Search

Background: Bladder cancer is a common malignancy with high recurrence rate. Early diagnosis and recurrence surveillance are pivotal to patients' outcomes, which require novel minimal-invasive diagnostic tools. The urinary microbiome is associated with bladder cancer and can be used as biomarkers, but the underlying mechanism is to be fully illustrated and diagnostic performance to be improved. Methods: A total of 23 treatment-naïve bladder cancer patients and 9 non-cancerous subjects were enrolled into the Before group and Control group. After surgery, 10 patients from the Before group were further assigned into After group. Void mid-stream urine samples were collected and sent for 16S rDNA sequencing, targeted metabolomic profiling, and flow cytometry. Next, correlations were analyzed between microbiota, metabolites, and cytokines. Finally, receiver operating characteristic (ROC) curves of the urinary biomarkers were plotted and compared. Results: Comparing to the Control group, levels of IL-6 (p < 0.01), IL-8 (p < 0.05), and IL-10 (p < 0.05) were remarkably elevated in the Before group. The α diversity of urine microbiome was also significantly higher, with the feature microbiota positively correlated to the level of IL-6 (r = 0.58, p < 0.01). Significant differences in metabolic composition were also observed between the Before and Control groups, with fatty acids and fatty acylcarnitines enriched in the Before group. After tumor resection, cytokine levels and the overall microbiome structure in the After group remained similar to that of the Before group, but fatty acylcarnitines were significantly reduced (p < 0.05). Pathway enrichment analysis revealed beta-oxidation of fatty acids was significantly involved (p < 0.001). ROC curves showed that the biomarker panel of Actinomycetaceae + arachidonic acid + IL-6 had superior diagnostic performance, with sensitivity of 0.94 and specificity of 1.00. Conclusions: Microbiome dysbiosis, proinflammatory environment and altered fatty acids metabolism are involved in the pathogenesis of bladder cancer, which may throw light on novel noninvasive diagnostic tool development. [ABSTRACT FROM AUTHOR]

Background: Long noncoding RNAs (lncRNAs) regulate the pathogenesis of Alzheimer's disease (AD). Objective: To identify lncRNAs in the peripheral blood as potential diagnostic biomarkers for amnestic mild cognitive impairment. Methods: In the discovery group, a microarray was used to screen for significant differences in lncRNA expression between patients with mild cognitive impairment (MCI) caused by AD and normal controls (NCs) (n = 10; MCI, 5; NC, 5). Furthermore, two analytic groups were assessed (analytic group 1: n = 10; amnestic MCI (aMCI), 5; NC, 5; analytic group 2: n = 30; AD, 10; aMCI, 10; NC, 10) and finalized in the validation group (n = 150; AD, 50; aMCI, 50; NC, 50). In the analytic and validation groups, real-time quantitative reverse-transcription polymerase chain reaction was used to identify differentially expressed lncRNAs between the aMCI and NC groups. Results: We identified 67 upregulated and 220 downregulated lncRNAs among the expression profiles. The panel with lncRNAs T324988, NR_024049, ENST00000567919, and ENST00000549762 displayed the highest discrimination ability between patients with aMCI and NCs. The area under the receiver operating characteristic curve of this combined model was 0.941, with a sensitivity of 92.00% and specificity of 84.00%. Conclusions: This study reports on a panel of four lncRNAs as promising biomarkers to diagnose aMCIs. [ABSTRACT FROM AUTHOR]

Background: The objective of this investigation is to evaluate the superiority of dose-volume parameters relying on magnetic resonance imaging (MRI)-defined active bone marrow (ABM) over those based on total bone marrow (TBM) contoured via CT in the prediction of hematologic toxicity (HT) occurrence among patients with pelvic malignancies undergoing radiotherapy. Methods: The clinical data of 116 patients with pelvic malignancies treated with pelvic radiotherapy were analyzed retrospectively. The ABM areas on T1-weighted MRI were contoured. The statistical significance between TBM and ABM dose-volume measures was assessed through the utilization of either Student's t-test or Wilcoxon signed rank test. Logistic and linear regression models were employed to analyze the correlation between dose-volume parameters (V5-V50) and HT occurrence in pelvic ABM and TBM. Receiver operating characteristic (ROC) curves were used to compare predictors of HT2+. Results: There were significant differences in dosimetric parameters between ABM and TBM. Logistic regression analysis showed that ABM V5, ABM V10, ABM V15, ABM V20, and TBM V5 were significantly associated with the occurrence of HT2+ in pelvic malignancies. Linear regression analysis showed that ABM V5, ABM V10, and ABM V15 were significantly associated with white blood cell (WBC), absolute neutrophil count (ANC), hemoglobin (Hb), and lymphocyte (Lym) nadir. ABM V5, ABM V10, ABM V15, and ABM V30 were predictive of HT2+. Conclusions: More accurate prediction of HT in patients receiving pelvic radiotherapy may be achieved by relying on dose-volume parameters of MRI-based ABM. Further prospective studies are needed to confirm this. [ABSTRACT FROM AUTHOR]

Background: Circular RNAs (circRNAs) exhibit unique patterns of expression and high levels of stability in patient plasma samples such that they represent ideal non-invasive biomarkers that can be leveraged to detect a wide array of diseases including endometrial cancer (EC). This study was designed to identify circRNAs with potential diagnostic utility in serum samples from EC patients while also evaluating the utility of macrophage migration inhibitory factor (MIF) as a biomarker when screening for this form of cancer in the clinic. Methods: Levels of circEPSTI1 and MIF were assessed in the plasma of EC patients and healthy subjects (n=186 each) through qPCR and ELISAs. The diagnostic utility of these biomarkers was assessed with receiver operating characteristic curve (ROC) analyses. Results: Relative to healthy subjects, EC patient serum contained significantly elevated circEPSTI1 and MIF. An association was noted between circEPSTI1 expression in stages, histologic grade, and residual tumor. ROC curves confirmed that serum circEPSTI1 levels distinguished between controls and patients with EC with an Area of 0.835 and serum MIF levels distinguished between controls and patients with EC with an Area of 0.6646. When instead diagnosing patients based on the combination of MIF and circEPSTI1, the Area further rose to 0.8604. Conclusion: Assessing the combination of circEPSTI1 and MIF may be a viable approach to reliably diagnosing EC. [ABSTRACT FROM AUTHOR]

Objective To explore the relationship between the levels of serum 8-hydroxy deoxyguanosine (8 - OHdG) and malondialdehyde (MDA) and cognitive dysfunction in patients with Parkinson's disease (PD). Methods From February 2021 to February 2022, 126 patients with PD in The Affiliated Hospital of Xuzhou Medical University were divided into normal cognitive function group (PDN group, n = 41), mild cognitive impairment group (PD-MCI group, n = 47) and Parkinson's disease dementia group (PDD group, n = 38), and 50 healthy subjects were selected as control group. Hoehn-Yahr staging was used to evaluate the severity of patients with PD during the "close" period of anti-PD drugs, and the motor function of patients with PD was evaluated by the Unified Parkinson's Disease Rating Scale III (UPDRS III). Montreal Cognitive Assessment (MoCA) was used to evaluate the severity of cognitive dysfunction at rest and in the "on" period of anti-PD drugs, and the serum 8-OHdG and MDA of PD patients and controls were collected. Pearson and partial correlation analyses were used to analyze the correlation between the levels of serum 8-OHdG and MDA and MoCA score in PD patients. Univariate and multivariate Logistic regression analyses were used to analyze the influencing factors of cognitive dysfunction in PD patients. The efficacy analysis of serum 8-OHdG and MDA levels in predicting the risk of cognitive dysfunction in PD patients was carried out by using receiver operating characteristic curve (ROC curve). Results The results of correlation analysis showed that there was a negative correlation between the MoCA score and the duration (r = - 0.241, P = 0.007), Hoehn-Yahr staging (r = - 0.333, P = 0.007), 8-OHdG (r = - 0.310, P = 0.000) and MDA (r = - 0.291, P = 0.004) in PD patients. The results of Logistic regression analysis showed that the elevated levels of 8-OHdG (OR = 1.335, 95%CI: 1.137-1.568; P = 0.000) and MDA (OR = 2.928, 95%CI: 1.676-5.115; P = 0.000) were risk factors for cognitive dysfunction in PD patients. The results of ROC curve showed the areas under the curve of 8-OHdG, MDA and their combination in predicting cognitive dysfunction in PD patients were 0.831 (95%CI: 0.761-0.902, P = 0.000), 0.846 (95%CI: 0.775-0.916, P = 0.000) and 0.922 (95%CI: 0.878-0.966, P = 0.000), respectively. Conclusions The detection of 8-OHdG and MDA in peripheral blood is expected to be a serum marker to evaluate the severity of cognitive dysfunction in patients with PD, and to predict cognitive dysfunction in patients with PD. [ABSTRACT FROM AUTHOR]

Peroxisome proliferator‐activated receptors (PPARs) are essential for cellular physiological processes. However, there is less research on the PPAR‐related genes in lung adenocarcinoma (LUAD). Open‐access data were get from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. All the analysis were conducted in the R software based on different R packages. In this study, we gauged the PPAR score employing a set of 72 PPAR‐associated genes and probed the biological impact of this score on lung adenocarcinoma (LUAD). Subsequently, we established a unique signature composed of eight PPAR‐related genes (ANGPTL4, ACSL3, ADIPOQ, FABP1, SLC27A1, ACOX2, PPARD and OLR1) to forecast the prognosis of LUAD. The signature's effectiveness in predicting survival was validated through the receiver operating characteristic curve in the TCGA‐LUAD cohort. As per the pathway enrichment analysis, several crucial oncogenic pathways and metabolic processes were enriched in high‐risk individuals. Further, we observed that these high‐risk patients exhibited heightened genomic instability. Additionally, compared to the low‐risk cohort, high‐risk patients demonstrated diminished immune components and function. Intriguingly, high‐risk patients exhibited a potential heightened sensitivity to immunotherapy and certain drugs, including Gefitinib, Afatinib, Erlotinib, IAP_5620, Sapitinib, LCL161, Lapatinib and AZD3759. The prognosis model based on eight PPAR‐related genes has satisfactory prognosis prediction efficiency. Meanwhile, our results can provide direction for future studies in the relevant aspects. [ABSTRACT FROM AUTHOR]

