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.

Background Schizophrenic patients have metabolic disorders,impaired language and information processing function.Inflammatory factors may play an important role in the occurrence and development of schizophrenia.Objective To explore the relationship of the inflammatory factor levels with metabolic levels,language fluency and information processing function in patients with schizophrenia,so as to provide references for clinical understanding of the neuropathological mechanisms of schizophrenia.Methods A total of 96 patients with schizophrenia were included in the study group,who were hospitalized in the Fourth People's Hospital of Hefei from January 2021 to December 2022 as well as met the diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders,fifth edition(DSM-5)and Mini-International Neuropsychiatric Interview(MINI)6.0.Meanwhile,population who underwent physical examination at the same hospital were included in the control group(n=42).A high-sensitivity multi factor electrochemiluminescence analyzer was used to detect the levels of inflammatory factors IL-4,IL-5,IL-7,IL-8,IL-10 and IL-13.A fully automated biochemical analyzer was used to detect the levels of metabolic indicators such as fasting blood glucose,triglycerides,high-density lipoprotein,apolipoprotein A,creatinine and urea nitrogen.Verbal fluency and information processing function of all participants were assessed by using Verbal Fluency Test(VFT)and Stroop Color Word Test(SCWT).Results There were statistically significant differences in the levels of IL-4,IL-5,IL-7,IL-8,IL-10,IL-13 and IL-15 between the study group and the control group(P<0.05).There were statistically significant differences in BMI,waist circumference,fasting blood glucose,triglycerides,high-density lipoprotein,urea nitrogen,apolipoprotein A and creatinine levels between the two groups(P<0.05).The differences in the correct number of household appliances,animals,fruits,vegetables,names starting with"water"and"self"in VFT between the two groups were statistically significant(P<0.05).The differences in point reaction time,character reaction time and character color reaction time in SCWT between the two groups were statistically significant(P<0.05).Correlation analysis showed that except for creatinine levels,the levels of IL-4 and IL-5 in patients with schizophrenia were correlated with other indicators(P<0.05).IL-7 levels were correlated with creatinine levels,household appliances,animals,fruits,correct number of names starting with"water"in VFT,point reaction time and word reaction time in SCWT(P<0.05).IL-8 levels were correlated with triglyceride levels,household appliances,animals,fruits,vegetables,correct number of names starting with"water"and"self"in VFT and word reaction time in SCWT(P<0.05).Except for creatinine levels and the correct number of names starting with"self",IL-10 levels were correlated with all other indicators(P<0.05).Except for creatinine and urea nitrogen levels,IL-13 levels were correlated with other indicators(P<0.05).Conclusion The levels of inflammatory factors in patients with schizophrenia may be related to their metabolic levels,language fluency and information processing function.

Objective:To explore the clinical and electrophysiological characteristics of peripheral neuropathy in prediabetic patients.Methods:Subjects aged 20-65 years with high-risk factors of impaired glycemia enrolled in Beijing Tiantan Hospital, Capital Medical University from 2019 to 2022 were recruited to conduct oral glucose tolerance test, after excluding other causes of neuropathy or radiculopathy. Patients with impaired fasting glucose or impaired glucose tolerance were defined by American Diabetes Association criteria. These patients were divided into clinical polyneuropathy (PN) and clinical non-PN groups, according to the 2010 Toronto consensus criteria and the presence of PN symptoms and signs or not. Nerve conduction studies (NCS), F wave, sympathetic skin response (SSR), R-R interval variation (RRIV) and current perception thresholds (CPT) were performed and the abnormal rate was compared between different electrodiagnostic methods and between clinical subgroups.Results:Among the 73 prediabetic patients ultimately enrolled, only 20 (27.4%) can be diagnosed as clinical PN according to the Toronto consensus criteria. The abnormal rate of CPT (68.5%, 50/73) was significantly higher than those of F wave (2.7%, 2/73), lower limb NCS (0, 0/73), upper limb NCS changes of carpal tunnel syndrome (26.0%, 19/73), SSR (6.8%, 5/73) and RRIV (5.5%, 4/73; McNemar test, all P<0.001). With sinusoid-waveform current stimuli at frequencies of 2 000 Hz, 250 Hz and 5 Hz, the CPT device was used to measure cutaneous sensory thresholds of large myelinated, small myelinated and small unmyelinated sensory fibers respectively. CPT revealed a 21.9% (16/73) abnormal rate of unmyelinated C fiber in the hands of prediabetic patients, significantly higher than that of large myelinated Aβ fibers [8.2% (6/73), χ2=5.352, P=0.021]. Both abnormal rates of small myelinated Aδ [42.5% (31/73)] and unmyelinated C fibers [39.7% (29/73)] in the feet of prediabetic patients were significantly higher than that of large myelinated Aβ fibers [11.0% (8/73), χ2=18.508, 15.965, both P<0.001]. Compared with the clinical non-PN group, the abnormal rates of CPT [90.0% (18/20) vs 60.4% (32/53), χ2=5.904, P=0.015] and SSR [20.0% (4/20) vs 1.9% (1/53), P=0.016) were significantly higher in the clinical PN group. Conclusions:Peripheral neuropathies in prediabetic patients are usually asymptomatic or subclinical, and predispose to affect unmyelinated and small myelinated sensory fibers. Selective electrodiagnostic measurements of small fibers help to detect prediabetic neuropathies in the earliest stages of the disease.

