic strategy for cerebral I/R.

Abstract To evaluate the changes of dry eye parameters after small incision lenticule extraction (SMILE) surgery in patients with different ocular surface disease index (OSDI) scores. Prospective research. Participants were divided into two groups: Group A, OSDI

Abstract We compare how humans retell stories to how ChatGPT retells stories in chains of three retellings by different people or different accounts on ChatGPT. ChatGPT provides competent summaries of the original narrative texts in one step of retelling. In subsequent retellings few additional changes occur. Human retellers, by contrast, reduce the original text incrementally and by creating 55–60% of novel words and concepts (synsets) at each iteration. The retellings by both ChatGPT and humans show very stable emotion ratings, which is a puzzle for human retellers given the high degree of novel inventions across retellings. ChatGPT maintains more nouns, adjectives, and prepositions and also uses language later acquired in life, while humans use more verbs, adverbs, and negations and use language acquired at a younger age. The results reveal that spontaneous retelling by humans involves ongoing creativity, anchored by emotions, beyond the default probabilistic wording of large language models such as ChatGPT.

Abstract This work deals with the fabrication of one low density steel by mixing AISI S2 tool steel and AlSi10Mg powders using powder-based directed energy deposition (P-DED) technique. Two approaches of mixing powders were compared-continuous mixing during the process (in-situ) and mixing the powder prior to the process (premixed). The P-DED sample was characterised by a variety of techniques such as optical microscopy, scanning electron microscopy, electron backscatter diffraction, X-ray diffraction, and hardness measurement. Our findings demonstrate the successful achievement of steel with a 8 wt. % AlSi10Mg addition when two dissimilar powders are premixed, resulting in approximately 12% reduction in the density of S2 steel. Optimizing the powder feed rate and the ratio of AlSi10Mg powder contribute to an improvement of printability, eliminating materials separation, leading to a homogenous deposited part. Compared to the in-situ mixing case, the premixed process within the current process window generates a more homogeneous microstructure consisting of three phases: Ferrite, Fe3Al and Fe3AlC carbide. Whereas, the in-situ sample exhibits only two phases Ferrite and Fe3Al. The hardness of the premixed sample is found to be slightly higher compared to the in-situ sample.

Abstract Antrodia cinnamomea (AC) is a treasured Asian medicinal mushroom, which has attracted attention due to recent research on its effectiveness in targeting a variety of serious ailments such as cancer and liver diseases. Among different A. cinnamomea constituents, triterpenoids are regarded as the most therapeutically attractive components because of their anti-inflammatory and cytotoxic activities. In the present study, we proposed a mathematical and statistical extraction protocol to evaluate the concentrations of total ergostane and lanostane triterpenoid derivatives from the ethanolic extract of the wild fruiting bodies of A. cinnamomea (EEAC) by utilizing response surface methodology (RSM) and quantitative NMR (qNMR) approaches. The optimum response surface model showed that the variations of the investigated response variables reached more than 90%, suggesting that the developed model is accurate in explaining response variability. Furthermore, the EEAC major characteristic triterpenoids were quantified through the comparison of the HPLC-tandem MS results with those of the qNMR results. The precision of the used techniques was also evaluated. The experimental design of the EEAC optimum extraction procedure obtained by using RSM and qNMR enabled accurate characterization and quantitation of A. cinnamomea triterpenoids.

Abstract To evaluate the possibility of judging the degree of bone healing by wall thickness analysis provide reference for quantitative analysis of bone healing. Patients with lower limb fracture from April 2014 to October 2019 were recruited and divided into bone healing (group A), poor bone healing (group B), and nonunion (group C). Models were built in Mimics 20.0 with DICOM 3.0 data obtained from patient’s CT. Three-dimensional geometric models of unaffected limb and affected limb after simulated removal of internal fixation were established, corresponding to basic phase and simulated phase, respectively. Wall thickness analysis was performed to obtain median wall thickness after meshing. R2 (median wall thickness ratio), R4 (CT value ratio), and R5 (healing index ratio) were obtained by calculating the ratio of each value in simulated phase to that in basic phase. Receiver operating characteristic curve analysis was used to evaluate the ability of Wall Thickness Analysis to indicate fracture healing. 112 CT scans of 79 patients were included in the study. The frequency of categorization in groups A, B, and C was 49, 37 and 26, respectively. The median R2 in groups A, B, and C was 0.91, 0.80, and 0.67, respectively (group A > group B > group C, all P

Abstract To estimate the association between central retinal artery occlusion (CRAO) and major adverse cardiovascular and cerebrovascular events (MACCE), including their clinical characteristics, blood markers, and the contribution of CRAO to MACCE, as well as to assess any sex differences. This retrospective cohort study included continuous new-onset CRAO patients and 1:4 controls during the same period. Correlations of CRAO with the incidence of MACCE during follow-up and the sex-related differences were studied. One hundred and twenty-four CRAO patients and four hundred and ninety-six controls were enrolled. Neutrophil-to-lymphocyte ratio (NLR, P = 0.014) and high-sensitivity C-reactive protein (hs-CRP, P = 0.038) were tended to be higher in CRAO patients. After the follow-up period, 78 patients experienced MACCE. Multivariate Cox regression analysis showed that CRAO was a predictor of the occurrence of MACCE (HR 2.321, 95% CI 1.439–3.744, P = 0.001). Sex subgroups indicated that age, diabetes, current smoking, CRAO, NLR and hs-CRP increased the risk factor of MACCE in males (All P