Objective: To propose a scoring system based on laryngoscopic characteristics for the differential diagnosis of benign and malignant vocal fold leukoplakia. Methods: Laryngoscopic images from 200 vocal fold leukoplakia cases were retrospectively analysed. The laryngoscopic signs of benign and malignant vocal fold leukoplakia were compared, and statistically significant features were assigned and accumulated to establish the leukoplakia finding score. Results: A total of five indicators associated with malignant vocal fold leukoplakia were included to construct the leukoplakia finding score, with a possible range of 0–10 points. A score of 6 points or more was indicative of a diagnosis of malignant vocal fold leukoplakia. The sensitivity, specificity and accuracy values of the leukoplakia finding score were 93.8 per cent, 83.6 per cent and 86.0 per cent, respectively. The consistency in the leukoplakia finding score obtained by different laryngologists was strong (kappa = 0.809). Conclusion: This scoring system based on laryngoscopic characteristics has high diagnostic value for distinguishing benign and malignant vocal fold leukoplakia. [ABSTRACT FROM AUTHOR]

Background: Various clinical similarities are present in ischemic (ICM) and idiopathic dilated cardiomyopathy (IDCM), leading to ambiguity on some occasions. Previous studies have reported that intestinal microbiota appeared dysbiosis in ICM, whether implicating in the IDCM remains unclear. The aim of this study was to assess the alterations in intestinal microbiota and fecal metabolites in ICM and IDCM. Methods: ICM (n = 20), IDCM (n = 22), and healthy controls (HC, n = 20) were enrolled in this study. Stool samples were collected for 16S rRNA gene sequencing and gas chromatography-mass spectrometry (GC–MS) analysis. Results: Both ICM and IDCM exhibited reduced alpha diversity and altered microbial community structure compared to HC. At the genus level, nine taxa including Blautia, [Ruminococcus]_torques_group, Christensenellaceae_R-7_group, UCG-002, Corynebacterium, Oceanobacillus, Gracilibacillus, Klebsiella and Citrobacter was specific to ICM, whereas one taxa Alistipes uniquely altered in IDCM. Likewise, these changes were accompanied by significant metabolic differences. Further differential analysis displayed that 18 and 14 specific metabolites uniquely changed in ICM and IDCM, respectively. The heatmap was generated to display the association between genera and metabolites. Receiver operating characteristic curve (ROC) analysis confirmed the predictive value of the distinct microbial-metabolite features in disease status. The results showed that microbial (area under curve, AUC = 0.95) and metabolic signatures (AUC = 0.84) were effective in discriminating ICM from HC. Based on the specific microbial and metabolic features, the patients with IDCM could be separated from HC with an AUC of 0.80 and 0.87, respectively. Furthermore, the gut microbial genus (AUC = 0.88) and metabolite model (AUC = 0.89) were comparable in predicting IDCM from ICM. Especially, the combination of fecal microbial-metabolic features improved the ability to differentiate IDCM from ICM with an AUC of 0.96. Conclusion: Our findings highlighted the alterations of gut microbiota and metabolites in different types of cardiomyopathies, providing insights into the pathophysiological mechanisms of myocardial diseases. Moreover, multi-omics analysis of fecal samples holds promise as a non-invasive tool for distinguishing disease status. [ABSTRACT FROM AUTHOR]

This study aimed to develop a postoperative MRI-based fibrosis scoring system and to assess its correlation with anorectal function in locally advanced rectal cancer (LARC) cases administered neoadjuvant chemoradiotherapy (nCRT). Pathologically confirmed LARC cases administered nCRT and radical resection were assessed retrospectively. Based on postoperative magnetic resonance imaging (MRI) findings, anastomotic fibrosis score (AFS) and perirectal fibrosis score (PFS) were determined to evaluate the extent of fibrosis. The Wexner continence score for anorectal function was obtained 2 years postoperatively and assessed for correlation with MRI fibrosis scores. The cases were divided into 2 groups by the median Wexner score. Univariable and multivariable analyses were adopted for building a nomogram model, whose diagnostic performance was estimated by receiver operating characteristic (ROC) and decision curve analyses (DCA). Finally, 144 patients with LARC were included in cohort 1 (training set). 52 patients were enrolled in cohort 2 (external validation set). Spearman correlation analysis indicated that AFS and PFS were positively correlated with the Wexner score. Univariable and multivariable analyses revealed age, tumor height, AFS, and PFS were independent predictors of anorectal function. The nomogram model achieved a good diagnostic performance, with AUCs of 0.800 and 0.827 in the training and validation sets, respectively; its predicting value was also confirmed by DCA. The present study showed AFS and PFS derived from postoperative MRI are positively correlated with Wexner score. In addition, the new scoring system was effective in predicting anorectal function in LARC cases administered nCRT. [ABSTRACT FROM AUTHOR]

Purpose: Prediction of Fuhrman nuclear grade is crucial for making informed herapeutic decisions in clear cell renal cell carcinoma (ccRCC). The current study aimed to develop a multi-information fusion model utilizing computed tomography (CT)-based features of tumors and preoperative biochemical parameters to predict the Fuhrman nuclear grade of ccRCC in a non-invasive manner. Methods: 218 ccRCC patients confirmed by histopathology were retrospectively analyzed. Univariate and multivariate logistic regression analyses were performed to identify independent predictors and establish a model for predicting the Fuhrman grade in ccRCC. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration, the 10-fold cross-validation method, bootstrapping, the Hosmer–Lemeshow test, and decision curve analysis (DCA). Results: R.E.N.A.L. Nephrometry Score (RNS) and serum tumor associated material (TAM) were identified as independent predictors for Fuhrman grade of ccRCC through multivariate logistic regression. The areas under the ROC curve (AUC) for the multi-information fusion model composed of the above two factors was 0.810, higher than that of the RNS (AUC 0.694) or TAM (AUC 0.764) alone. The calibration curve and Hosmer–Lemeshow test showed the integrated model had a good fitting degree. The 10-fold cross-validation method (AUC 0.806) and bootstrap test (AUC 0.811) showed the good stability of the model. DCA demonstrated that the model had superior clinical utility. Conclusion: A multi-information fusion model based on CT features of tumor and routine biochemical indicators, can predict the Fuhrman grade of ccRCC using a non-invasive approach. This model holds promise for assisting clinicians in devising personalized management strategies. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the value of secretions Eosinophilic cationic protein (ECP) detection in the diagnosis of endotypes of Chronic rhinosinusitis (CRS) and its correlation with clinical symptoms, so as to provide guidance for the clinical application of EOS and ECP detection in secretions. Methods: Patients' nasal secretions and polyps (or middle turbinate for control) were collected and their EOS% and ECP levels were measured. Correlation analysis was performed for EOS% and ECP levels in secretions and tissues, respectively. The correlation between secretions EOS% and ECP and clinical symptom scores (symptomatic visual analog scale (VAS) scores, Lanza‐kennedy scores from nasal endoscopy and Lund‐Mackay scores from sinus CT) was further analyzed. Receiver operating characteristic curves were used to assess the predictive potential of EOS% and ECP in nasal secretions. Results: Eosinophilic chronic rhinosinusitis (ECRS) patients had higher concentrations of ECP in nasal secretions than healthy subjects and NECRS (non‐eosinophilic CRS) (p < 0.0001;0.0001); EOS% in nasal secretions was higher in ECRS than healthy subjects (p = 0.0055), but the differences between ECRS and NECRS were not statistically significant (p = 0.0999). Correlation analysis showed that tissue EOS% was correlated with ECP concentration and EOS% in nasal secretions (R = 0.5943;0.2815). There was a correlation between EOS% in secretions with a total LM score (R = 0.3131); ECP concentration in secretions with a total LK score (R = 0.3792). To diagnose ECRS, the highest area under the curve (0.8230) was determined for ECP in secretions; the highest area under the curve (0.6635) was determined for EOS% in secretions. Conclusion: Measurement of ECP in nasal secretions is useful for non‐invasive diagnosis of ECRS. Level of Evidence: 3 Laryngoscope, 133:3304–3312, 2023 [ABSTRACT FROM AUTHOR]

To investigate whether glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), and 12 cytokines can serve as serum biomarkers of olfactory identification dysfunction in patients with Parkinson's disease (PD). GFAP and NFL levels were measured in 75 patients with PD and 36 healthy controls (HCs). The levels of 12 cytokines were assayed in 41 patients with PD. The 16-item Sniffin' Sticks test and the Mini-Mental State Examination (MMSE) were used to assess olfactory identification ability and cognitive function, respectively. Linear regression models were applied to control for confounding effects. Receiver operating characteristic curves were used to examine the diagnostic accuracy of serum NFL, GFAP, and interleukin-6 (IL-6) levels. The cut-off value for the SS-16 test in diagnosing dysosmia was equal to 9.5 points. Serum GFAP levels were higher in patients with PD with olfactory identification dysfunction than in those without. GFAP, NFL, and IL-6 levels were correlated with SS-16 scores. Moreover, combining these three biomarkers yielded the best-fitting model for distinguishing patients with PD with or without dysosmia. We found a prominent indirect effect of GFAP on MMSE scores through its contribution to SS-16 scores. GFAP, NFL, and IL-6 can serve as serum biomarkers for olfactory identification dysfunctions in PD. We inferred that astrogliosis might promote the occurrence of dysosmia by releasing proinflammatory factors and causing neuronal damage and may indirectly impair cognition through its effect on olfactory function. [ABSTRACT FROM AUTHOR]

