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.

PURPOSE: The fixation method commonly employed worldwide for treating unstable fractures of the posterior pelvic ring is the percutaneous iliosacral screw technique. However, prolonged operation time and frequent fluoroscopies result in surgical risks. This study aimed to investigate whether a new triangulation method could reduce operative and fluoroscopy times and increase the accuracy of screw placement. METHODS: This study is a real-world retrospective cohort analysis that examined a patient cohort who underwent percutaneous iliosacral screw fixation between January 1, 2019 and December 31, 2022. Inclusion criteria were patients (1) diagnosed with posterior pelvic ring instability who underwent pelvic fracture closed reduction and percutaneous S1 transverse-penetrating iliosacral screw placement and (2) aged >18 years. Exclusion criteria were: (1) combined proximal femoral fractures, (2) severe soft tissue injury in the surgical area, (3) incomplete imaging data, and (4) declining to provide written informed consent by the patient. The patients were divided into 2 groups according to the screw insertion method: conventional and triangulation methods. Screw placement and fluoroscopy times recorded by the C-arm were compared between the 2 methods. The accuracy of screw placement was evaluated by Smith grading on postoperative CT. Normality tests were conducted to assess the distribution of the quantitative variables and the Chi-square test was used to compare the qualitative variables. RESULTS: The study included a total of 94 patients diagnosed with posterior pelvic ring instability, who underwent percutaneous iliosacral screw placement. The patients were divided into 2 groups: 46 patients treated with the conventional surgical method and 48 patients received the triangulation method. The operation time (61.13±9.69 vs. 35.77±6.27) min and fluoroscopy frequency times (52.15±9.29 vs. 24.40±4.04) of the triangulation method were significantly reduced (p<0.001). CONCLUSIONS The use of a triangular positioning technique for the surface positioning of percutaneous iliosacral screws could reduce the operative time and fluoroscopy frequency. And the screw placement accuracy using this new method was comparable to that using other conventional methods.
Humans ; Retrospective Studies ; Bone Screws ; Pelvic Bones/surgery* ; Male ; Female ; Fracture Fixation, Internal/methods* ; Fractures, Bone/surgery* ; Adult ; Middle Aged ; Fluoroscopy ; Aged ; Sacrum/surgery* ; Operative Time

OBJECTIVE: To evaluate the efficacy and safety of Shexiang Tongxin Dropping Pill (STDP) in treating stable angina patients with phlegm-heat and blood-stasis syndrome by exercise duration and metabolic equivalents. METHODS: This multicenter, randomized, double-blind, placebo-controlled clinical trial enrolled stable angina patients with phlegm-heat and blood-stasis syndrome from 22 hospitals. They were randomized 1:1 to STDP (35 mg/pill, 6 pills per day) or placebo for 56 days. The primary outcome was the exercise duration and metabolic equivalents (METs) assessed by the standard Bruce exercise treadmill test after 56 days of treatment. The secondary outcomes included the total angina symptom score, Chinese medicine (CM) symptom scores, Seattle Angina Questionnaire (SAQ) scores, changes in ST-T on electrocardiogram and adverse events (AEs). RESULTS: This trial enrolled 309 patients, including 155 and 154 in the STDP and placebo groups, respectively. STDP significantly prolonged exercise duration with an increase of 51.0 s, compared to a decrease of 12.0 s with placebo (change rate: -11.1% vs. 3.2%, P<0.01). The increase in METs was significantly greater in the STDP group than in the placebo group (change: -0.4 vs. 0.0, change rate: -5.0% vs. 0.0%, P<0.01). The improvement of total angina symptom scores (25.0% vs. 0.0%), CM symptom scores (38.7% vs. 11.8%), reduction of nitroglycerin consumption (100.0% vs. 11.3%), and all domains of SAQ, were significantly greater with STDP than placebo (all P<0.01). The changes in Q-T intervals at 28 and 56 days from baseline were similar between the two groups (both P>0.05). Twenty-five participants (16.3%) with STDP and 16 (10.5%) with placebo experienced AEs (P=0.131), with no serious AEs observed. CONCLUSION STDP could improve exercise tolerance in patients with stable angina and phlegm-heat and blood stasis syndrome, with a favorable safety profile. (Registration No. ChiCTR-IPR-15006020).
Humans ; Double-Blind Method ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Middle Aged ; Angina, Stable/physiopathology* ; Aged ; Syndrome ; Treatment Outcome ; Placebos ; Tablets