Abstract Numerous studies have evaluated the use of single nucleotide polymorphism array (SNP-array) in prenatal diagnostics, but very few have evaluated its application under different risk conditions. Here, SNP-array was used for the retrospective analysis of 8386 pregnancies and the cases were categorized into seven groups. Pathogenic copy number variations (pCNVs) were found in 699 (8.3%, 699/8386) cases. Among the seven different risk factor groups, the non-invasive prenatal testing-positive group had the highest pCNVs rate (35.3%), followed by the abnormal ultrasound structure group (12.8%), and then the chromosomal abnormalities in the couples group (9.5%). Notably the adverse pregnancy history group presented with the lowest pCNVs rate (2.8%). Further evaluation of the 1495 cases with ultrasound abnormalities revealed that the highest pCNV rates were recorded in those cases with multiple system structure abnormalities (22.6%), followed by the groups with skeletal system (11.6%) and urinary system abnormalities (11.2%). A total of 3424 fetuses with ultrasonic soft markers were classified as having one, two, or three ultrasonic soft markers. The different pCNV rates in the three groups were statistically significant. There was little correlation between pCNVs and a previous history of adverse pregnancy outcomes, suggesting that genetic screening under these conditions should be evaluated on a case-by-case basis.

Abstract The relationship between serum creatinine and type 2 diabetes is limited. We aimed to investigate the association of baseline serum creatinine and new-onset type 2 diabetes in Chinese population. This retrospective cohort study was conducted using data from the health screening program in China. The population were divided into four groups based on serum creatinine levels, and the outcome of interest was the occurrence of a diabetic event. Cox proportional risk model was used to assess the independent effect of baseline serum creatinine level on future diabetes risk. Sensitivity and subgroup analysis were used to verify the reliability of the results. After an average follow-up of 3.12 years, among 201,298 individuals aged ≥ 20 years, 3389 patients developed diabetes. Compared with participants in quartile 2–4 (> 51.6umol/L for female, > 71.8umol/L for male,), a significantly higher risk of new-onset Type 2 Diabetes (OR, 1.15; 95%CI: 1.07–1.23) was found in those in quartile 1 (

Abstract It is difficult to accurately estimate the incidence rate of intimate partner violence (IPV) using traditional social survey methods because IPV victims are often reluctant to disclose their experiences, leading to an underestimation of the incidence rate. To address this issue, we applied machine learning algorithms to predict the incidence rate of IPV in China based on data from the Third Wave Survey on the Social Status of Women in China (TWSSSCW 2010). Specifically, we examined five unbalanced sample-processing methods and six machine learning algorithms, choosing the random under-sampling ensemble method and the random forest algorithm to impute the missing data. Analysis of the complete data showed that the incidence rates of physical violence, verbal violence, and cold violence were 7.10%, 13.74%, and 21.35%, respectively, which were higher than the incidence rates in the original dataset (4.05%, 11.21%, and 17.95%, respectively). The robustness of our findings was further confirmed by analysis using different training sets. Overall, this study demonstrates that better tools need to be developed to accurately estimate the incidence rates of IPV. It also serves as a useful guide for future research that imputes missing data using machine learning.

Abstract The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people’s lives, reduce pollution control costs and improve the quality of the environment. In this paper, we constructed a combined prediction model based on real hourly AQI data in Beijing. First, we used singular spectrum analysis (SSA) to decompose the AQI data into different sequences, such as trend, oscillation component and noise. Then, bidirectional long short-term memory (BiLSTM) was introduced to predict the decomposed AQI data, and a light gradient boosting machine (LightGBM) was used to integrate the predicted results. The experimental results show that the prediction effect of SSA-BiLSTM-LightGBM for the AQI data set is good on the test set. The root mean squared error (RMSE) reaches 0.6897, the mean absolute error (MAE) reaches 0.4718, the symmetric mean absolute percentage error (SMAPE) reaches 1.2712%, and the adjusted R2 reaches 0.9995.

Abstract The growth of endophytic bacteria is influenced by the host plants and their secondary metabolites and activities. In this study, P. megaterium P-NA14 and P. megaterium D-HT207 were isolated from potato tuber and dendrobium stem respectively. They were both identified as Priestia megaterium. The antimicrobial activities and metabolites of both strains were explored. For antimicrobial activities, results showed that P. megaterium P-NA14 exhibited a stronger inhibition effect on the pathogen of dendrobium, while P. megaterium D-HT207 exhibited a stronger inhibition effect on the pathogen of potato. The supernatant of P. megaterium P-NA14 showed an inhibition effect only on Staphylococcus aureus, while the sediment of P. megaterium D-HT207 showed an inhibition effect only on Escherichia coli. For metabolomic analysis, the content of L-phenylalanine in P. megaterium P-NA14 was higher than that of P. megaterium D-HT207, and several key downstream metabolites of L-phenylalanine were associated with inhibition of S. aureus including tyrosine, capsaicin, etc. Therefore, we speculated that the different antimicrobial activities between P. megaterium P-NA14 and P. megaterium D-HT207 were possibly related to the content of L-phenylalanine and its metabolites. This study preliminarily explored why the same strains isolated from different hosts exhibit different activities from the perspective of metabolomics.