Background: Respiratory variation in the internal jugular vein (IJVV) has not shown promising results in predicting volume responsiveness in ventilated patients with low tidal volume (Vt) in prone position. We aimed to determine whether the baseline respiratory variation in the IJVV value measured by ultrasound might predict fluid responsiveness in patients with adolescent idiopathic scoliosis (AIS) undergoing posterior spinal fusion (PSF) with low Vt. Methods: According to the fluid responsiveness results, the included patients were divided into two groups: those who responded to volume expansion, denoted the responder group, and those who did not respond, denoted the non-responder group. The primary outcome was determination of the value of baseline IJVV in predicting fluid responsiveness (≥15% increases in stroke volume index (SVI) after 7 ml·kg-1 colloid administration) in patients with AIS undergoing PSF during low Vt ventilation. Secondary outcomes were estimation of the diagnostic performance of pulse pressure variation (PPV), stroke volume variation (SVV), and the combination of IJVV and PPV in predicting fluid responsiveness in this surgical setting. The ability of each parameter to predict fluid responsiveness was assessed using a receiver operating characteristic curve. Results: Fifty-six patients were included, 36 (64.29%) of whom were deemed fluid responsive. No significant difference in baseline IJVV was found between responders and non-responders (25.89% vs. 23.66%, p = 0.73), and no correlation was detected between baseline IJVV and the increase in SVI after volume expansion (r = 0.14, p = 0.40). A baseline IJVV greater than 32.00%, SVV greater than 14.30%, PPV greater than 11.00%, and a combination of IJVV and PPV greater than 64.00% had utility in identifying fluid responsiveness, with a sensitivity of 33.33%, 77.78%, 55.56%, and 55.56%, respectively, and a specificity of 80.00%, 50.00%, 65.00%, and 65.00%, respectively. The area under the receiver operating characteristic curve for the baseline values of IJVV, SVV, PPV, and the combination of IJVV and PPV was 0.52 (95% CI, 0.38–0.65, p=0.83), 0.54 (95% CI, 0.40–0.67, p=0.67), 0.58 (95% CI, 0.45–0.71, p=0.31), and 0.57 (95% CI, 0.43–0.71, p=0.37), respectively. Conclusions: Ultrasonic-derived IJVV lacked accuracy in predicting fluid responsiveness in patients with AIS undergoing PSF during low Vt ventilation. In addition, the baseline values of PPV, SVV, and the combination of IJVV and PPV did not predict fluid responsiveness in this surgical setting. Trail registration: This trial was registered at www.chictr.org (ChiCTR2200064947) on 24/10/2022. All data were collected through chart review. [ABSTRACT FROM AUTHOR]

Background: Posttransplant lymphoproliferative disease (PTLD) is a serious complication after pediatric liver transplantation (pLT), which may lead to death. 18F‐FDG PET/CT is rarely considered in PTLD after pLT and lacks clear diagnostic guidelines, especially in the differential diagnosis of nondestructive PTLD. The aim of this study was to find a quantifiable 18F‐FDG PET/CT index to identify nondestructive PTLD after pLT. Methods: This retrospective study collected the data of patients who underwent pLT, postoperative lymph node biopsy, and 18F‐FDG PET/CT at Tianjin First Central Hospital from January 2014 to December 2021. Quantitative indexes were established using lymph node morphology and the maximum standardized uptake value (SUVmax). Results: A total of 83 patients met the inclusion criteria and were included in this retrospective study. To distinguish between PTLD‐negative cases and nondestructive PTLD cases, according to the receiver operating characteristic curve, (the shortest diameter of the lymph node at the biopsy site [SDL]/the longest diameter of the lymph node at the biopsy site [LDL])*(SUVmax at the biopsy site [SUVmaxBio]/SUVmax of the tonsils [SUVmaxTon]) had the maximum area under the curve (0.923; 95% confidence interval: 0.834–1.000), and the cutoff value was 0.264 according to the maximum value of Youden's index. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 93.6%, 94.7%, 97.8%, 85.7%, and 93.9%, respectively. Conclusions: (SDL/LDL)*(SUVmaxBio/SUVmaxTon) has good sensitivity, specificity, positive predictive and negative predictive values, and accuracy, and can be used as a good quantitative index for the diagnosis of nondestructive PTLD. [ABSTRACT FROM AUTHOR]

Objectives: To explore associations between inflammatory endotypes and clinical presentations in CRS. To investigate the value of secretions myeloperoxidase (MPO) and eosinophilic cationic protein (ECP) detections in the diagnosis of endotypes of chronic rhinosinusitis (CRS), so as to provide guidance for the clinical application of MPO and ECP detection in secretions. Methods: We collected clinical symptom scores from patients with CRS and examined the differences between endotypes in clinical features. Patients' nasal secretions and polyps (or middle turbinate for control) were collected and their NEU number, EOS%, MPO and ECP levels were measured. Correlation analysis was performed for these biomarkers in secretions and tissues, respectively. Receiver operating characteristic curves were used to assess the predictive potential of the biomarkers mentioned above in nasal secretions. Results: Patients with Eos+Neu+ and Eos+Neu−CRS scored highest in most clinical symptom scores, while Eos−Neu+ and Eos−Neu−CRS scored lowest. Correlation analysis showed that tissues NEU number was correlated with NEU number and MPO level in nasal secretions (R = 0.4088; 0.6613); tissues EOS % was correlated with EOS% and ECP level in nasal secretions (R = 0.2344; 0.5774). To diagnose Neu+CRS, the highest area under the curve (AUC) (0.8961) was determined for MPO in secretions; the highest AUC (0.7400) was determined for NEU number in secretions. To diagnose Eos+Neu−CRS from Eos−Neu−CRS in Neu−CRS, the highest AUC (0.8801) was determined for ECP in secretions. Conclusions: Clinical presentations are directly associated with CRS endotypes. Measurement of MPO and ECP in nasal secretions is useful for the endotypes diagnosis of CRS. [ABSTRACT FROM AUTHOR]

Background: There is a recognized need for additional approaches to improve the accuracy of extrathyroidal extension (ETE) diagnosis in papillary thyroid carcinoma (PTC) before surgery. Up to now, multimodal ultrasound has been widely applied in disease diagnosis. We investigated the value of radiomic features extracted from multimodal ultrasound in the preoperative prediction of ETE. Methods: We retrospectively pathologically confirmed PTC lesions in 235 patients from January 2019 to April 2022 in our hospital, including 45 ETE lesions and 205 non-ETE lesions. MaZda software was employed to obtain radiomics parameters in multimodal sonography. The most valuable radiomics features were selected by the Fisher coefficient, mutual information, probability of classification error and average correlation coefficient methods (F + MI + PA) in combination with the least absolute shrinkage and selection operator (LASSO) method. Finally, the multimodal model was developed by incorporating the clinical records and radiomics features through fivefold cross-validation with a linear support vector machine algorithm. The predictive performance was evaluated by sensitivity, specificity, accuracy, F1 scores and the area under the receiver operating characteristic curve (AUC) in the training and test sets. Results: A total of 5972 radiomics features were extracted from multimodal sonography, and the 13 most valuable radiomics features were selected from the training set using the F + MI + PA method combined with LASSO regression. The multimodal prediction model yielded AUCs of 0.911 (95% CI 0.866–0.957) and 0.716 (95% CI 0.522–0.910) in the cross-validation and test sets, respectively. The multimodal model and radiomics model showed good discrimination between ETE and non-ETE lesions. Conclusion: Radiomics features based on multimodal ultrasonography could play a promising role in detecting ETE before surgery. [ABSTRACT FROM AUTHOR]

Purpose: To explore the relationship between C2 slope with sagittal parameters and clinical function of degenerative cervical kyphosis (DCK). Methods: A retrospective analysis of 127 patients with degenerative cervical spondylosis treated in our spinal deformity center from January 2019 to June 2022. Patients were categorized into two groups and compared based on C2-7 angle (C2-7 ≥ 5° as kyphosis group, C2-7 < 5° as lordosis group). Pearson correlation or Spearman correlation was used to analyze the relationship between C2S and conventional radiological parameters and health -related quality-of-life (HRQOL) outcomes as measured by the EuroQol 5 dimension questionnaire (EQ5D), NRS, and the neck disability index (NDI). The cutoff value of C2S was determined by a receiver operating characteristic (ROC) curve. Results: There were 127 patients who met inclusion criteria (79 men and 48 women). Average 56.00 ± 10.27 years old (range 31–81 years old). C2S of kyphosis group is higher than non-kyphosis group. Aggravating cervical kyphosis increases cSVA positively. For all patients, C2S demonstrated a significant correlation with the O-C2 angle, C2-7 angle, cSVA, and TS-CL (p < 0.05). NRS, NDI and EQ5D-VAS scores revealed a significant correlation with C2S and cSVA (p < 0.01). For the subgroup of patients presenting with DCK, ROC curves demonstrated the cutoff values of C2S as 26.3°, and 30.5°, according to a cSVA of 40 mm, and severe disability expressed by NDI, respectively. Conclusion: On the basis of retaining the consistency of cranio-cervical and cervico-thoracic structure, C2S can better analyze the sagittal alignment of DCK patients than TS-CL and has good practicability in clinical application and HRQOL evaluation. [ABSTRACT FROM AUTHOR]

To reduce the cargo loss rate caused by abnormal consumption behavior in smart retail cabinets, two problems need to be solved. The first is that the diversity of consumers leads to a diversity of actions contained in the same behavior, which makes the accuracy of consumer behavior identification low. Second, the difference between normal interaction behavior and abnormal interaction behavior is small, and anomalous features are difficult to define. Therefore, we propose an anomalous behavior detection algorithm with human–object interaction graph convolution and confidence-guided difference enhancement. Aiming to solve the problem of low accuracy of consumer behavior recognition, including interactive behavior, the human–object interaction graph convolutional network is used to recognize action and extract video frames of abnormal human behavior. To define anomalies, we detect anomalies by delineating anomalous areas of the anomaly video frames. We use a confidence-guided anomaly enhancement module to perform confidence detection on the encoder-extracted coded features using a confidence full connection layer. The experimental results showed that the action recognition algorithm had good generalization ability and accuracy, and the screened video frames have obvious destruction characteristics, and the area under the receiver operating characteristic (AUROC) curve reached 82.8% in the detection of abnormal areas. Our research provides a new solution for the detection of abnormal behavior that destroys commodity packaging, which has considerable application value. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to develop and validate a predictive model for the prediction of in-hospital mortality in patients with acute pancreatitis (AP) based on the intensive care database. Patients and Methods: We analyzed the data of patients with AP in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and Electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Then, patients from MIMIC-IV were divided into a development group and a validation group according to the ratio of 8:2, and eICU-CRD was assigned as an external validation group. Univariate logistic regression and least absolute shrinkage and selection operator regression were used for screening the best predictors, and multivariate logistic regression was used to establish a dynamic nomogram. We evaluated the discrimination, calibration, and clinical efficacy of the nomogram, and compared the performance of the nomogram with Acute Physiology and Chronic Health Evaluation II (APACHE-II) score and Bedside Index of Severity in AP (BISAP) score. Results: A total of 1030 and 514 patients with AP in MIMIC-IV database and eICU-CRD were included in the study. After stepwise analysis, 8 out of a total of 37 variables were selected to construct the nomogram. The dynamic nomogram can be obtained by visiting https://model.sci-inn.com/KangZou/. The area under receiver operating characteristic curve (AUC) of the nomogram was 0.859, 0.871, and 0.847 in the development, internal, and external validation set respectively. The nomogram had a similar performance with APACHE-II (AUC = 0.841, p = 0.537) but performed better than BISAP (AUC = 0.690, p = 0.001) score in the validation group. Moreover, the calibration curve presented a satisfactory predictive accuracy, and the decision curve analysis suggested great clinical application value of the nomogram. Conclusion: Based on the results of internal and external validation, the nomogram showed favorable discrimination, calibration, and clinical practicability in predicting the in-hospital mortality of patients with AP. [ABSTRACT FROM AUTHOR]