Numerous studies on the formation and consolidation of memory have shown that memory processes are characterized by phase-dependent and dynamic regulation. Memory retrieval, as the only representation of memory content and an active form of memory processing that induces memory reconsolidation, has attracted increasing attention in recent years. Although the molecular mechanisms specific to memory retrieval-induced reconsolidation have been gradually revealed, an understanding of the time-dependent regulatory mechanisms of this process is still lacking. In this study, we applied a transcriptome analysis of memory retrieval at different time points in the recent memory stage. Differential expression analysis and Short Time-series Expression Miner (STEM) depicting temporal gene expression patterns indicated that most differential gene expression occurred at 48 h, and the STEM cluster showing the greatest transcriptional upregulation at 48 h demonstrated the most significant difference. We then screened the differentially-expressed genes associated with that met the expression patterns of those cluster-identified genes that have been reported to be involved in learning and memory processes in addition to dipeptidyl peptidase 9 (DPP9). Further quantitative polymerase chain reaction verification and pharmacological intervention suggested that DPP9 is involved in 48-h fear memory retrieval and viral vector-mediated overexpression of DPP9 countered the 48-h retrieval-induced attenuation of fear memory. Taken together, our findings suggest that temporal gene expression patterns are induced by recent memory retrieval and provide hitherto undocumented evidence of the role of DPP9 in the retrieval-induced reconsolidation of fear memory.
Animals ; Fear/physiology* ; Male ; Dipeptidyl-Peptidases and Tripeptidyl-Peptidases/genetics* ; Memory Consolidation/physiology* ; Time Factors ; Mental Recall/drug effects* ; Mice ; Gene Expression Profiling

Objective To observe the clinical efficacy of thumb-tack needling for subcutaeous embedding combined with joint mobilization in the treatment of post-stroke shoulder-hand syndrome.Methods A total of 80 patients with post-stroke shoulder-hand syndrome were randomly divided into a treatment group and a control group,with 40 patients in each group.Both groups were given arthrocentesis,the control group was given ordinary acupuncture on the basis of arthrocentesis,and the treatment group was combined with thumb-tack needling for subcutaeous embedding.One course of treatment was 4 weeks and a total of 4 weeks of treatment was given.After 1 month of treatment,the clinical efficacy of the two groups was evaluated.The changes of Visual Analogue Scale(VAS)of pain scores and simplified Fugl-Meyer Assessment(FMA)scores,as well as the pain-free passive forward flexion and abduction of the shoulder joint of the affected limb were observed before and after treatment.The Simple Quality of Life Scale(SF-36)scores of the patients in the two groups were compared after treatment.The safety and the occurrence of adverse reactions in the two groups were also evaluated.Results(1)The total effective rate was 95.00%(38/40)in the treatment group and 80.00%(32/40)in the control group.The efficacy of the treatment group was superior to that of the control group,and the difference was statistically significant(P＜0.05).(2)After treatment,the VAS scores and upper extremity FMA scores of the patients in the two groups were significantly improved(P＜0.05),and the treatment group was significantly superior to the control group in improving the VAS scores and upper extremity FMA scores,and the differences were statistically significant(P＜0.05).(3)After treatment,the joint mobility of patients in the two groups were significantly improved(P＜0.05),and the improvement of shoulder joint movement in the treatment group was superior to that in the control group,and the difference was statistically significant(P＜0.05).(4)After treatment,the SF-36 Quality of Life Scale scores of the treatment group were significantly superior to those of the control group in terms of physical function,psychological function,emotional health,and social function levels,and the difference was statistically significant(P＜0.05).(5)There was no significant difference in the incidence of adverse reactions between the treatment group and the control group(P＞0.05).Conclusion Thumb-tack needling for subcutaeous embedding combined with joint mobilization exert certain effect in the treatment of post-stroke shoulder-hand syndrome.It can significantly improve the pain symptoms of patients,thus improving their quality of life,and the clinical effect is remarkable.

The medical-industrial fusion training model combines the knowledge and technology of medical and engineering disciplines in the training of oncology graduate students, which can help accurate diagnosis and treatment of tumors, promote cooperation and innovation in oncology research, as well as promote the cultivation and exchanges of composite and innovative medical talents in oncology, promote the innovation and development of oncology diagnostic and treatment technology, and improve the survival rate and quality of life of oncology patients. This paper discusses the application of medical-industrial fusion training model in the training of o ncology professionals, and explores the new teaching mode of medical-industrial fusion thinking in the cultivation of complex and innovative medical talents in oncology under the background of "new medical science".