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 explore the effects of Lycium berry seed oil on Nrf2/ARE pathway and oxidative damage in testis of subacute aging rats. Methods Fifty out of 60 male SD rats, aged 8 weeks, were subcutaneously injected with 125 mg • kg"D-galactosidase in the neck for 8 weeks to establish a subacute senescent rat model. The presence of senescent cells was observed using P-galactosidase ((3-gal), while testicular morphology was examined using HE staining. Serum levels of testosterone (testosterone, T), follicle-stimulating hormone ( follicle stimulating hormone, FSH ) , luteinizing hormone ( luteinizing hormone, LH ) , superoxide dis-mutase ( superoxide dismutase, SOD ) , glutathione ( glutathione, GSH) and malondialdehyde ( malondial-dehyde, MDA) were measured through ELISA, and the expressions of factors related to aging, oxidative damage, and the Nrf2/ARE pathway were assessed via immunohistochemical analysis and Western blotting. Results After successfully identifying the model, the morphology of the testis was improved and the intervention of Lycium seed oil led to a down-regulation in the expression of [3-gal and -yH2AX. The serum levels of SOD, GSH, T, and FSH increased while MDA and LH decreased (P 0. 05) . Additionally, there was an up-regulated expression of Nrf2, GCLC, NQOl, and SOD2 proteins in testicular tissue ( P 0. 05 ) and nuclear expression of Nrf2 in sertoli cells. Conclusion Lycium barbarum seed oil may reduce oxidative damage in testes of subacute senescent rats by activating the Nrf2/ARE signaling pathway.

BACKGROUND:As a leading technique in the treatment of primary stenosis by posterior spinal endoscopy through unilateral approach and bilateral decompression using single channel endoscopy,the long-term efficacy needs to be further observed.There are few reports on the scope of intraoperative resection and few relevant studies on biomechanics and finite element analysis. OBJECTIVE:A three-dimensional finite element model was established to evaluate the effects of bilateral lumbar canal decompression under a one-hole split endoscope on lumbar range of motion and intradiscal pressure,to provide suggestions for clinical operation and theoretical basis for further clinical research. METHODS:A complete L3-L5 vertebral body model was reconstructed by CT images of nine healthy volunteers,which was used as the preoperative model M1.The simulated surgical resection range of L4-L5 was performed,and 1/4,1/3 and 1/2 of bilateral facet joints were removed respectively to obtain models M2,M3 and M4.The range of motion and the maximum Von Mises stress of the four models were compared in the six directions of forward bending,backward extension,left and right bending,and left and right rotation. RESULTS AND CONCLUSION:(1)The L3-L5 finite element model established in this study was effective,and the range of motion was within the range of previous solid studies under six motion states.(2)Compared with the M1 model,the L4-L5 lumbar spine range of motion increased with the increase of resection range in M2 with M3 and M4 models under forward bending,left and right bending and left and right rotation loading,and the difference was significant(P＜0.05).Under posterior extension loading,there was no significant difference in lumbar range of motion between M1 and M2(P＞0.05),but there was a significant difference of M1,M3 and M4(P＜0.05).(3)The range of motion of the L3-L4 lumbar spine had no significant change with the increase of bilateral facet arthrotomy(P＞0.05).(4)There was a significant difference in the maximum value of L4-L5 Von Mises between M1 and M2(P＜0.05),and there was a significant difference in the maximum value of L4-L5 Von Mises between M1 and M3,M4(P＜0.01),and the maximum value of L4-L5 lumbar von Mises increased with the increasing range of bilateral facet joint resection.Resection of more than 1/3 was particularly obvious.(5)The maximum value of Von Mises in the L3-L4 lumbar spine was increased with the increase of the resection range under forward bending,left and right bending and left and right rotation loading and the difference was significant(P＜0.05).(6)The results exhibited that the L4-L5 lumbar motion and intervertebral disc pressure increased with the increase of the excision range.Intervertebral disc pressure at L3-L4 increased with the increased extent of excision,but the lumbar range of motion was not significantly affected.In conclusion,the stability of the operative segment may be affected by the increase in the scope of facet joint resection.Although the immediate stability of adjacent segments is not affected,it may accelerate disc degeneration.