Background: Pathologic complete response (pCR) following preoperative systemic therapy is associated with improved outcomes after subsequent liver transplant/resection in hepatocellular carcinoma (HCC). However, the relationship between radiographic and histopathological response remains unclear. Methods: We retrospectively examined patients with initially unresectable HCC who received tyrosine kinase inhibitor (TKI) plus anti–programmed death 1 (PD-1) therapy before undergoing liver resection between March 2019 and September 2021 across 7 hospitals in China. Radiographic response was evaluated using mRECIST. A pCR was defined as no viable tumor cells in resected samples. Results: We included 35 eligible patients, of whom 15 (42.9%) achieved pCR after systemic therapy. After a median follow-up of 13.2 months, tumors recurred in 8 non-pCR and 1 pCR patient. Before resection, there were 6 complete responses, 24 partial responses, 4 stable disease cases, and 1 progressive disease case, per mRECIST. Predicting pCR by radiographic response yielded an area under the receiver operating characteristic curve (AUC) of 0.727 (95% CI: 0.558–0.902), with an optimal cutoff value of 80% reduction in the enhanced area in MRI (called major radiographic response), which had a 66.7% sensitivity, 85.0% specificity, and a 77.1% diagnostic accuracy. When radiographic response was combined with α-fetoprotein response, the AUC was 0.926 (95% CI: 0.785–0.999); the optimal cutoff value was 0.446, which had a 91.7% sensitivity, 84.6%, specificity, and an 88.0% diagnostic accuracy. Conclusions: In patients with unresectable HCC receiving combined TKI/anti–PD 1 therapy, major radiographic response alone or combined with α-fetoprotein response may predict pCR. [ABSTRACT FROM AUTHOR]

Stroke is closely associated with carotid plaques. The assessment of carotid plaque is still the key issue of stroke prevention in clinical practice. This prospective cross‐sectional study included patients with carotid plaque evaluated by ultrasonography (US). The intima‐media thickness (IMT), lumen stenosis severity, thickness, and length of carotid plaque were measured by the routine US, and the amplitudes of subharmonics in the upstream shoulder, top, and downstream shoulder of all plaques and corresponding lumens were observed by Subharmonic Aided Pressure Estimation (SHAPE) US examination from the US contrast agent perflubutane microbubbles (Sonazoid), which analyzed the clinical parameters of patients, the subharmonic amplitude characteristics of all plaques and lumens, and the parameter differences between the ischemic stroke (IS) group and control group. From May 2021 to February 2022, 46 carotid plaques of 23 patients were included. For plaques, the subharmonic amplitude in the plaque (−60.52 ± 4.46) was lower than that in the opposing level lumen (−56.82 ± 5.68 dB), the subharmonic gradient across the plaque cap was negatively correlated with plaque thickness (r = −0.51, p < 0.001), and with the lumen stenosis severity (r = −0.42, p = 0.003). The median IMT of the IS group was thicker than the control group. The subharmonic gradient of the intraplaque of the IS group was larger than the control group (p = 0.004). In this analysis, we use the receiver operating characteristic (ROC) curve to establish the cutoff value of the difference to predict a new monitoring method for plaque without invasion to predict IS. It still needs a large‐scale study with long‐term follow‐up to validate these findings. [ABSTRACT FROM AUTHOR]

Lymph node metastasis (LNM) is an important factor affecting the prognosis of patients with gastric adenocarcinoma (STAD), which is the most common malignancy of the human digestive system. Current detection techniques have limited sensitivity and specificity, and there is a lack of effective biomarkers to screen for LNM. Therefore, it is critical to screen for biomarkers that predict LNM in STAD. Gene expression differential analysis (false discovery rate < 0.05, |log2Fold change| ≥1.5) was performed on 102 LNM samples, 224 non-LNM samples, and 29 normal gastric tissue samples from The Cancer Genome Atlas (TCGA) STAD dataset, and 269 LNM-specific genes (DEGs) were obtained. Enrichment analysis showed that LNM-specific genes functioned mainly in cytokine-cytokine receptor interactions, calcium signaling, and other pathways. Ten DEGs significantly associated with overall survival in STAD patients were screened by multivariate Cox regression, and an LNM-based 10-mRNA prognostic signature was established (Logrank P < 0.0001). This 10-mRNA signature was well predicted in both the TCGA training set and the Gene Expression Omnibus validation dataset (GSE84437) and was associated with survival in patients with LNM or advanced-stage STAD. Using Kaplan-Meier survival, receiver operating characteristic curve, C-index analysis, and decision curve analysis, the 10-mRNA signature was found to be a more effective predictor of prognosis in STAD patients than the other two reported models (P < 0.0005). Protein-protein interaction network and gene set enrichment analysis of the 10-mRNA signature revealed that the signature may affect the expression of multiple biological pathways and related genes. Finally, the expression levels of prognostic genes in STAD tissues and cell lines were verified using qRT-PCR, Western blot, and the Human Protein Atlas database. Taken together, the prognostic signature constructed in this study may become an indicator for clinical prognostic assessment of LNM-STAD and provide a new strategy for future targeted therapy. [ABSTRACT FROM AUTHOR]

Platinum drugs combined with other agents have been the first-line treatment for non-small cell lung cancer (NSCLC) in the past decades. To better evaluate the efficacy of platinum–based chemotherapy in NSCLC, we establish a platinum chemotherapy response prediction model. Here, a total of 217 samples from Xiangya Hospital of Central South University were selected as the discovery cohort for a genome-wide association analysis (GWAS) to select SNPs. Another 216 samples were genotyped as a validation cohort. In the discovery cohort, using linkage disequilibrium (LD) pruning, we extract a subset that does not contain correlated SNPs. The SNPs with p < 10−3 and p < 10−4 are selected for modeling. Subsequently, we validate our model in the validation cohort. Finally, clinical factors are incorporated into the model. The final model includes four SNPs (rs7463048, rs17176196, rs527646, and rs11134542) as well as two clinical factors that contributed to the efficacy of platinum chemotherapy in NSCLC, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.726. [ABSTRACT FROM AUTHOR]

Background: Few models exist to predict mortality in cancer patients receiving immunotherapy. Our aim was to build a machine learning‐based risk stratification model for predicting mortality in atezolizumab‐treated cancer patients. Methods: Data from 2538 patients in eight atezolizumab‐treated cancer clinical trials across three cancer types (non‐small‐cell lung cancer, bladder transitional cell carcinoma, and renal cell carcinoma) were included. The whole cohort was randomly split into development and validation cohorts in a 7:3 ratio. Machine‐learning algorithms (extreme gradient boosting, random forest, logistic regression with lasso regularization, support vector machine, and K‐nearest neighbor) were applied to develop prediction models. Model performance was mainly assessed by area under the receiver operating characteristic curve (AUC) value, calibration plot, and decision curve analysis. The probability of death risk was then stratified. Results: One thousand and three hundred and seventy‐nine (54.33%) patients died. The random forest (RF) model was overall the best in terms of predictive performance, with the AUC of 0.844 (95% confidence interval [CI]: 0.826–0.862) in the development cohort and 0.786 (95% CI: 0.754–0.818) in the validation cohort for predicting mortality. Twelve baseline variables contributing to mortality prediction in the RF model were C‐reactive protein, PD‐L1 level, cancer type, prior liver metastasis, derived neutrophil‐to‐lymphocyte ratio, alkaline phosphatase, albumin, hemoglobin, white blood cell count, number of metastatic sites, pulse rate, and Eastern Cooperative Oncology Group (ECOG) performance status. A total of 1782 (70.2%) patients were separated into the high‐risk and 756 (29.8%) low‐risk groups. Patients in the high‐risk group were significantly more likely to die, experience disease progression, discontinue study, and discontinue treatment than patients in the low‐risk group (all p values < 0.001). Risk groups were not associated with immune‐related adverse events and grades 3–5 treatment‐related adverse events (all p values > 0.05). Conclusion: RF model has good performance in mortality prediction and risk stratification for cancer patients receiving atezolizumab monotherapy. [ABSTRACT FROM AUTHOR]

Severe fever with thrombocytopenia syndrome (SFTS) is a life‐threatening infectious disease caused by the SFTS virus (SFTSV). This study aimed to evaluate the predictive power of C‐reactive protein to lymphocyte ratio (CLR) and establish an early‐warning model for SFTS mortality. We retrospectively analyzed hospitalized SFTS patients in six clinical centers from May 2011 to 2022. The efficacy of CLR prediction was evaluated by the receiver operating characteristic (ROC) analysis. A nomogram was established and validated. Eight hundred and eighty‐two SFTS patients (median age 64 years, 48.5% male) were enrolled in this study, with a mortality rate of 17.8%. The area under the ROC curve (AUC) of CLR was 0.878 (95% confidence interval [CI]: 0.850−0.903, p < 0.001), which demonstrates high predictive strength. The least absolute shrinkage and selection operator regression selected seven potential predictors. Multivariate logistic regression analysis determined three independent risk factors, including CLR, to construct the nomogram. The performance of the nomogram displayed excellent discrimination and calibration, with significant net benefits in clinical uses. CLR is a brand‐new predictor for SFTS mortality. The nomogram based on CLR can serve as a convenient tool for physicians to identify critical SFTS cases in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Tumor micronecrosis is a histopathological feature predicting poor prognosis in patients with hepatocellular carcinoma (HCC) who underwent liver resection. However, the role of tumor micronecrosis in liver transplantation remains unclear. Methods: We retrospectively reviewed patients with HCC who underwent liver transplantation between January 2015 and December 2021 at our center. We then classified them into micronecrosis(−) and micronecrosis(+) groups and compared their recurrence-free survival (RFS) and overall survival (OS). We identified independent prognostic factors using Cox regression analysis and calculated the area under the receiver operating characteristic curve (AUC) to evaluate the predictive value of RFS for patients with HCC after liver transplantation. Results: A total of 370 cases with evaluable histological sections were included. Patients of the micronecrosis(+) group had a significantly shorter RFS than those of the micronecrosis(−) group (P = 0.037). Shorter RFS and OS were observed in micronecrosis(+) patients without bridging treatments before liver transplantation (P = 0.002 and P = 0.007), while no differences were detected in those with preoperative antitumor therapies that could cause iatrogenic tumor necrosis. Tumor micronecrosis improved the AUC of Milan criteria (0.77–0.79), the model for end-stage liver disease score (0.70–0.76), and serum alpha-fetoprotein (0.63–0.71) for the prediction of prognosis after liver transplantation. Conclusion: Patients with HCC with tumor micronecrosis suffer from a worse prognosis than those without this feature. Tumor micronecrosis can help predict RFS after liver transplantation. Therefore, patients with HCC with tumor micronecrosis should be treated with adjuvant therapy and closely followed after liver transplantation. Clinical trials registration: Not Applicable. [ABSTRACT FROM AUTHOR]