Abstract Antimicrobial resistance, especially carbapenem resistance, poses a serious threat to global public health. Here, a carbapenem-resistant Comamonas aquatica isolate SCLZS63 was recovered from hospital sewage. Whole-genome sequencing showed that SCLZS63 has a 4,048,791-bp circular chromosome and three plasmids. The carbapenemase gene bla AFM-1 is located on the 143,067-bp untypable plasmid p1_SCLZS63, which is a novel type of plasmid with two multidrug-resistant (MDR) regions. Notably, a novel class A serine β-lactamase gene, bla CAE-1, coexists with bla AFM-1 in the mosaic MDR2 region. Cloning assay showed that CAE-1 confers resistance to ampicillin, piperacillin, cefazolin, cefuroxime, and ceftriaxone, and elevates the MIC of ampicillin-sulbactam two-fold in Escherichia coli DH5α, suggesting that CAE-1 functions as a broad-spectrum β-lactamase. Amino acid sequences analysis suggested that bla CAE-1 may originate from Comamonadaceae. The bla AFM-1 in p1_SCLZS63 is located in a conserved structure of ISCR29-ΔgroL-bla AFM-1-ble-ΔtrpF-ΔISCR27-msrB-msrA-yfcG-corA. Comprehensive analysis of the bla AFM-bearing sequences revealed important roles of ISCR29 and ΔISCR27 in the mobilization and truncation of the core module of bla AFM alleles, respectively. The diverse passenger contents of class 1 integrons flanking the bla AFM core module make the complexity of genetic contexts for bla AFM. In conclusion, this study reveals that Comamonas may act as an important reservoir for antibiotics-resistance genes and plasmids in the environment. Continuous monitoring for the environmental emergence of antimicrobial-resistant bacteria is needed to control the spread of antimicrobial resistance.

Abstract To determine whether photobiomodulation (PBM) therapy can retard ocular axial length (AL) in children with myopia. A randomized controlled clinical trial was conducted on two consecutive cohorts of 50 eligible children aged 8–12 years with ≤ − 0.75 Diopter (D) of spherical equivalent refraction (SER). Participants were randomly assigned to the intervention group (n = 25) and treated with PBM therapy or the control group (n = 25) and treated with single vision spectacles only. At the 12-month follow-up, the changes in AL and cycloplegic SER from baseline were both compared between the two groups. In addition, the subfoveal choroidal thickness (SFChT), anterior chamber depth (ACD), and central corneal refractive power (CCP) were analysed at the 3-, 6-, 9-, and 12-month follow-ups, respectively. Among the 50 children, 78% were included at the final follow-up, with a mean age of 9.7 ± 1.5 years and a mean SER of − 2.56 ± 1.70. The mean difference in AL growth between the two groups at 12 months was 0.50 mm (PBM vs. Control, − 0.02 mm ± 0.11 vs. 0.48 mm ± 0.16, P

Abstract The ecology of soil fungi is poorly understood, and recent comprehensive reports on Trichoderma are unavailable for any region, including the Zoige alpine wetland ecological region in China. One hundred soil samples were collected from different soil types and soil layers in Zoige alpine wetland ecological regions. Using the traditional suspension plating method, 80 Trichoderma strains were chosen to analyze species diversity. After a preliminary classification of morphological characteristics and the genes glyceraldehyde-3-phosphate dehydrogenase (gpd), 57 representative strains were selected and eventually identified as seven species via phylogenetic analyses of multilocus sequences based on the genes transcription elongation factor 1 alpha (tef1), encoding RNA polymerase II subunit B (rpb2) and ATP citrate lyase (acl1). Among them, T. harzianum was the dominant species isolated from five soil layers and four soil types, and had the highest isolation frequency (23%) in this zone, while T. polysporum and T. pyramidale were rare species, with isolation frequencies of less than 1%. Our detailed morphological observation and molecular phylogenetic analyses support the recognition of Trichoderma zoigense was described for the first time as a new species, while T. atrobrunneum as a new record for China was found. Our results will be used as a reference for a greater understanding of soil microbial resources, ecological rehabilitation and reconstructions in the Zoige alpine wetland.

