Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To compare the efficacy of renal proximal renal artery denervation(pRDN)and full-length renal artery denervation(fRDN)for treatment of hypertension.Methods Fifty-six hypertensive patients were enrolled and randomly assigned to full-length renal artery denervation group(n=25)and proximal renal artery denervation group(n=31).After the procedure,24-hour ambulatory blood pressure monitoring(24 h-ABPM)at 6 months and office blood pressure at 12 months was recorded for statistical analysis.Results The blood pressure at follow-up reduced significantly in both groups,while there was no significant difference between groups.The baseline office blood pressure in fRDN group and pRDN group was(180±15)/(104±10)mmHg and(180±12)/(103±8)mmHg,respectively,which decreased to(142±9)/(82±7)mmHg and(143±10)/(83±6)mmHg at 12 months postoperatively(P＜0.001 within groups and P＞0.05 between groups).The baseline 24 h-ABPM in the two groups was(162±13)/(95±8)mmHg and(160±12)/(94±8)mmHg,respectively,which decreased to(142±11)/(83±7)mmHg and(141±8)/(81±7)mmHg at 6 months postoperatively(P＜0.001 within groups and P＞0.05 between groups).However,there was no significant difference in the reduction of office blood pressure and ambulatory blood pressure between the two groups.No treatment-related adverse events were observed.Conclusions pRDN has similar antihypertensive effect to fRDN.

Graft-versus-host disease(GVHD)reduces the clinical effect and life quality of patients after allogeneic hematopoietic stem cell transplantation(HSCT).Especially for steroid-resistant GVHD,it becomes essential to explore new prevention and treatment strategies.Rapamycin has shown certain clinical advantages in the prevention and treatment of acute and chronic GVHD by inhibiting the mTOR signal pathway.Furthermore,rapamycin exhibits the ability to regulate cell subsets,including T cells,B cell,dendritic cells and myeloid-derived suppressor cells,which elucidates the mechanism on effective preventing and treating GVHD.This article reviewed the roles of mTOR inhibitor-rapamycin on GVHD,and discussed how to optimize the usage of rapamycin.

China prospective multicenter birth cohort (Prospective Omics Health Atlas birth cohort, POHA birth cohort) study was officially launched in 2022. This study, in collaboration with 12 participating units, aims to establish a high-quality, multidimensional cohort comprising 20 000 naturally conceived families and assisted reproductive families. The study involves long-term follow-up of parents and offspring, with corresponding biological samples collected at key time points. Through multi-omics testing and analysis, the study aims to conduct multi-omics big data research across the entire maternal and infant life cycle. The goal is to identify new biomarkers for maternal and infant diseases and provide scientific evidence for risk prediction related to maternal diseases and neonatal health.