Background: The pathogenesis of pulmonary arterial hypertension (PAH) and associated biomarkers remain to be studied. Copper metabolism is an emerging metabolic research direction in many diseases, but its role in PAH is still unclear. Methods: PAH-related datasets were downloaded from the Gene Expression Omnibus database, and 2067 copper metabolism-related genes (CMGs) were obtained from the GeneCards database. Differential expression analysis and the Venn algorithm were used to acquire the differentially expressed CMGs (DE-CMGs). DE-CMGs were then used for the coexpression network construction to screen candidate key genes associated with PAH. Furthermore, the predictive performance of the model was verified by receiver operating characteristic (ROC) analysis, and genes with area under the curve (AUC) values greater than 0.8 were selected as diagnostic genes. Then support vector machine, least absolute shrinkage and selection operator regression, and Venn diagrams were applied to detect biomarkers. Moreover, gene set enrichment analysis was performed to explore the function of the biomarkers, and immune-related analyses were utilized to study the infiltration of immune cells. The drug-gene interaction database was used to predict potential therapeutic drugs for PAH using the biomarkers. Biomarkers expression in clinical samples was verified by real-time quantitative PCR. Results: Four biomarkers (DDIT3, NFKBIA, OSM, and PTGER4) were screened. The ROC analysis showed that the 4 biomarkers performed well (AUCs > 0.7). The high expression groups for the 4 biomarkers were enriched in protein activity-related pathways including protein export, spliceosome and proteasome. Furthermore, 8 immune cell types were significantly different between the two groups, including naive B cells, memory B cells, and resting memory CD4 T cells. Afterward, a gene-drug network was constructed. This network illustrated that STREPTOZOCIN, IBUPROFEN, and CELECOXIB were shared by the PTGER4 and DDIT3. Finally, the results of RT-qPCR in clinical samples further confirmed the results of the public database for the expression of NFKBIA and OSM. Conclusion: In conclusion, four biomarkers (DDIT3, NFKBIA, OSM, and PTGER4) with considerable diagnostic values were identified, and a gene-drug network was further constructed. The results of this study may have significant implications for the development of new diagnostic biomarkers and actionable targets to expand treatment options for PAH patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Perineural invasion (PNI) is present in 17–75% of prostate cancer patients and is an important mechanism for cancer progression, leading to poor prognoses. An optimized preoperative technique is needed to detect PNI in prostate cancer patients and administer the best treatment. The aim of our retrospective study was to develop a model based on high-throughput radiomic features of bi-parametric MRI combined with clinical factors that can predict PNI status in high-grade prostate cancers. In total, 183 high-grade PCa patients were included in this retrospective study, and the radiomics model based on 13 selected features of bi-parametric MRI showed better discrimination than did the conventional model in the test cohort (area under the curve (AUC): 0.908). Discrimination efficiency improved when the radiomics and clinical models were combined (AUC: 0.947). This improved model may help predict PNI in prostate cancer patients and allow more personalized clinical decision-making. Purpose: To explore the role of bi-parametric MRI radiomics features in identifying PNI in high-grade PCa and to further develop a combined nomogram with clinical information. Methods: 183 high-grade PCa patients were included in this retrospective study. Tumor regions of interest (ROIs) were manually delineated on T2WI and DWI images. Radiomics features were extracted from lesion area segmented images obtained. Univariate logistic regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for feature selection. A clinical model, a radiomics model, and a combined model were developed to predict PNI positive. Predictive performance was estimated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: The differential diagnostic efficiency of the clinical model had no statistical difference compared with the radiomics model (area under the curve (AUC) values were 0.766 and 0.823 in the train and test group, respectively). The radiomics model showed better discrimination in both the train cohort and test cohort (train AUC: 0.879 and test AUC: 0.908) than each subcategory image (T2WI train AUC: 0.813 and test AUC: 0.827; DWI train AUC: 0.749 and test AUC: 0.734). The discrimination efficiency improved when combining the radiomics and clinical models (train AUC: 0.906 and test AUC: 0.947). Conclusion: The model including radiomics signatures and clinical factors can accurately predict PNI positive in high-grade PCa patients. [ABSTRACT FROM AUTHOR]

Background: Disagreement in assessments of Ki67 expression based on core‐needle biopsy and matched surgical samples complicates decisions in the treatment of breast cancer. Purpose: To examine whether preoperative breast MRI could be useful in predicting Ki67 discordance between core‐needle biopsy and surgical samples. Study Type: Retrospective. Population: Three hundred and sixty‐five breast cancer patients with MRI scans and having both core‐needle biopsy and surgical samples from 2017 to 2019. Field Strength/Sequence: 3.0 T, T2‐weighted iterative decomposition of water and fat with echo asymmetry and least squares estimation sequence, diffusion‐weighted sequence using b‐values 0/1000, dynamic contrast enhanced image by volume imaged breast assessme NT. Assessment: We collected clinicopathologic variables and preoperative MRI features (tumor size, lesion type, shape of mass, spiculated margin, internal enhancement, peri‐tumoral edema, intra‐tumoral necrosis, multifocal/multicentric, apparent diffusion coefficient [ADC] minimum, ADC mean, ADC maximum, ADC difference). Statistical Tests: K‐means clustering, multivariable logistic regression, receiver operating characteristic curve. Results: Sixty‐one patients showed Ki67 discordance and 304 patients show Ki67 concordance according to our definition using K‐means clustering. Multivariable regression analysis showed that the following parameters were independently associated with Ki67 discordance: peri‐tumoral edema, odds ratio (OR) 2.662, 95% confidence interval (CI) 1.432–4.948; ADCmin ≤ 0.829 × 10−3 mm2/sec, OR 2.180, 95% CI 1.075–4.418; and ADCdiff > 0.317 × 10−3 mm2/sec, OR 3.365, 95% CI 1.698–6.669. This multivariable model resulted in an AUC of 0.758 (95% CI 0.711–0.802) with sensitivity and specificity being 0.803 and 0.621, respectively. Conclusion: Presence of peri‐tumoral edema, smaller ADCmin and greater ADCdiff in preoperative breast MRI may indicate high risk of Ki67 discordance between core‐needle biopsy and surgical samples. For patients with these MRI‐based risk factors, clinicians should not rely on Ki67 assessment only from core‐needle biopsy. [ABSTRACT FROM AUTHOR]

Objectives To evaluate MRI changes to define muscle-lesion specific patterns in patients with antisynthetase syndrome (ASS), and compare them with those in other common idiopathic inflammatory myopathy subtypes. Methods Qualitative and semi-quantitative thigh MRI evaluations were conducted in patients with ASS, DM and immune-mediated necrotizing myopathy (IMNM). Results This study included 51 patients with ASS, 56 with DM and 61 with IMNM. Thigh MRI revealed muscle oedema (62.7%), myofascial oedema (90.2%), subcutaneous-tissue oedema (60.8%) and fatty infiltration of muscles (68.6%) in patients with ASS. Compared with IMNM, ASS and DM were associated with more frequent adductor-muscle relative sparing (40.6% vs 3.6%, P <0.001, and 25.6% vs 3.6%, P <0.001) and subcutaneous-tissue oedema (60.8% vs 23.0%, P <0.001, and 57.1% vs 23.0%, P <0.001). Although ASS and DM exhibited similar oedema patterns, there were certain subtle differences between them. The ASS group was less frequently symmetric (60.6% vs 88.4%, P =0.005, and 60.6% vs 80.0%, P =0.048), but more frequently showed myofascial oedema of the tensor fasciae latae (80.4% vs 48.2%, P <0.001, and 80.4% vs 31.1%, P <0.001) than either the DM or IMNM groups. The receiver operating characteristic curve analysis showed an optimal combination of thigh MRI findings had an area under the curve with 0.893 for diagnosing ASS. Conclusion Thigh MRI in ASS exhibited frequent myofascial oedema. ASS oedema patterns resembled those of DM more than those of IMNM. Bilateral asymmetry, adductor-muscle relative sparing and remarkable myofascial oedema of tensor fasciae latae were the most characteristic ASS imaging findings. [ABSTRACT FROM AUTHOR]

Background. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancers. As cuproptosis, a new cell death mechanism proposed recently, differs from all other known mechanisms regulating cell death, we aimed to create prognostic markers using cuproptosis-related long non-coding ribonucleic acids (RNAs; lncRNAs) and elucidate the molecular mechanism. Methods. Data from transcriptome RNA sequencing of ccRCC samples and the relevant clinical data were downloaded from The Cancer Genome Atlas, and Pearson's correlation analysis was implemented to obtain the cuproptosis-related lncRNAs. Then, univariate Cox, multivariate Cox, and Least Absolute Shrinkage and Selection Operator Cox analyses were performed to construct the risk signatures. The cuproptosis-related lncRNAs predictive signature was evaluated with receiver operating characteristic curves and subgroup analysis. Finally, Gene Set Enrichment Analysis (GSEA), single-sample GSEA (ssGSEA), tumor immune microenvironment (TIME), and immune checkpoints were performed to explore the relationship between immunity and patient prognosis. Results. Five cuproptosis-related lncRNAs, including FOXD2-AS1, LINC00460, AC091212.1, AC007365.1, and AC026401.3, were used to construct the signature. In the training and test sets, low-risk groups (as identified by a risk score lower than the median) demonstrated a better prognosis with an area under the curve for 1-, 3-, and 5-year survival being 0.793, 0.716, and 0.719, respectively. GSEA analysis suggested significant enrichment of the tricarboxylic acid cycle and metabolism-related pathways in the low-risk group. Besides, both ssGSEA and TIME suggested that the high-risk group exhibited more active immune infiltration. Conclusion. We proposed a cuproptosis-related lncRNAs signature, which had the potential for prognoses and prediction. Our findings might contribute to elucidating potential genomic biomarkers and targets for future therapies in the cuproptosis-related signaling pathways. [ABSTRACT FROM AUTHOR]