Abstract Mitochondrial metabolism disorders play an important role in the occurrence and development of tumors, and iron–sulfur protein is an important molecule for maintaining the normal function of mitochondria. However, the relationship between the expression, prognostic value, and immune infiltration of MMDS-related iron–sulfur protein genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Based on online databases bioinformatics analysis was performed to evaluate the expression differences, survival impacts, immune infiltration, and prognostic significance of multiple mitochondrial dysfunction syndrome (MMDS)-related iron–sulfur protein genes in KIRC patients. For example, the protein–protein interaction (PPI) network was constructed using STRING and GEPIA database; Survival impacts were constructed by TCGA database; Immune infiltration was analyzed using TIMER database. There were significant differences in the mRNA expression levels of ISCA1, ISCA2, C1ORF69 and NFU1 in KIRC among different tumor grades and individual cancer stages. Furthermore, KIRC with high transcription levels of ISCA1, ISCA2, C1ORF69 and NFU1 (p

Abstract Cinnamon aqueous extract’s active substance base remains unclear and its mechanisms, mainly the therapeutic target of anti-Alzheimer’s disease (AD)-related GABAergic synaptic dysfunction, remain unclear. Here, 30 chemical components were identified in the aqueous extract of cinnamon using LC/MS; secondly, we explored the brain-targeting components of the aqueous extract of cinnamon, and 17 components had a good absorption due to the blood–brain barrier (BBB) limitation; thirdly, further clustering analysis of active ingredient targets by network pharmacology showed that the GABA pathway with GABRG2 as the core target was significantly enriched; then, we used prominent protein–protein interactions (PPI), relying on a protein-metabolite network, and identified the GABRA1, GABRB2 and GABRA5 as the closest targets to GABRG2; finally, the affinity between the target and its cognate active compound was predicted by molecular docking. In general, we screened five components, methyl cinnamate, propyl cinnamate, ( +)-procyanidin B2, procyanidin B1, and myristicin as the brain synapse-targeting active substances of cinnamon using a systematic strategy, and identified GABRA1, GABRB2, GABRA5 and GABRG2 as core therapeutic targets of cinnamon against Alzheimer's disease-related GABAergic synaptic dysfunction. Exploring the mechanism of cinnamon’ activities through multi-components and multiple targets strategies promise to reduce the threat of single- target and symptom-based drug discovery failure.

Abstract Karst rocky desertification (KRD) has become one of the most serious ecological and environmental problems in karst areas. At present, mapping KRD with a high accuracy and on a large scale is still a difficult problem in the control of KRD. In this study, a random forest (RF) based on maximum information coefficient and correlation coefficient feature selection is proposed to predict KRD. Nine predictors stood out as feature factors to estimate KRD. Rock exposure was the most important predictor, followed by fractional vegetation cover for the prediction of KRD processes. The kappa and classification accuracy indexes were to evaluate the performance of the model. We recorded overall accuracy rate and kappa index values of 94.7% and 0.92 for the testing datasets respectively. The RF model was then used to predict the KRD in 2001, 2011, 2016, and 2020, and it was found that the KRD in the study area has exhibited a positive trend of improvement. Therefore, the use of multisource remote sensing data combined with the RF model can obtain better prediction results of KRD, thereby providing a new idea for large-scale estimation of the KRD in peak-cluster depression.

Abstract Rapid and accurate detection of a C-shaped root canal on mandibular second molars can assist dentists in diagnosis and treatment. Oral panoramic radiography is one of the most effective methods of determining the root canal of teeth. There are already some traditional methods based on deep learning to learn the characteristics of C-shaped root canal tooth images. However, previous studies have shown that the accuracy of detecting the C-shaped root canal still needs to be improved. And it is not suitable for implementing these network structures with limited hardware resources. In this paper, a new lightweight convolutional neural network is designed, which combined with receptive field block (RFB) for optimizing feature extraction. In order to optimize the hardware resource requirements of the model, a lightweight, multi-branch, convolutional neural network model was developed in this study. To improve the feature extraction ability of the model for C-shaped root canal tooth images, RFB has been merged with this model. RFB has achieved excellent results in target detection and classification. In the multiscale receptive field block, some small convolution kernels are used to replace the large convolution kernels, which allows the model to extract detailed features and reduce the computational complexity. Finally, the accuracy and area under receiver operating characteristics curve (AUC) values of C-shaped root canals on the image data of our mandibular second molars were 0.9838 and 0.996, respectively. The results show that the deep learning model proposed in this paper is more accurate and has lower computational complexity than many other similar studies. In addition, score-weighted class activation maps (Score-CAM) were generated to localize the internal structure that contributed to the predictions.

Abstract Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. While clinical trials demonstrated benefits of ICIs over chemotherapy, variation in outcomes across patients has been observed and trial populations may not be representative of clinical practice. Predictive models can help understand heterogeneity of treatment effects, identify predictors of meaningful clinical outcomes, and may inform treatment decisions. We applied machine learning (ML)-based survival models to a real-world cohort of patients with aNSCLC who received 1L ICI therapy extracted from a US-based electronic health record database. Model performance was evaluated using metrics including concordance index (c-index), and we used explainability techniques to identify significant predictors of overall survival (OS) and progression-free survival (PFS). The ML model achieved c-indices of 0.672 and 0.612 for OS and PFS, respectively, and Kaplan–Meier survival curves showed significant differences between low- and high-risk groups for OS and PFS (both log-rank test p

Abstract Airplanes have always been one of the first choices for people to travel because of their convenience and safety. However, due to the outbreak of the new coronavirus epidemic in 2020, the civil aviation industry of various countries in the world has encountered severe challenges. Predicting aircraft passenger satisfaction and excavating the main influencing factors can help airlines improve their services and gain advantages in difficult situations and competition. This paper proposes a RF-RFE-Logistic feature selection model to extract the influencing factors of passenger satisfaction. First, preliminary feature selection is performed using recursive feature elimination based on random forest (RF-RFE). Second, based on different classification models, KNN, logistic regression, random forest, Gaussian Naive Bayes, and BP neural network, the classification performance of the models before and after feature selection is compared, and the prediction model with the best classification performance is selected. Finally, based on the RF-RFE feature selection, combined with the logistic model, the factors affecting customer satisfaction are further extracted. The experimental results show that the RF-RFE model selects a feature subset containing 17 variables. In the classification prediction model, the random forest after RF-RFE feature selection shows the best classification performance. Finally, combined with the four important variables extracted by RF-RFE and logistic regression, further discussion is carried out, and suggestions are given for airlines to improve passenger satisfaction.

