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.

Aim To establish a gait analysis system based on DeepLabCut(DLC)algorithm for evaluating motor function in aged mice.Methods Based on DLC algorithm in deep learning technology,treadmill device and fully closed design were used in the system,including software and hardware.This system was applied to evaluate gait characteristics of mice due to aging un-der different movement modes.Correlation analysis was used to explore the effects of body weight and body length on gait indica-tors.Results This system realized the synchronous analysis of three-dimensional gait(lateral and ventral plane)of mice at specific gait speed,and automatically quantified 47 gait indica-tors.Using this system,it was found that during walking(15 cm·s-1),the standard deviation of body turning angle decreased,forelimb sway duration,standard deviation of knee angle,mean outward angles of left and right hind paw increased in 8 and 15 month-old mice,compared with 2-month-old mice.However,15-month-old mice showed decreased walking frequency,and in-creased stride width,total duration of double support,and knee extension and contraction distance.In addition,at trot(20 cm·s-1),15-month-old mice were unable to walk steadily,and 8-month-old mice had increased total duration of double support and mean outward angles of left hind paw,compared with 2-month-old mice.Correlation analysis revealed that indicators like walking frequency,stride width,forelimb sway duration,total duration of double support,standard deviation of knee an-gle,knee extension and contraction distance,were not affected by changes in body weight and body length.Conclusions The gait analysis system based on DLC algorithm can achieve a more sensitive,accurate and comprehensive evaluation of the gait of aged mice,distinguishing the gait characteristics of aged mice to maintain gait stability,and selecting behavioral indicators that better reflect the gait changes of aged mice.It provides a meth-odological basis for more effective assessment of efficacy and side effects of drugs for anti-aging and anti-decline of motor coordina-tion in the future.

Objective To explore feasibility of 3D metal printing technology combined with virtual design proximal clavicle anatomical plate.Methods A 52-year-old male healthy volunteer was retrospectively selected to design proximal clavicle anatomical plate system by using Mimics15.01,NX12.0 and other software.STL data were input into 3D printer to print 1∶1 clavicle model and proximal clavicle anatomical plate.The fit of the plate was tested in vitro and the accuracy of screw position was evaluated by imaging.Printing time of model,nail path design and fabrication time of the anatomical plate at proximal clavicle were recorded.Results The 3D metal printing proximal clavicle anatomical plate fitted well to clavicle model,orienta-tion of proximal clavicle locking screw was accurate,and X-ray and CT scan showed the screw position was good.Printing time of model,the time of nail path design,and the time of making anatomical plate of proximal clavicle were 120,15 and 300 min respectively.Conclusion The proximal clavicular anatomical plate system based on 3D metal printing technology could achieve good lamination of proximal clavicular fracture plate and precise screw placement,providing a new and accurate surgical method for the treatment of the proximal clavicular fracture.

The main components and working principle of the negative pressure isolation treatment equipment were described,and the classification and application scopes of the negative pressure isolation treatment equipment from foreign countries and China were reviewed.The structure,key technical parameters and characteristics of different types of negative pressure isolation treatment equipment were analyzed under different conditions.The problems of the negative pressure isolation treatment equipment were analyzed,and it's pointed out it would be enhanced in component simplification,comfort,intelligence and multifunctionality.[Chinese Medical Equipment Journal,2024,45(2):97-104]

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Aim To investigate the role of metabolites of eicosapentaenoic acid (EPA) in promoting the transdifferentiation of pancreatic α cells to β cells. Methods Male C57BL/6J mice were injected intraperitoneally with 60 mg/kg streptozocin (STZ) for five consecutive days to establish a type 1 diabetes (T1DM) mouse model. After two weeks, they were randomly divided into model groups and 97% EPA diet intervention group, 75% fish oil (50% EPA +25% DHA) diet intervention group, and random blood glucose was detected every week; after the model expired, the regeneration of pancreatic β cells in mouse pancreas was observed by immunofluorescence staining. The islets of mice (obtained by crossing GCG