The diagnostic value of procalcitonin (PCT) in patients undergoing hemodialysis (HD) remains unclear. We searched multiple databases (PubMed, EMBASE, and Cochrane Library) for studies published through August 2021 that evaluated the diagnostic performance of PCT in patients undergoing HD and having suspected bacterial infections. The bivariate fixed effects model was used to calculate pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and summary receiver operating characteristic (SROC) curves. We identified a total of 1799 studies, of which seven diagnostic studies comprised 1444 patients and 430 bacterial infection episodes. Bivariate pooled sensitivity and specificity for PCT were 0.90 (95% CI: 0.85–0.94) and 0.83 (95% CI: 0.56–0.95), respectively. Furthermore, pooled DOR, PLR, NLR, and area under the curve (AUC) were 47 (95% CI: 11–209), 5.4 (95% CI: 1.7–16.9), 0.12 (95% CI: 0.07–0.20), and 0.92 (95% CI: 0.90–0.94), respectively. We also compared the diagnostic accuracy of PCT and C-reactive protein (CRP), and our results showed that the diagnostic accuracy parameters for PCT were significantly higher than those for CRP. PCT is a useful marker for diagnosis of bacterial infections in patients undergoing HD at a cutoff value of 1.5 ng/ml. [ABSTRACT FROM AUTHOR]

C‐reactive protein‐to‐albumin ratio (CAR) can be used to assess the prognosis of various diseases. This study aimed to evaluate the relationship between CAR on the prognosis of patients with severe fever with thrombocytopenia syndrome (SFTS). This study included 155 SFTS patients from the Public Health Clinical Center of Dalian from January to December 2021. They were divided into survival and deceased groups based on the clinical prognosis. The independent risk factors for poor prognosis of SFTS patients at an early stage were determined by Cox regression. The efficacy of CAR prediction was assessed by the receiver operating characteristic (ROC) curve. A total of 155 patients were included in this study, with an average age of 61.98± 11.70 years, including 77 males and 65 females. The mortality rate of the patients enrolled in this study was 14.19%. Multivariate Cox regression indicated that CAR (hazard ratio = 2.585, 95% confidence interval [CI] 1.405–4.753, p = 0.002) could be an independent predictor for prognosis in SFTS patients at an early stage. CAR had an AUC of 0.781 (95% CI, 0.665–0.898, p = 0.000), a cutoff value of 0.57, a sensitivity of 0.77, and a specificity of 0.80, with better predictive efficacy, compared to neutrophil‐to‐lymphocyte ratio (NLR). High levels of CAR are associated with poor prognosis in SFTS patients, and CAR can be used as an independent predictor for SFTS patients. [ABSTRACT FROM AUTHOR]

Background and Objectives: The histopathological and clinical conditions for transforming peri-implant mucositis into peri-implantitis (PI) are not fully clarified. We aim to uncover molecular mechanisms and new potential biomarkers of PI. Materials and Methods: Raw GSE33774 and GSE57631 datasets were obtained from the Gene Expression Omnibus (GEO) database. The linear models for microarray data (LIMMA) package in R software completes differentially expressed genes (DEGs). We conducted a weighted gene co-expression network analysis (WGCNA) on the top 25% of altered genes and identified the key modules associated with the clinical features of PI. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the R software. We constructed a protein–protein interaction (PPI) network through the STRING database. After that we used Cytohubba plug-ins of Cytoscape to screen out the potential hub genes, which were subsequently verified via receiver operating characteristic (ROC) curves in another dataset, GSE178351, and revalidation of genes through the DisGeNET database. Results: We discovered 632 DEGs (570 upregulated genes and 62 downregulated genes). A total of eight modules were screened by WGCNA, among which the turquoise module was most correlated with PI. The Cytohubba plug-ins were used for filtering hub genes, which are highly linked with PI development, from the candidate genes in the protein–protein interaction (PPI) network. Conclusions: We found five key genes from PI using WGCNA. Among them, ICAM1, CXCL1, and JUN are worthy of further study of new target genes, providing the theoretical basis for further exploration of the occurrence and development mechanism of PI. [ABSTRACT FROM AUTHOR]

To evaluate the value of texture analysis based on dynamic contrast enhanced MRI (DCE-MRI) in the differential diagnosis of thymic carcinoma and thymic lymphoma. Sixty-nine patients with pathologically confirmed (thymic carcinoma, n = 32; thymic lymphoma, n = 37) were enrolled in this retrospective study. Ktrans, Kep and Ve maps were automatically generated, and texture features were extracted, including mean, median, 5th/95th percentile, skewness, kurtosis, diff-variance, diff-entropy, contrast and entropy. The differences in parameters between the two groups were compared and the diagnostic efficacy was calculated. The Ktrans-related significant features yielded an area under the curve (AUC) of 0.769 (sensitivity 90.6%, specificity 51.4%) for the differentiation between thymic carcinoma and thymic lymphoma. The Kep-related significant features yielded an AUC of 0.780 (sensitivity 87.5%, specificity 62.2%). The Ve-related significant features yielded an AUC of 0.807 (sensitivity 75.0%, specificity 78.4%). The combination of DCE-MRI textural features yielded an AUC of 0.962 (sensitivity 93.8%, specificity 89.2%). Five parameters were screened out, including age, Ktrans-entropy, Kep-entropy, Ve-entropy, and Ve-P95. The combination of these five parameters yielded the best discrimination efficiency (AUC of 0.943, 93.7% sensitivity, 81.1% specificity). Texture analysis of DCE-MRI may be helpful to distinguish thymic carcinoma from thymic lymphoma. [ABSTRACT FROM AUTHOR]

Proteins maintain the functional order of cell in life by interacting with other proteins. Determination of protein complex structural information gives biological insights for the research of diseases and drugs. Recently, a breakthrough has been made in protein monomer structure prediction. However, due to the limited number of the known protein structure and homologous sequences of complexes, the prediction of residue–residue contacts on hetero-dimer interfaces is still a challenge. In this study, we have developed a deep learning framework for inferring inter-protein residue contacts from sequential information, called HDIContact. We utilized transfer learning strategy to produce Multiple Sequence Alignment (MSA) two-dimensional (2D) embedding based on patterns of concatenated MSA, which could reduce the influence of noise on MSA caused by mismatched sequences or less homology. For MSA 2D embedding, HDIContact took advantage of Bi-directional Long Short-Term Memory (BiLSTM) with two-channel to capture 2D context of residue pairs. Our comprehensive assessment on the Escherichia coli (E. coli) test dataset showed that HDIContact outperformed other state-of-the-art methods, with top precision of 65.96%, the Area Under the Receiver Operating Characteristic curve (AUROC) of 83.08% and the Area Under the Precision Recall curve (AUPR) of 25.02%. In addition, we analyzed the potential of HDIContact for human–virus protein–protein complexes, by achieving top five precision of 80% on O75475-P04584 related to Human Immunodeficiency Virus. All experiments indicated that our method was a valuable technical tool for predicting inter-protein residue contacts, which would be helpful for understanding protein–protein interaction mechanisms. [ABSTRACT FROM AUTHOR]

The centrosome plays a central role for cellular signaling and is critical for several fundamental cellular processes in human cells. Centrosome abnormalities have been linked to multiple solid tumors and hematological malignancies. We sought to explore the potential role of the DNA methylation, a critical epigenetic modification, of centrosome-related genes in different cancers. The 450K array DNA methylation data and RNA-seq data were downloaded for ~4000 tumor samples and ~500 normal controls from The Cancer Genome Atlas (TCGA) project, covering 11 major cancer types. Cancers with more than 30 normal controls were retained for analysis. Differentially modified CpGs of centrosome genes were identified, and cancer-specific epigenetic models were developed using a machine-learning algorithm for each cancer type. The association between the methylation level of differential CpGs and the corresponding gene expression, as well as the co-localization of the differential CpGs and cis-regulatory elements were evaluated. In total, 2761 CpGs located on 160 centrosome genes for 6 cancers were included in the analysis. Cancer-specific models demonstrated a high accuracy in terms of the area under the receiver operating characteristic (ROC) curve (AUC > 0.9) in five cancers and showed tissue specificity. This study enhanced our understanding of the epigenetic mechanisms underlying the DNA methylation of centrosome-related genes in cancers, and showed the potential of these epigenetic modifications as novel cancer biomarkers. [ABSTRACT FROM AUTHOR]

Background: The objective of this work was to discriminate between primary breast lymphoma (PBL) and breast cancer by systematically analyzing clinical characteristics, laboratory examination results, ultrasound features, and mammography findings to establish a diagnostic model for PBL and to analyze the influence of surgical treatment on the prognosis of PBL patients. Method: We analyzed 20 PBL and 70 breast cancer patients treated during the same period by comparing several characteristics: clinical features, such as age, tumor position, and breast complaints; laboratory examination findings, such as the lactate dehydrogenase (LDH) level, and imaging features such as the maximum diameter, shape, margins, aspect ratio, and calcification of the mass and axillary lymph node involvement. A diagnostic model was then developed using logistic regression analysis. The impact of surgery on the prognosis of PBL patients was assessed through Kaplan–Meier survival analysis. Result: Breast cancer and PBL could be distinguished based on imaging features, including the maximum diameter, shape, margin, and calcification of the mass, and lymph node involvement (P < 0.05). There were no significant differences between PBL and breast cancer patients in terms of clinical features, or the LDH level. The area under the receiver operating characteristic curve was 0.821. The log-rank test showed that surgery had no significant influence on the prognosis of PBL patients. Conclusion: Ultrasound and mammography are the most useful methods for detecting malignant breast tumors. Compared with breast cancer tumors, breast lymphoma tumors are larger with a more regular shape and less calcification and are often accompanied by axillary lymph node involvement. Patients with a breast malignancy should not undergo surgical excision without an accurate diagnosis. [ABSTRACT FROM AUTHOR]

Background: Most prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients. Methods: Prognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan–Meier (K–M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model. Results: A total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues. Conclusion: A credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients. [ABSTRACT FROM AUTHOR]

Objective: Alzheimer's Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD. Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests. Results: 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment. Conclusion: Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD. [ABSTRACT FROM AUTHOR]