Abstract In countries with a high incidence of tuberculosis, the typical clinical features of Crohn's disease (CD) may be covered up after tuberculosis infection, and the identification of atypical Crohn's disease and intestinal tuberculosis (ITB) is still a dilemma for clinicians. Least absolute shrinkage and selection operator (LASSO) regression has been applied to select variables in disease diagnosis. However, its value in discriminating ITB and atypical Crohn's disease remains unknown. A total of 400 patients were enrolled from January 2014 to January 2019 in second Xiangya hospital Central South University.Among them, 57 indicators including clinical manifestations, laboratory results, endoscopic findings, computed tomography enterography features were collected for further analysis. R software version 3.6.1 (glmnet package) was used to perform the LASSO logistic regression analysis. SPSS 20.0 was used to perform Pearson chi-square test and binary logistic regression analysis. In the variable selection step, LASSO regression and Pearson chi-square test were applied to select the most valuable variables as candidates for further logistic regression analysis. Secondly, variables identified from step 1 were applied to construct binary logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed on these models to assess the ability and the optimal cutoff value for diagnosis. The area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy rate, together with their 95% confidence and intervals (CIs) were calculated. MedCalc software (Version 16.8) was applied to analyze the ROC curves of models. 332 patients were eventually enrolled to build a binary logistic regression model to discriminate CD (including comprehensive CD and tuberculosis infected CD) and ITB. However, we did not get a satisfactory diagnostic value via applying the binary logistic regression model of comprehensive CD and ITB to predict tuberculosis infected CD and ITB (accuracy rate:79.2%VS 65.1%). Therefore, we further established a binary logistic regression model to discriminate atypical CD from ITB, based on Pearsonchi-square test (model1) and LASSO regression (model 2). Model 1 showed 89.9% specificity, 65.9% sensitivity, 88.5% PPV, 68.9% NPV, 76.9% diagnostic accuracy, and an AUC value of 0.811, and model 2 showed 80.6% specificity, 84.4% sensitivity, 82.3% PPV, 82.9% NPV, 82.6% diagnostic accuracy, and an AUC value of 0.887. The comparison of AUCs between model1 and model2 was statistically different (P

Abstract Tongue strengthening exercise (TSE) has been proposed as an intervention to increase tongue strength and improve swallowing. However, clinical evidence of its effectiveness is lacking. In this review, seven databases were searched from inception to September 30, 2021 for randomized controlled trials that compared tongue strengths between the TSE intervention and control groups, obtained from maximal tongue elevation peak force in kilopascals (kPa). The Cochrane risk of bias tool was used for quality assessment. In total, 12 studies with 388 participants were included. The pooled meta-analysis demonstrated that the anterior tongue strength (ATS) (MD = 5.34 kPa; 95% CI 3.28–7.40; I 2 = 71%) and posterior tongue strength (MD = 8.12; 95% CI 3.45–12.79; I 2 = 90%) were significantly higher in the TSE intervention than that in the control group. Among healthy participants, subgroup analysis showed that TSE had improvements on ATS in all age groups, with the greatest improvement in old people (≥ 65 years) (MD = 8.01; 95% CI 4.39–11.64; I 2 = 30%). Meta-regression analysis revealed a nonsignificant trend toward greater improvement on tongue strength with increasing TSE duration. This study provides positive evidence that TSE may be beneficial in improving tongue strength and could be applied for adults, especially healthy older adults.

Abstract Ectopic ceramide accumulation in insulin-responsive tissues contributes to the development of obesity and impairs insulin sensitivity. Moreover, pharmacological inhibition of serine palmitoyl transferase (SPT), the first enzyme essential for ceramide biosynthesis using myriocin in rodents reduces body weight and improves insulin sensitivity and associated metabolic indices. Myriocin was originally extracted from fruiting bodies of the fungus Isaria sinclairii and has been found abundant in a number of closely related fungal species such as the Cordyceps. Myriocin is not approved for human use but extracts from Cordyceps are routinely consumed as part of traditional Chinese medication for the treatment of numerous diseases including diabetes. Herein, we screened commercially available extracts of Cordyceps currently being consumed by humans, to identify Cordyceps containing myriocin and test the efficacy of Cordyceps extract containing myriocin in obese mice to improve energy and glucose homeostasis. We demonstrate that commercially available Cordyceps contain variable amounts of myriocin and treatment of mice with a human equivalent dose of Cordyceps extract containing myriocin, reduces ceramide accrual, increases energy expenditure, prevents diet-induced obesity, improves glucose homeostasis and resolves hepatic steatosis. Mechanistically, these beneficial effects were due to increased adipose tissue browning/beiging, improved brown adipose tissue function and hepatic insulin sensitivity as well as alterations in the abundance of gut microbes such as Clostridium and Bilophila. Collectively, our data provide proof-of-principle that myriocin containing Cordyceps extract inhibit ceramide biosynthesis and attenuate metabolic impairments associated with obesity. Moreover, these studies identify commercially available Cordyceps as a readily available supplement to treat obesity and associated metabolic diseases.