Objective:To investigate the specimen quality of intersphincteric resection with transabdominal transanal mixed approach for rectal cancer in the Chinese Transanal Total Mesorectal Excision Registry Collaborative (CTRC) database.Methods:The retrospective case-control study was conducted. Based on the concept of real-world research, the clinicopathological data of 281 pati-ents with rectal cancer in the CTRC database who underwent intersphincteric resection with trans-abdominal transanal mixed approach in 19 medical centers, including the Beijing Friendship Hospital of Capital Medical University et al, from November 15,2017 to December 31,2023 were collected. There were 196 males and 85 females, aged 61(range, 27-87)years. Observation indicators: (1) preoperative examinations; (2) neoadjuvant therapy; (3) postoperative examinations; (4) analysis of influencing factors for positive circumferential margin in surgical specimen of intersphincteric resec-tion for rectal cancer. Measurement data with normal distribution were represented as Mean±SD. Measurement data with skewed distribution were represented as M(range). Count data were described as absolute numbers or percentages. The chi-square test was used for univariate analysis. Logistic regression model was used for multivariate analysis. Results:(1) Preoperative examinations. Of the 281 patients, 234 cases underwent preoperative pelvic magnetic resonance imaging (MRI) examina-tion. There were 2 cases in clinical stage T0, 3 cases in clinical stage T1, 58 cases in clinical stage T2, 137 cases in clinical stage T3, 24 cases in clinical stage T4, 3 cases in clinical stage Tx, 7 cases missing clinical T staging data. There were 87 cases in clinical stage N0, 68 cases in clinical stage N1, 60 cases in clinical stage N2, 9 cases in clinical stage Nx, 10 cases missing clinical N staging data. There were 30 cases with mesorectal fascia invasion, 53 cases with extramural venous invasion. The distance from lower margin of tumor to anal margin was 41.9(range, 1.0-80.0)mm. (2) Neoadjuvant therapy. Of the 281 patients, 125 cases underwent neoadjuvant therapy, including 39 cases receiving chemo-therapy alone, 6 cases receiving short-course simultaneous chemoradiotherapy, 5 cases receiving short-course simultaneous chemoradiotherapy and delayed surgery, 48 cases receiving long-course simultaneous chemoradiotherapy, 2 cases receiving other treatments, and 25 cases missing neoadju-vant therapy data. (3) Postoperative examinations. Of the 281 patients, 249 cases achieved R 0 resection, 9 cases achieved R 1 resection, and there were 23 cases missing surgical margin data. The maximum tumor diameter, the number of lymph nodes harvested and positive rate of vessel carcinoma embolus were 30.0(range, 0.5-200.0)mm, 13(range, 0-70) and 27.55%(73/265) in 281 patients. There were 252 patients with circumferential margin records, showing positive in 15 cases, with a positive rate as 5.95%(15/252). The minimum distance from deep part of tumor to circumferential margin was 7.0(range, 0-150.0)mm in 252 patients. There were 85 cases with distal margin records, showing positive in 1 case, and the distance from lower margin of tumor to distal margin was 10.0(range, 0-202.0)mm. There were 273 patients with specimen integrity records, which showed intact specimen in 208 cases, fair specimen in 58 cases, poor specimen in 4 cases, unevaluated specimen in 3 cases. There were 7 cases with rectal perforation. Of the 281 patients, cases in pathological stage T0, Tis, T1, T2, T3, T4 were 14, 5, 22, 107, 113, 12, respectively, and there were 8 cases missing pathological T staging data. Of the 281 patients, cases in pathological stage N0, N1a, N1b, N1c, N2a, N2b were 176, 27, 27, 11,20, 12, respectively, and there were 8 cases missing pathological N staging data. Of the 281 patients, there were 4 cases with distant metastasis, 262 cases without distant metastasis, 5 cases not evaluated, and 10 cases missing tumor metastasis data. Of the 125 patients undergoing neoadjuvant therapy, there were 85 cases with tumor regression grade records, including 16 cases as grade 1, 27 cases as grade 2, 19 cases as grade 3, 15 cases as grade 4, 8 cases as grade 5. (4) Analysis of influencing factors for positive circumferential margin in surgical specimen of intersphincteric resection for rectal cancer. Results of univariate analysis showed that preoperative T staging on preoperative pelvic MRI, mesorectal fascia invasion, extramural venous invasion, pathological T staging, and pathological N staging were related factors for positive circumferential margin in surgical specimen of intersphincteric resection for rectal cancer ( P<0.05). Conclusions:Intersph-incteric resection with transabdominal transanal mixed approach has good specimen quality and low positive rate of surgical margin. T staging on preoperative pelvic MRI may be related to positive circumferential margin after intersphincteric resection for rectal cancer.

The biological clock(also known as the circadian rhythm)is the fundamental reliance for all organisms on Earth to adapt and survive in the Earth's rotation environment.Circadian rhythm is the most basic regulatory mechanism of life activities,and plays a key role in maintaining normal physiological and biochemical homeostasis,disease occurrence and treatment.Recent studies have shown that the biologi-cal clock plays an important role in the development of oral tissues and in the occurrence and treatment of oral diseases.Since there is cur-rently no guiding literature on the research methods of biological clock in stomatology,researchers mainly conduct research based on pub-lished references,which has led to controversy about the research methods of biological clock in stomatology,and there are many confusions about how to rationally apply the research methods of circadia rhythms.In view of this,this expert consensus summarizes the characteristics of the biological clock and analyzes the shortcomings of the current biological clock research in stomatology,and organizes relevant experts to summarize and recommend 10 principles as a reference for the rational implementation of the biological clock in stomatology research.