Objective: Cervical microbial community in the cervical intraepithelial neoplasia and cervical cancer patients was analysed to study its composition, diversity and signalling pathways by high-throughput 16S rDNA sequencing,and the candidate genes associated with occurrence and progression of cervical intraepithelial neoplasia were screened out and the model was established to predict the evolution of cervical intraepithelial neoplasia malignant transformation from the cervical microbial genes aspect.Methods: Cervical tissues of normal, cervical intraepithelial neoplasia and cervical cancer patients without receiving any treatment were collected. The correlation between candidate genes and cervical intraepithelial neoplasia progression was initially determined by analyzing the microbial flora. Real-time fluorescence quantitative PCR was used to detect the expression of candidate genes in different cervical tissues, ROC curve and logistic regression was used to analyse and predict the risk factors related to the occurrence and progression of cervical intraepithelial neoplasia. Finally, the early warning model of cervical intraepithelial neoplasia occurrence and progression is established.Results: Cervical tissues from normal, cervical intraepithelial neoplasia and cervical cancer patients were collected for microbial community high-throughput 16S rDNA sequencing. The analysis revealed five different pathways related to cervical intraepithelial neoplasia. 10 candidate genes were selected by further bioinformatics analysis and preliminary screening. Real time PCR, ROC curve and Logistic regression analysis showed that human papillomavirus infection, TCT severity, ABCG2, TDG, PCNA were independent risk factors for cervical intraepithelial neoplasia. We used these indicators to establish a random forest model. Seven models were built through different combinations. The model 4 (ABCG2 + PCNA + TDG) was the best early warning model for the occurrence and progression of CIN.Conclusions: A total of 5 differential pathways and 10 candidate genes related to occurrence and progression of cervical intraepithelial neoplasia were found in cervical microbial community. This study firstly identified the genes from cervical microbial community that play an important role in the occurrence and progression of cervical intraepithelial neoplasia. At the same time, the early warning model including ABCG2 + PCNA+TDG genes provided a new idea and target for clinical prediction and blocking the evolution of cervical intraepithelial neoplasia malignant transformation from the aspect of cervical microbiological related genes. [ABSTRACT FROM AUTHOR]

Objective : Amphetamine-type stimulant (ATS) and opioid dependencies are chronic inflammatory diseases with similar symptoms and common genomics. However, their coexpressive genes have not been thoroughly investigated. We aimed to identify and verify the coexpressive hub genes and pathway involved in the pathogenesis of ATS and opioid dependencies. Methods : The microarray of ATS- and opioid-treatment mouse models was obtained from the Gene Expression Omnibus database. GEO2R and Venn diagram were performed to identify differentially expressed genes (DEGs) and coexpressive DEGs (CDEGs). Functional annotation and protein–protein interaction network detected the potential functions. The hub genes were screened using the CytoHubba and MCODE plugin with different algorithms, and further validated by receiver operating characteristic analysis in the GSE15774 database. We also validated the hub genes mRNA levels in BV2 cells using qPCR. Result : Forty-four CDEGs were identified between ATS and opioid databases, which were prominently enriched in the PI3K/Akt signaling pathway. The top 10 hub genes were mainly enriched in apoptotic process (CD44, Dusp1, Sgk1, and Hspa1b), neuron differentiation, migration, and proliferation (Nr4a2 and Ddit4), response to external stimulation (Fos and Cdkn1a), and transcriptional regulation (Nr4a2 and Npas4). Receiver operating characteristic (ROC) analysis found that six hub genes (Fos, Dusp1, Sgk1, Ddit4, Cdkn1a, and Nr4a2) have an area under the curve (AUC) of more than 0.70 in GSE15774. The mRNA levels of Fos, Dusp1, Sgk1, Ddit4, Cdkn1a, PI3K, and Akt in BV2 cells and GSE15774 with METH and heroin treatments were higher than those of controls. However, the Nr4a2 mRNA levels increased in BV2 cells and decreased in the bioinformatic analysis. Conclusions: The identification of hub genes was associated with ATS and opioid dependencies, which were involved in apoptosis, neuron differentiation, migration, and proliferation. The PI3K/Akt signaling pathway might play a critical role in the pathogenesis of substance dependence. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the most common malignant tumor, and it has a high mortality rate. However, the study of miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer (NSCLC) is insufficient. Therefore, this study explored the differential expression of mRNA and miRNA in the plasma of NSCLC patients.Methods: The Gene Expression Omnibus (GEO) database was used to download microarray datasets, and the differentially expressed miRNAs (DEMs) were analyzed. We predicted transcription factors and target genes of the DEMs by using FunRich software and the TargetScanHuman database, respectively. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for GO annotation and KEGG enrichment analysis of downstream target genes. We constructed protein-protein interaction (PPI) and DEM-hub gene networks using the STRING database and Cytoscape software. The GSE20189 dataset was used to screen out the key hub gene. Using The Cancer Genome Atlas (TCGA) and UALCAN databases to analyze the expression and prognosis of the key hub gene and DEMs. Then, GSE17681 and GSE137140 datasets were used to validate DEMs expression. Finally, the receiver operating characteristic (ROC) curve was used to verify the ability of the DEMs to distinguish lung cancer patients from healthy patients.Results: Four upregulated candidate DEMs (hsa-miR199a-5p, hsa-miR-186-5p, hsa-miR-328-3p, and hsa-let-7d-3p) were screened from 3 databases, and 6 upstream transcription factors and 2253 downstream target genes were predicted. These genes were mainly enriched in cancer pathways and PI3k-Akt pathways. Among the top 30 hub genes, the expression of KLHL3 was consistent with the GSE20189 dataset. Except for let-7d-3p, the expression of other DEMs and KLHL3 in tissues were consistent with those in plasma. LUSC patients with high let-7d-3p expression had poor overall survival rates (OS). External validation demonstrated that the expression of hsa-miR-199a-5p and hsa-miR-186-5p in peripheral blood of NSCLC patients was higher than the healthy controls. The ROC curve confirmed that the DEMs could better distinguish lung cancer patients from healthy people.Conclusion: The results showed that miR-199a-5p and miR-186-5p may be noninvasive diagnostic biomarkers for NSCLC patients. MiR-199a-5p-KLHL3 may be involved in the occurrence and development of NSCLC. [ABSTRACT FROM AUTHOR]

Introduction: Vitamin D-binding protein (VDBP) is correlated with nonalcoholic fatty liver disease (NAFLD) through the biological functions of regulating plasma vitamin D (VD) level and the inflammatory process. Objective: This study aims to investigate the effects of VD level and VDBP gene polymorphisms on the risk of NAFLD in a Chinese population. Methods: Plasma 25-hydroxyvitamin D3 levels were measured and seven VDBP candidate genetic variants (rs222020, rs2282679, rs4588, rs1155563, rs7041, rs16847024, rs3733359) were genotyped among participants in this case-control study. The control group was frequency-matched to the NAFLD case group by age and gender. Correlation analysis and multiple linear regressions were used to screen determinants of 25-hydroxyvitamin D3 levels. Multivariable unconditional logistic regression was performed to estimate odds ratio (OR) and 95% confidence interval (95% CI). The prediction capability of models containing independent factors was estimated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow test. Results: Age, body mass index, and triacylglycerol were independent factors influencing VD levels. Participants with low VD levels had significantly higher prevalence of NAFLD compared to subjects with normal VD levels (p < 0.001). A low VD level contributed to increased the risk of NAFLD, independent of metabolic factors known to affect VD levels (adjusted OR = 2.282, 95% CI = 1.422–3.661, p = 0.001). Logistic regression analysis showed that individuals carrying rs7041-G allele had a significantly decreased the risk of NAFLD occurrence compared to T allele (additive model: adjusted OR = 0.814, 95% CI = 0.713–0.929, p = 0.002; codominant model: adjusted OR = 0.623, 95% CI = 0.449–0.866, p = 0.005), after adjusting for age, gender, and overweight. Stratification by multiple metabolic disorders did not alter this relationship. Moreover, we developed a simple model including age, gender, metabolic disorders, and VDBP single nucleotide polymorphism (SNP) to assess NAFLD risk, an AUC of which being 0.817, significantly higher than the model not included VDBP SNP, with Hosmer-Lemeshow test fitting well (p = 0.182). Conclusions: Low plasma VD levels may increase susceptibility to NAFLD, while rs7041-G allele in VDBP contributed to a decreased NAFLD risk among Chinese population. The VDBP variant significantly improved the capability for NAFLD risk assessment, which could be used for early screening and management of NAFLD. [ABSTRACT FROM AUTHOR]