Objective To explore the efficacy and safety of recombinant human anti-severe acute respiratory syn-drome coronavirus 2(anti-SARS-CoV-2)monoclonal antibody injection(F61 injection)in the treatment of patients with coronavirus disease 2019(COVID-19)combined with renal damage.Methods Patients with COVID-19 and renal damage who visited the PLA General Hospital from January to February 2023 were selected.Subjects were randomly divided into two groups.Control group was treated with conventional anti-COVID-19 therapy,while trial group was treated with conventional anti-COVID-19 therapy combined with F61 injection.A 15-day follow-up was conducted after drug administration.Clinical symptoms,laboratory tests,electrocardiogram,and chest CT of pa-tients were performed to analyze the efficacy and safety of F61 injection.Results Twelve subjects(7 in trial group and 5 in control group)were included in study.Neither group had any clinical progression or death cases.The ave-rage time for negative conversion of nucleic acid of SARS-CoV-2 in control group and trial group were 3.2 days and 1.57 days(P=0.046),respectively.The scores of COVID-19 related target symptom in the trial group on the 3rd and 5th day after medication were both lower than those of the control group(both P＜0.05).According to the clinical staging and World Health Organization 10-point graded disease progression scale,both groups of subjects improved but didn't show statistical differences(P＞0.05).For safety,trial group didn't present any infusion-re-lated adverse event.Subjects in both groups demonstrated varying degrees of elevated blood glucose,elevated urine glucose,elevated urobilinogen,positive urine casts,and cardiac arrhythmia,but the differences were not statistica-lly significant(all P＞0.05).Conclusion F61 injection has initially demonstrated safety and clinical benefit in trea-ting patients with COVID-19 combined with renal damage.As the domestically produced drug,it has good clinical accessibility and may provide more options for clinical practice.

Objective To explore the potential mechanism of proguanil on the proliferation and apoptosis of bladder cancer cells.Methods 253J cells were randomly divided into control group(normal treatment),proguanil group(42.06 μmol·L-1 proguanil),pcDNA group(transfected with pcDNA+42.06 μmol·L-1 proguanil),FADS2 group[transfected fatty acid desaturase gene 2(FADS2)+42.06 μmol·L-1 proguanil],si-NC(transfection si-NC),si-FADS2(transfection si-FADS2),Ferrostatin-1 group(transfected with si-FADS2+10 μmol·L-1 ferrostatin-1).Real-time fluorescence quantitative polymerase chain reaction(RT-qPCR)assay was used to detect mRNA expression of related genes;Western blot assay was used to detect the expression of each protein;apoptosis was detected by TdT mediated dUDP nick end labeling(Tunel)assay;5-ethynyl-2'-deoxyuridine(EdU)assay to detect cell proliferation;the Transwell assay measures the ability of cells to migrate;Fe2+levels were determined by kit method;DCFH-DA probe was used to detect ROS levels.Results The mRNA levels of FADS2 in control group,proguanil group,pcDNA group and FADS2 group were 1.00±0.11,0.47±0.09,0.49±0.06 and 2.09±0.21,respectively;cell proliferation rate were(100.00±3.50)％,(54.31±4.90)％,(56.46±5.17)％and(78.76±6.50)％,respectively;the apoptosis rate were(3.92±0.53)％,(28.79±3.30)％,(27.20±2.90)％and(7.34±0.68)％,respectively;the migration number were 132.70±9.81,70.10±5.05,68.70±537 and 101.80±11.25,respectively;Fe2+level were(100.00±8.14)％,(201.33±17.84)％,(192.38±21.34)％and(116.70±10.90)％,respectively;GPX4 protein relative expression level were 0.77±0.05,0.31±0.05,0.34±0.05 and 0.68±0.06,respectively.The above indexes in proguanil group were compared with those in control group,the above indexes in FADS2 group were compared with those in pcDNA group,all the differences were statistically significant(all P＜0.05).The ROS levels of si-NC,si-FADS2 and Ferrostatin-1 groups were 9.72±1.18,40.94±5.63 and 13.77±1.40,respectively.Compared the si-FADS2 group with the si-NC group,Ferrostatin-1 group compared with si-FADS2 group,ROS level were significantly different(all P＜0.05).Conclusion Proguanil can induce the apoptosis of bladder cancer cells by inhibiting FADS2 expression mediated by oxidation-reduction driven ferroptosis pathway.

Persistent left superior vena cava(PLSVC)is a common congenital anomaly of systemic venous drainage,often draining into the right atrium without the need for special treatment.Sometimes,PLSVC drains into the left atrium,creating a right-to-left shunt,leading to reduced blood oxygen saturation and paradoxical embolism,requiring intervention.Traditional surgical ligation of PLSVC is the conventional approach for managing abnormal shunting,but it is associated with significant trauma and carries the risk of damaging the phrenic nerve.Here,we present a case of a patient with right heart dysfunction due to an untreated PLSVC-left atrium communication after corrective surgery for complex congenital heart disease,resulting in left-to-right shunting postoperatively.The patient was successfully treated by using a Plug vascular occluder via a transseptal approach to occlude the PLSVC.To our knowledge,this is the first report of successful closure of the left-to-right shunting through the heart chambers via a transseptal approach,indicating that interventional occlusion is an ideal management approach.