Abstract Agricultural reclamation is widely regarded as a primary cause of marshes degradation. However, the process of marshes degradation on different geomorphology has rarely explored, which fail to explain the marshes degradation driven by natural restrictions in detail. The information deficiency unable propounded the targeted suggestions for the sustainable management of marshes. According to the development of China, we quantified the degradation rate of marshes on different geomorphic types from 1954 to 2020 in a typical transect in the Sanjiang Plain. The results indicated that (1) A total of 1633.92 km2 of marshes reduced from 1954 to 2020. And 97% (1582.35 km2) of marshes were converted to crop cultivation. The process of marshes degradation had obvious historical stages characteristics. The marshes degradation rate showed a trend of increasing first and then decreasing. The most serious period was 1995–2005 (6.29%) which was approximately 35 times of the period of before the reform and opening up (1954–1976) a minimal shrunk period. (2) The background of geological tectonic decided the whole trends in marshes degradation process. The degradation occurred first and worst in the meco-scale recent slow ascent region, and then extended to substantially recent slow subsidence region and the small-amplitude recent slow ascent region. (3) Significant location characteristics of marshes degradation reflected in this research. The spatial location of marshes degradation on the sub-regions sequentially consisted of alluvial plain, lower terrace, high floodplain, micro-knoll, low floodplain, and depressions. (4) Most of the existing marshes of the sub-Sanjiang Plain distribution in the national reserves. This study provides important scientific information for restoration and conservation of marshes.

Abstract Raoultella planticola is an emerging pathogen causing several infections in humans, and its roles in the propagation of antibiotic resistance genes (ARGs) remain uncharacterized. In this study, a carbapenem and tigecycline-resistant R. planticola isolate was recovered from hospital sewage. It carried nine plasmids, bearing 30 ARGs, including one bla KPC-2 and two bla NDM-1. It also contained a plasmid-borne efflux pump gene cluster, tmexCD1-toprJ, conferring resistance to tigecycline. Analysis of plasmid sequences revealed that both bla NDM-1-carrying plasmids were highly similar to those recovered from humans, reinforcing the close relatedness of environmental and clinical isolates. We also identified that plasmid bearing bla NDM-1 or tmexCD1-toprJ1 was transferable, and can be stabilized in the host bacteria, indicating that the R. planticola isolate has a considerable potential in the dissemination of ARGs. Besides, we found that this isolate could produce biofilm and was virulent in a Galleria mellonella infection model. In conclusion, our study shows the convergence of virulence and multidrug resistance in a R. planticola isolate. This potentially virulent superbug may disseminate into its receiving rivers, and finally to humans through cross-contamination during recreation activities or daily use of water, which poses a risk to public health.

Abstract The endometrium plays a critical role in embryo implantation and pregnancy, and a thin uterus is recognized as a key factor in embryo implantation failure. Umbilical cord mesenchymal stem cells (UC-MSCs) have attracted interest for the repair of intrauterine adhesions. The current study investigated the repair of thin endometrium in rats using the UC-MSCs and the mechanisms involved. Rats were injected with 95% ethanol to establish a model of thin endometrium. The rats were randomly divided into normal, sham, model, and UC-MSCs groups. Endometrial morphological alterations were observed by hematoxylin–eosin staining and Masson staining, and functional restoration was assessed by testing embryo implantation. The interaction between UC-MSCs and rat endometrial stromal cells (ESCs) was evaluated using a transwell 3D model and immunocytochemistry. Microarray mRNA and miRNA platforms were used for miRNA-mRNA expression profiling. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses were performed to identify the biological processes, molecular functions, cellular components, and pathways of endometrial injury and UC-MSCs transplantation repair and real-time quantitative reverse transcription PCR (qRT-PCR) was performed to further identify the expression changes of key molecules in the pathways. Endometrium thickness, number of glands, and the embryo implantation numbers were improved, and the degree of fibrosis was significantly alleviated by UC-MSCs treatment in the rat model of thin endometrium. In vitro cell experiments showed that UC-MSCs migrated to injured ESCs and enhanced their proliferation. miRNA microarray chip results showed that expression of 45 miRNAs was downregulated in the injured endometrium and upregulated after UC-MSCs transplantation. Likewise, expression of 39 miRNAs was upregulated in the injured endometrium and downregulated after UC-MSCs transplantation. The miRNA-mRNA interactions showed the changes in the miRNA and mRNA network during the processes of endometrial injury and repair. GO and KEGG analyses showed that the process of endometrial injury was mainly attributed to the decomposition of the extracellular matrix (ECM), protein degradation and absorption, and accompanying inflammation. The process of UC-MSCs transplantation and repair were accompanied by the reconstruction of the ECM, regulation of chemokines and inflammation, and cell proliferation and apoptosis. The key molecules involved in ECM-receptor interaction pathways were further verified by qRT-PCR. Itga1 and Thbs expression decreased in the model group and increased by UC-MSCs transplantation, while Laminin and Collagen expression increased in both the model group and MSCs group, with greater expression observed in the latter. This study showed that UC-MSCs transplantation could promote recovery of thin endometrial morphology and function. Furthermore, it revealed the expression changes of miRNA and mRNA after endometrial injury and UC-MSCs transplantation repair processed, and signaling pathways that may be involved in endometrial injury and repair.