Background: SIRT7 has been shown to be expressed in many cancer types, including clear cell renal cell carcinoma (KIRC), but its functional role in this oncogenic context remains to be firmly defined. This study was designed to explore correlations between SIRT7 and KIRC characteristics using the TCGA database. Methods: Relationships between SIRT7 expression and KIRC patient clinicopathological characteristics were assessed through Kruskal–Wallis tests, Wilcoxon signed-rank tests, and logistic regression analyses. Area under the ROC curve (AUC) values were used to assess the prognostic value of SIRT7 as a means of classifying clear cell renal cell carcinoma patients. The functional role of SIRT7 in this cancer type was assessed through GO/KEGG enrichment analyses and immune cell infiltration analyses. Results: In KIRC patients, higher levels of SIRT7 expression were associated with Race, M stage, T stage (all P < 0.05). SIRT7 offered significant diagnostic value in ROC curve analyses (AUC = 0.912), and elevated SIRT7 levels were linked to worse patient overall survival (OS; P < 0.001). The expression of SIRT7 was independently related with KIRC patient OS (HR: 1.827; 95% CI: 1.346– 2.481; P< 0.001). In GO/KEGG analyses, SIRT7 was found to be associated with ubiquitin-mediated proteolysis and nucleotide excision repair. Higher SIRT7 expression was related to the enhanced infiltration of certain immune cells. Conclusion: Increased SIRT7 expression was associated with a worse KIRC patient prognosis, and immune infiltrates, suggesting it may offer value as a prognostic biomarker for this cancer type. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to develop a noninvasive artificial intelligence (AI) model to diagnose signet‐ring cell carcinoma (SRCC) of gastric cancer (GC) and identify patients with SRCC who could benefit from postoperative chemotherapy based on preoperative contrast‐enhanced computed tomography (CT). Methods: A total of 855 GC patients with 855 single GCs were included, of which 249 patients were diagnosed as SRCC by histopathologic examinations. The AI model was generated with clinical, handcrafted radiomic, and deep learning features. Model diagnostic performance was measured by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, while predictive performance was measured by Kaplan–Meier curves. Results: In the test cohort (n = 257), the AUC, sensitivity, and specificity of our AI model for diagnosing SRCC were 0.786 (95% CI: 0.721–0.845), 77.3%, and 69.2%, respectively. For the entire cohort, patients with AI‐predicted high risk had a significantly shorter median OS compared with those with low risk (median overall survival [OS], 38.8 vs. 64.2 months, p = 0.009). Importantly, in pathologically confirmed advanced SRCC patients, AI‐predicted high‐risk status was indicative of a shorter overall survival (median overall survival [OS], 31.0 vs. 54.4 months, p = 0.036) and marked chemotherapy resistance, whereas AI‐predicted low‐risk status had substantial chemotherapy benefit (median OS [without vs. with chemotherapy], 26.0 vs. not reached, p = 0.013). Conclusions: The CT‐based AI model demonstrated good performance for diagnosing SRCC, stratifying patient prognosis, and predicting chemotherapy responses. Advanced SRCC patients with AI‐predicted low‐risk status may benefit substantially from adjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Background: We have previously reported that ferroptosis has an important role in bladder cancer development. In this study, we aimed to further explore the possible predictive ability of ferroptosis-related long non-coding RNAs (lncRNAs) in bladder cancer and their relation with immune microenvironment and immunotherapy response. Materials and methods: The ferroptosis-related lncRNAs were identified by Pearson's correlation analysis. The predictive lncRNA signature was developed by univariate and multivariate regression analyses. Only the main effects of independent variables in multivariate analysis were included in this signature. The TCGA dataset was defined as the training cohort and GEO was the validation cohort in this study. All samples were grouped into a high- or low-risk group depending on risk signature. The prognostic role of lncRNA signature was explored through survival analysis and receiver operating characteristic curve (ROC) analysis in both TCGA and GEO cohorts. Additionally, the independent prognostic ability of the lncRNA signature was confirmed by multivariate independent analysis. Furthermore, the relationship between lncRNAs and immune microenvironment as well as immunotherapy response in bladder cancers was studied. Results: The Kaplan–Meier curves identified significantly poorer overall survival outcomes for high-risk groups in both TCGA (p < 0.001) and GEO (p < 0.001) cohorts. The area under the curve (AUC) during ROC analysis of 1, 3, and 5 years was 0.781 ± 0.046, 0.784 ± 0.027, and 0.817 ± 0.025, respectively, in the TCGA cohort and 0.665 ± 0.177, 0.719 ± 0.068, and 0.791 ± 0.055, respectively, in the GEO cohort. The multivariate independent analysis in TCGA cohort identified age (p = 0.003), stage (p < 0.001), and signature risk score (p < 0.001) as independent risk factors for overall survival. Furthermore, this study demonstrated a significant difference in infiltration levels of various immune cells between high- and low-risk groups. The high risk group tended to have a lower expression of proteins including PD1 (p < 0.01), PD-L1 (p < 0.01), CTLA-4 (p < 0.05), etc. corresponding to various immune checkpoints. Additionally, the immunotherapy trial confirmed that the high-risk group tended to have a poorer treatment response than the low-risk group (p < 0.001). Conclusions: The ferroptosis-related lncRNAs exhibited a good predictive capacity for overall survival in bladder cancer. Additionally, they could be utilized to reveal tumour-immune microenvironment and immunotherapy responses. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To compare the diagnostic value of shear wave elastography (SWE), fine needle aspiration (FNA) and BRAF gene detection (BRAFV600E gene mutation detection) in ACR TI-RADS 4 and 5 thyroid nodules. METHODS: SWE images, FNA cytological results and BRAF detection results of ACR TI-RADS 4 and 5 thyroid nodules confirmed by pathology were analyzed retrospectively. The receiver operating characteristic (ROC) curve was drawn to determine the best cutoff value of SWE Emax. In the combined diagnosis of SWE, FNA and BRAF, firstly, the nodules with BRAF gene mutation were included in the positive ones, secondly, the nodules with benign and malignant FNA were included in the FNA + SWE or FNA + SWE + BRAF negative and positive ones respectively, finally, for FNA uncertain nodules: those whose SWE Emax were less than or equal to the cutoff value were included in FNA + SWE or FNA + SWE + BRAF negative ones, and those whose SWE Emax were greater than the cutoff value were included in positive ones. The diagnostic efficacy of SWE, FNA, SWE + FNA, FNA + BRAF and their combination in ACR TI-RADS 4 and 5 thyroid nodules were compared. RESULTS: The ROC curve showed that the best cutoff value of SWE Emax was 40.9 kpa, and the area under ROC curve (AUC) was 0.842 (0.800∼0.885). The sensitivity, specificity and accuracy of SWE were 76.3% (270/354), 75.5% (80/106) and 76.1% (350/460), respectively. The sensitivity, specificity and accuracy of FNA were 58.2% (206/354), 88.7% (94/106) and 65.2(300/460), respectively. The sensitivity, specificity and accuracy of FNA + BRAF were 95.5% (338/354), 88.7% (94/106) and 93.9% (432/460), respectively. The sensitivity, specificity and accuracy of SWE + FNA were 85.9% (304/354), 98.1% (104/106) and 88.7% (408/460), respectively. The sensitivity, specificity and accuracy of SWE + FNA + BRAF were 98.3% (348/354), 98.1% (104/106) and 98.3% (452/460), respectively. For the diagnostic accuracy, SWE + FNA + BRAF > FNA + BRAF > FNA + SWE > SWE > FNA, the difference was statistically significant (all P > 0.05). CONCLUSIONS: For ACR TI-RADS 4 and 5 thyroid nodules, SWE and FNA have high diagnostic efficiency. For the diagnostic accuracy, FNA + BRAF is better than FNA + SWE. FNA combination with BRAF gene detection further improves the diagnostic sensitivity and accuracy of FNA. The combined application of the three is the best. [ABSTRACT FROM AUTHOR]

• MiR-8078 expression levels are significantly higher in patients with moderate to severe CHD-PAH. • MiR-8078 has both predictive and diagnostic value for CHD-PAH. • MiR-8078 is an independent risk factor for CHD-PAH. • MiR-8078 may participate in the pathogenesis of CHD-PAH by affecting the PASMC proliferation. • The combination of miR-8078 and BNP for the diagnosis of CHD-PAH has higher sensitivity, specificity, and accuracy. This study aimed to analyze the plasma levels of miR-8078 in patients with congenital heart disease-associated pulmonary arterial hypertension (CHD-PAH) and to explore the diagnostic value and potential mechanisms of miR-8078 in CHD-PAH. Plasma samples were collected from 110 patients with congenital heart disease. Subsequently, based on the mean pulmonary artery pressure (PAPm) measured via right heart catheterization, the patients were divided into three groups: no-PAH group (Group W, PAPm < 25 mmHg), mild group (Group M, 25 mmHg ≤ PAPm < 35 mmHg), and moderate-to-severe group (Group H, PAPm ≥ 35 mmHg). The study also involved a control group (Group C) comprised of 40 healthy individuals. The miR-8078 expression levels were determined by means of reverse transcription-polymerase chain reaction (RT-PCR). The target genes and biological functions of miR-8078 were predicted using TargetScan, PicTar, and miRDB software. Statistical analysis was performed to evaluate the correlation between miR-8078 and hemodynamic parameters in CHD-PAH, in addition to its diagnostic value. The plasma miR-8078 expression levels were significantly higher in the moderate-to-severe group when compared with the control group, no-PAH group, and mild group (p < 0.05). Furthermore, the mild group and no-PAH group showed significantly higher miR-8078 expression levels when compared with the control group (p < 0.05). Both results were consistent with the high-throughput sequencing results. KEGG pathway analysis of the miR-8078 target genes revealed associations with morphine addiction, ubiquitin-mediated proteolysis, and parathyroid hormone synthesis and secretion. GO enrichment analysis indicated the involvement of miR-8078 in the regulation of transcription by RNA polymerase II, the positive regulation of stress-activated MAPK cascade, the transmembrane transport of CI- and K+ ions, chromatin organization, and atrioventricular valve morphogenesis. Correlation analysis showed that the miR-8078 expression levels were positively correlated with the pulmonary artery systolic pressure, mean pulmonary artery pressure, and pulmonary vascular resistance (correlation coefficients of 0.404, 0.397, and 0.283, respectively; all p < 0.05). Univariate and multivariate regression analyses revealed plasma miR-8078 (odds ratio: 1.475, 95 % confidence interval: 1.053–2.065, p < 0.05) to be an independent risk factor for CHD-PAH. Receiver operating characteristic curve analysis revealed that the area under the curve (AUC) for miR-8078 alone and for B-type natriuretic peptide alone in diagnosing CHD-PAH was 0.686 and 0.851, respectively, while the AUC for a combined diagnosis was 0.874, which was higher than that associated with the individual diagnoses (p < 0.05). The findings of this study suggest that miR-8078 is upregulated in CHD-PAH, while the results of the bioinformatics analysis indicate its involvement in the pathogenesis of CHD-PAH, suggesting it to be a potential therapeutic target or biomarker. [ABSTRACT FROM AUTHOR]

Background: SPP1 , secreted phosphoprotein 1, is a member of the small integrin-binding ligand N-linked glycoprotein (SIBLING) family. Previous studies have proven SPP1 overexpressed in a variety of cancers and can be identified as a prognostic factor, while no study has explored the function and carcinogenic mechanism of SPP1 in cervical cancer. Methods: We aimed to demonstrate the relationship between SPP1 expression and pan-cancer using The Cancer Genome Atlas (TCGA) database. Next, we validated SPP1 expression of cervical cancer in the Gene Expression Omnibus (GEO) database, including GSE7803, GSE63514, and GSE9750. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of SPP1 as a differentiating factor by the area under curve (AUC) score. Cox regression and logistic regression were performed to evaluate factors associated with prognosis. The SPP1 -binding protein network was built by the STRING tool. Enrichment analysis by the R package clusterProfiler was used to explore potential function of SPP1. The single-sample GSEA (ssGSEA) method from the R package GSVA and TIMER database were used to investigate the association between the immune infiltration level and SPP1 expression in cervical cancer. Results: Pan-cancer data analysis showed that SPP1 expression was higher in most cancer types, including cervical cancer, and we got the same result in the GEO database. The ROC curve suggested that SPP1 could be a potential diagnostic biomarker (AUC = 0.877). High SPP1 expression was associated with poorer overall survival (OS) (P = 0.032). Further enrichment and immune infiltration analysis revealed that high SPP1 expression was correlated with regulating the infiltration level of neutrophil cells and some immune cell types, including macrophage and DC. Conclusion: SPP1 expression was higher in cervical cancer tissues than in normal cervical epithelial tissues. It was significantly associated with poor prognosis and immune cell infiltration. Thus, SPP1 may become a promising prognostic biomarker for cervical cancer patients. [ABSTRACT FROM AUTHOR]