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.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

Aim To investigate the molecular mecha-nisms of the Wenyang Huazhuo Tongluo formula in in-hibiting endothelial-to-mesenchymal transition(En-doMT)of pulmonary microvascular endothelial cells and improving pulmonary microvascular injury in sys-temic sclerosis(SSc).Methods Pulmonary micro-vascular endothelial cells were cultured with serum from SSc patients to establish SSc pulmonary microvas-cular endothelial cells.A hypoxia model was estab-lished in SSc pulmonary microvascular endothelial cells using liquid paraffin sealing,and the cells were treated with the Wenyang Huazhuo Tongluo formula or HIF-1a inhibitor KC7F2.Western blot was used to detect the protein expression levels of VE-cadherin,CD31,vimen-tin,HIF-1α,Foxm1,smad3,Tie-1,and vWF.ELISA was used to measure the concentrations of E-selectin and ICAM-1 in cell culture medium.The luciferase re-porter gene system was used to detect the promoter ac-tivity of the Foxm1 gene.Results Compared to the control group,the levels of VE-cadherin,CD31,HIF-1α,Foxm1,smad3,Tie-1,and vWF significantly de-creased under hypoxic condition,while the levels of vi-mentin,E-selectin,and ICAM-1 significantly in-creased.In addition,the cell morphology exhibited a distinct"spindle-like"myoblast morphology.Treat-ment with the Wenyang Huazhuo Tongluo formula or KC7F2 reversed these changes in protein expression levels and cell morphology induced by hypoxia.Con-clusion The Wenyang Huazhuo Tongluo formula im-proves pulmonary microvascular injury in SSc by inhib-iting the HIF-1a/Foxm1/smad3 pathway-mediated En-doMT of pulmonary microvascular endothelial cells.

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.

Objective To explore establishment and finite element analysis of personalized proximal clavicular anatomical plate screw fixation model.Methods A 40-year-old male healthy volunteer was selected and the finite element analysis modules of 3D reconstruction software Mimics 15.01,Hypermesh 2019 and Abaqus 2020 were used.The finite element model of anatomic plate at the proximal clavicle was established,and a vertical load of 250 N was applied to the distal end of long axis of clavicle about 15 mm,then the overall structure,plate and screw displacement cloud image,Mises stress distribution were ob-served.Results The displacement distribution of the overall structure shows the maximum displacement was distributed on the distal clavicle.Under the four conditions of normal upper limb weight,longitudinal clavicle fracture,oblique fracture and shoulder impact violence during fall,longitudinal clavicle fracture and oblique fracture,the maximum displacement were 1.04 mm,1.03 mm,1.35 mm and 1.33 mm,respectively.The displacement cloud map of titanium alloy steel plate showed the largest displacement was distributed near the distal clavicular bone,and the maximum displacement were 0.89 mm,0.88 mm,1.10 mm and 1.09 mm,respectively.The displacement cloud map of titanium alloy screw showed the largest displacement was distribut-ed at the root of the distal screw,and the maximum displacement were 0.88 mm,0.87 mm,1.08 mm and 1.06 mm,respectively.Mises stress distribution showed the maximum stress was mainly distributed on titanium alloy plates and screws,and the stress on the clavicle was very small.Mises stress distribution cloud showed the maximum Mises stress was distributed at the second row of screw holes near the clavicle,and the maximum Mises stress were 673.1,678.1,648.5,654.4 MPa,respectively.The maximum stresses of titanium alloy screws were 414.5,417.4,415.8 and 419.7 MPa,respectively.Conclusion The biomechan-ical changes of personalized proximal clavicular anatomical plates are demonstrated by using 3D finite element method to pro-vide biomechanical data for personalized proximal clavicular anatomical plates.

Objective To evaluate the clinical effect of a new three-phase Chinese medicine(CM)external treatment for acute lateral ankle ligament injuries.Methods From July to December 2023,64 patients with acute lateral ankle ligament in-juries were randomly assigned to receive either the new three-phase CM external treatment combined with the POLICE(pro-tect,optimal loading,ice,compression,elevation)treatment(observation group)or the POLICE treatment(control group),with 32 cases in each group.The observation group consisted of 17 males and 15 females,with an average age of(30.59±3.10)years old ranging from 25 to 36 years old,while the control group included 14 males and 18 females,with an average age of(30.03±3.19)years old ranging from 24 to 37 years old.Visual analogue scale(VAS)evaluation and Figure of 8 measurement were used to evaluate the degree of ankle joint pain and swelling of the subjects at the initial enrollment and after 1 week and sixth weeks of treatment.At the same time,the American Orthopaedic Foot and Ankle Society(AOFAS)and Karlsson Ankle Function Score System were used to evaluate the improvement of ankle joint function in patients at all stages.MRI imaging was employed to observe the degree of biological healing of the anterior talofibular ligament,with the signal to noise ratio(SNR)in-dicating the level of healing.A lower SNR suggests better ligament healing,as it represents lower water content in the ligament.Results All patients completed a 6-week follow-up.There was no significant difference in VAS,AOFAS score and Karlsson score between the two groups before treatment(P＞0.05).After 1 week and 6 weeks of treatment,the VAS,AOFAS score and Karlsson score of the two groups were significantly improved(P＜0.05).After 1 week of treatment,the VAS score of the obser-vation group(3.21±0.87)was lower than that of the control group(4.21±1.50),and the difference was statistically significant(P＜0.05).After 1 weeks of treatment,the AOFAS and Karlsson scores[(50.84±4.70)points,(49.97±4.00)points]of the ob-servation group were higher than those[(46.91±5.56)points,(46.66±5.36)points]of the control group(P＜0.05).MRI images showed that after 6 weeks of treatment,the SNR value of the observation group was significantly lower than that of the control group,and the difference was statistically significant(SNR of the observation group was 75.25±16.59,the contral gruop was 85.81±15.55),(P＜0.05).Conclusion Compared with the control group,the new three-phase CM external treatment is signifi-cantly effective in reducing pain and swelling,enhancing ligament repair quality,and promoting functional recovery of the an-kle joint in patients with acute lateral malleolar ligament injuries.