Abstract High tumor regulatory T (Treg) cell infiltration is associated with poor prognosis of many cancers. CD25 is highly expressed on tumor Treg cells and is a potential target for Treg deletion. Previously characterized anti-CD25 antibodies appear to have limited efficacy in tumor inhibition. Here we identified two human anti-CD25 antibodies, BA9 and BT942, which did not prevent the activation of IL-2R signaling pathway by IL-2. BT942 had weaker binding and cytotoxic activity to human CD25-expressing cell lines than BA9. But both demonstrated significant tumor growth inhibition in early and late-stage animal cancer models. BT942 resulted in a higher expansion of CD8+ T cells and CD4+ T cells in tumor microenvironment in mouse MC38 model compared to BA9. BT942 also demonstrated significant higher tumor growth inhibition and higher expansion of CD8+ T cells and CD4+ T cells in combination with an anti-PD1 antibody. Pharmacokinetic study of BT942 in cynomolgus monkeys demonstrated a half-life of 206.97 ± 19.03 h. Structural analysis by cryo-EM revealed that BT942 recognizes an epitope on opposite side of the CD25-IL-2 binding site, consistent with no IL-2 signaling blockade in vitro. BT942 appears to be an excellent candidate for cancer immunotherapy.

Abstract Ultrasonography (US) is the primary diagnostic tool for thyroid nodules, while the accuracy is operator-dependent. It is widely used not only by radiologists but also by physicians with different levels of experience. The aim of this study was to investigate whether US with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Physicians with US experience less than 1 year (inexperienced, n = 10), or more than 5 years (experienced, n = 3) reviewed the US images of thyroid nodules with or without CAD assistance. The diagnostic performance of CAD was comparable to that of the experienced group, and better than those of the inexperienced group. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in the inexperienced group, but not in the experienced groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of less experienced physicians in US, especially in diagnosis of conventional PTC.

Abstract The zero echo time (ZTE) technique has improved the detection of lung nodules in PET/MRI but respiratory motion remains a challenge in lung scan. We investigated the feasibility and performance of fractionated deep-inspiration breath-hold (FDIBH) three-dimensional (3D) ZTE FDG PET/MRI for assessing lung nodules in patients with proved malignancy. Sixty patients who had undergone ZTE FDG PET/MRI and chest CT within a three-day interval were retrospectively included. Lung nodules less than 2 mm were excluded for analysis. Two physicians checked the adequacy of FDIBH ZTE and compared the lung nodule detection rates of FDIBH 3D ZTE and free-breathing (FB) four-dimensional (4D) ZTE, with chest CT as the reference standard. FDIBH resolved the effect of respiratory motion in 49 patients. The mean number and size of the pulmonary nodules identified in CT were 15 ± 31.3 per patient and 5.9 ± 4.6 mm in diameter. The overall nodule detection rate was 71% for FDIBH 3D ZTE and 70% for FB 4D ZTE (p = 0.73). FDIBH 3D ZTE significantly outperformed FB 4DZTE in detecting lung base nodules (72% and 68%; p = 0.03), especially for detecting those less than 6 mm (61% and 55%; p = 0.03). High inter-rater reliability for FDIBH 3D ZTE and FB 4D ZTE (k = 0.9 and 0.92) was noted. In conclusion, the capability of FDIBH 3D ZTE in respiratory motion resolution was limited with a technical failure rate of 18%. However, it could provide full expansion of the lung in a shorter scan time which enabled better detection of nodules (

Abstract Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness in the world and is influenced by various sociodemographic factors. This meta-analysis aims to determine the worldwide prevalence of POAG in the adult general population for the last 20 years, and explore variation in prevalence by age, gender and geographical location. An electronic literature search was performed using the PubMed, Embase, and Web of Science databases. Population-based cross-sectional or cohort studies published in the last 20 years (2000–2020) that reported prevalence of POAG were recruited. Relevant studies meeting defined eligibility criteria were selected and reviewed systematically by meta-analysis. The prevalence of POAG was analyzed according to various risk factors. A random effect model was used for the meta-analysis. Fifty publications with a total of 198,259 subjects were included in this meta-analysis. The worldwide overall prevalence of POAG was 2.4% (95% CI 2.0 ~ 2.8%). The prevalence increases with age. Men are found to be more susceptible to POAG than women (RR 1.28, p