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Objective:To investigate the prevalence of diabetes in the elderly aged ≥60 years in Liaoning Province from 2017 to 2019 and analyze the impact of blood glucose control on all-cause mortality and cardiovascular disease (CVD) mortality.Methods:A survey was conducted in the elderly aged ≥60 years in Liaoning from 2017 to 2019 to collect the information about the prevalence of diabetes and other chronic diseases in the diabetes patients. The mortality of the enrolled subjects was investigated in September 2023. Cox proportional hazards regression models were used to estimate the association between blood glucose control in the elderly with diabetes and the risks of all-cause mortality and CVD mortality.Results:The crude prevalence of diabetes in the elderly aged ≥60 years was 20.2% (2 014/9 958) in Liaoning from 2017 to 2019, and the standardized prevalence rate was 19.9%. The prevalence rates of hypertension, dyslipidemia, and overweight/obesity in the diabetes patients were 77.0%, 51.7%, and 67.5% respectively. The median follow-up time was 5.5 years, and the all-cause mortality and CVD mortality rates in the diabetes patients were 244.3/10 000 person-years and 142.9/10 000 person-years, respectively. The results of the Cox proportional hazards regression model analysis showed that compared with non-diabetic individuals, diabetes patients had an increased risk of all-cause mortality by 1.68 times [hazard ratio ( HR)=1.68, 95% CI: 1.44-1.94] and an increased risk of CVD mortality by 1.56 times ( HR=1.56, 95% CI: 1.29-1.89). The differences in risks of all-cause mortality and CVD mortality between the diabetes patients with normal fasting blood glucose and glycated hemoglobin levels and people without diabetes were not significant (all P>0.05). The failure to meet either the FPG or HbA1c target increased the risk of all-cause mortality (all P<0.05). For individuals who failed to meet the HbA1c target, there was an increased risk of CVD mortality (all P<0.05). Conclusions:The comorbidity rate of chronic diseases was higher in the elderly with diabetes than in the elderly without diabetes in Liaoning. Elderly diabetes patients can benefit from good blood glucose control.

Objective:To investigate the current status of endoscopic treatment for gastroesophageal varices in portal hypertension in China, and to provide supporting data and reference for the development of endoscopic treatment.Methods:In this study, initiated by the Liver Health Consortium in China (CHESS), a questionnaire was designed and distributed online to investigate the basic condition of endoscopic treatment for gastroesophageal varices in portal hypertension in 2022 in China. Questions included annual number and indication of endoscopic procedures, adherence to guideline for preventing esophagogastric variceal bleeding (EGVB), management and timing of emergent EGVB, management of gastric and isolated varices, and improvement of endoscopic treatment. Proportions of hospitals concerning therapeutic choices to all participant hospitals were calculated. Guideline adherence between secondary and tertiary hospitals were compared by using Chi-square test.Results:A total of 836 hospitals from 31 provinces (anotomous regions and municipalities) participated in the survey. According to the survey, the control of acute EGVB (49.3%, 412/836) and the prevention of recurrent bleeding (38.3%, 320/836) were major indications of endoscopic treatment. For primary [non-selective β-blocker (NSBB) or endoscopic therapies] and secondary prophylaxis (NSBB and endoscopic therapies) of EGVB, adherence to domestic guideline was 72.5% (606/836) and 39.2% (328/836), respectively. There were significant differences in the adherence between secondary and tertiary hospitals in primary prophylaxis of EGVB [71.0% (495/697) VS 79.9% (111/139), χ2=4.11, P=0.033] and secondary prophylaxis of EGVB [41.6% (290/697) VS 27.3% (38/139), χ2=9.31, P=0.002]. A total of 78.2% (654/836) hospitals preferred endoscopic therapies treating acute EGVB, and endoscopic therapy was more likely to be the first choice for treating acute EGVB in tertiary hospitals (82.6%, 576/697) than secondary hospitals [56.1% (78/139), χ2=46.33, P<0.001]. The optimal timing was usually within 12 hours (48.5%, 317/654) and 12-24 hours (36.9%, 241/654) after the bleeding. Regarding the management of gastroesophageal varices type 2 and isolated gastric varices type 1, most hospitals used cyanoacrylate injection in combination with sclerotherapy [48.2% (403/836) and 29.9% (250/836), respectively], but substantial proportions of hospitals preferred clip-assisted therapies [12.4% (104/836) and 26.4% (221/836), respectively]. Improving the skills of endoscopic doctors (84.2%, 704/836), and enhancing the precision of pre-procedure evaluation and quality of multidisciplinary team (78.9%, 660/836) were considered urgent needs in the development of endoscopic treatment. Conclusion:A variety of endoscopic treatments for gastroesophageal varices in portal hypertension are implemented nationwide. Participant hospitals are active to perform emergent endoscopy for acute EGVB, but are inadequate in following recommendations regarding primary and secondary prophylaxis of EGVB. Moreover, the selection of endoscopic procedures for gastric varices differs greatly among hospitals.