Abstract Both electrical properties and biomarkers of biological tissues can be used to distinguish between normal and diseased tissues, and the correlations between them are critical for clinical applications of conductivity (σ) and permittivity (ε); however, these correlations remain unknown. This study aimed to investigate potential correlations between electrical characteristics and biomarkers of breast cancer cells (BCC). Changes in σ and ε of different components in suspensions of normal cells and BCC were analyzed in the range of 200 kHz–5 MHz. Pearson's correlation coefficient heatmap was used to investigate the correlation between σ and ε of the cell suspensions at different stages and biomarkers of cell growth and microenvironment. σ and ε of the cell suspensions closely resembled those of tissues. Further, the correlations between Na+/H+ exchanger 1 and ε and σ of cell suspensions were extremely significant among all biomarkers (pε

Abstract This study aimed to investigate the association between cognitive impairment after general anesthesia and rs55763075 polymorphisms. We enrolled and grouped patients undergoing general anesthesia according to their genotypes of rs55763075 polymorphism. Mini–Mental State Examination (MMSE) scoring was performed to evaluate the cognitive status of patients. Quantitative real-time PCR was carried out to analyze the expression of methylenetetrahydrofolate reductase (MTHFR) mRNA and miR-34b while Western blot was performed to evaluate the expression of MTHFR protein. Furthermore, we studied the effect of rs55763075 polymorphism on the expression of MEHFR via luciferase assay. Accordingly, we found that the MMSE score in GG/GA groups was significantly higher than that in AA group. And a significant reduction of MTHFR mRNA expression was observed in the serum and peripheral blood mononuclear cells (PBMCs) of patients carrying AA genotype compared with the patients carrying GG/GA genotypes. Moreover, the MTHFR expression was much lower in the cultured AA-genotyped cells transfected with miR-34b. Luciferase assay results also showed that miR-34b transfection reduced luciferase activity in the cells carrying A allele but not in cells carrying G allele. In summary, the data of this study showed that minor allele (A) of rs55763075 polymorphisms in the 3'-untranslated region of MTHFR mRNA generated a potential binding site for miR-34b, which led to reduced level of folic acid in the patients carrying the AA genotype. Furthermore, we found that the MMSE score of AA-genotyped patients was lower than that of patients carrying GG/GA genotypes.

Abstract The 2019 novel coronavirus pandemic caused by SARS-CoV-2 remains a serious health threat to humans and there is an urgent need to develop therapeutics against this deadly virus. Recent scientific evidences have suggested that the main protease (Mpro) enzyme in SARS-CoV-2 can be an ideal drug target due to its crucial role in the viral replication and transcription processes. Therefore, there are ongoing research efforts to identify drug candidates against SARS-CoV-2 Mpro that resulted in hundreds of X-ray crystal structures of ligand-bound Mpro complexes in the Protein Data Bank (PDB) describing the interactions of different fragment chemotypes within different sites of the Mpro. In this work, we performed rigorous molecular dynamics (MD) simulation of 62 reversible ligand–Mpro complexes in the PDB to gain mechanistic insights about their interactions at the atomic level. Using a total of over 3 µs long MD trajectories, we characterized different pockets in the apo Mpro structure, and analyzed the dynamic interactions and binding affinity of ligands within those pockets. Our results identified the key residues that stabilize the ligands in the catalytic sites and other pockets of Mpro. Our analyses unraveled the role of a lateral pocket in the catalytic site in Mpro that is critical for enhancing the ligand binding to the enzyme. We also highlighted the important contribution from HIS163 in the lateral pocket towards ligand binding and affinity against Mpro through computational mutation analyses. Further, we revealed the effects of explicit water molecules and Mpro dimerization in the ligand association with the target. Thus, comprehensive molecular-level insights gained from this work can be useful to identify or design potent small molecule inhibitors against SARS-CoV-2 Mpro.

Abstract The aim is to investigate that 17β-estradiol (E2)/estrogen receptors (ERs) activation normalizes splenic CD4 + T lymphocytes proliferation and cytokine production through inhibition of endoplasmic reticulum stress (ERS) following hemorrhage. The results showed that hemorrhagic shock (hemorrhage through femoral artery, 38–42 mmHg for 90 min followed by resuscitation of 30 min and subsequent observation period of 180 min) decreased the CD4+ T lymphocytes proliferation and cytokine production after isolation and incubation with Concanavalin A (5 μg/mL) for 48 h, induced the splenic injury with evidences of missed contours of the white pulp, irregular cellular structure, and typical inflammatory cell infiltration, upregulated the expressions of ERS biomarkers 78 kDa glucose-regulated protein (GRP78) and activating transcription factor 6 (ATF6). Either E2, ER-α agonist propyl pyrazole triol (PPT) or ERS inhibitor 4-Phenylbutyric acid administration normalized these parameters, while ER-β agonist diarylpropionitrile administration had no effect. In contrast, administrations of either ERs antagonist ICI 182,780 or G15 abolished the salutary effects of E2. Likewise, ERS inducer tunicamycin induced an adverse effect similarly to that of hemorrhagic shock in sham rats, and aggravated shock-induced effects, also abolished the beneficial effects of E2 and PPT, respectively. Together, the data suggest that E2 produces salutary effects on CD4+ T lymphocytes function, and these effects are mediated by ER-α and GPR30, but not ER-β, and associated with the attenuation of hemorrhagic shock-induced ERS.