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 investigate the mechanism of curcumin inhibition of oxidative stress on osteogenic differentiation and its dose-dependent anti-osteoporosis effect. Methods Cellular oxidative stress models were used, different concentrations of curcumin were added to determinethebone formation markers, and the potential signaling pathways involvedwere detected. Meanwhile, the mouse model of osteoporosis ( ovariecto- mized, 0VX) was used to confirm its effect against osteoporosis. Results In vitro experiments found that low concentrations of curcumin (1-10 μmol · L

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.

BACKGROUND:Hypoxic exercise can promote the degradation of body fat,and changes in the external environment can affect the circadian rhythm of animals,but the mechanisms by which changes in circadian rhythm regulate adipose tissue browning and fat degradation are unclear. OBJECTIVE:To elucidate the mechanism of clock gene regulation on epididymal adipose tissue Browning in obese rats undergoing hypoxia exercise. METHODS:Forty obese rats were randomly selected and divided into four groups(n=10 per group):normoxic sedentary group,hypoxic sedentary group,normoxic exercise group,and hypoxic exercise group for 4 weeks of intervention.The rats in the sedentary groups were not intervened,while those in the hypoxic groups lived in a hypoxic chamber with an oxygen concentration of 13.6%for the whole day.In the exercise groups,adaptive training was performed in the 1st week,and the speed and length of training remained unchanged for the last 3 weeks.The body mass,body length and perirenal fat mass of obese rats were measured.Serum levels of triacylglycerol,total cholesterol,low-density lipoprotein cholesterol,and high-density lipoprotein cholesterol in obese rats were detected by a biochemical assay kit.Liver fat content was observed by oil red O staining.Hematoxylin-eosin staining was used to evaluate the browning of epididymal adipose tissue of rats in different groups.RNA sequencing combined with bioinformatics analysis was used to analyze transcriptome changes in adipose tissue.The mRNA expressions of PGC-1α,Beclin 1,KLF 2 and Perilipin 1 in epididymal adipose tissue were detected by RT-PCR. RESULTS AND CONCLUSION:Hypoxic exercise intervention significantly decreased body mass,body fat percentage,Lee's index,serum triacylglycerol,total cholesterol,and low-density lipoprotein cholesterol levels(P＜0.01),and significantly increased high-density lipoprotein cholesterol level(P＜0.01).Oil red O staining and hematoxylin-eosin staining results showed that hypoxic exercise was more effective in promoting fat mobilization in liver tissue and promoting the browning of parepididymal adipose tissue compared with normoxic sedentary group,hypoxic sedentary group,and normoxic exercise group.RNA-seq results showed that hypoxic exercise significantly upregulated the expression of clock genes Dbp,Nr1d1,Sik1 and adipose tissue browning gene Ppargc1a(PGC-1α)and downregulated the expression of Arntl(Bmal1),accompanied by the enhanced expression of genes related to substance metabolism.qRT-PCR indicated that hypoxic exercise significantly increased the mRNA expression levels of PGC-1α and Perilipin1(P＜0.01).Therefore,these findings indicate that clock genes play an important role in promoting adipose tissue browning during hypoxic exercise.

This paper applied gas chromatography-mass spectrometry (GC-MS), network pharmacology and nuclear magnetic resonance hydrogen spectroscopy (¹H NMR) metabolomics techniques to study the material basis and mechanism of action of Ning Shen Essential Oil in anti-insomnia. The main volatile components of Ning Shen Essential Oil were analyzed by gas chromatography-mass spectrometry (GC-MS), and the insomnia-related targets were predicted using the Traditional Chinese Medicine Systematic Pharmacology Database and Analytical Platform (TCMSP) and the databases of GeneCards, OMIM and Drugbank. The insomnia model of rats was replicated by intraperitoneal injection of 4-chloro-DL-phenylalanine (PCPA). Animal experiments were approved by the Animal Ethics Committee of Shandong University of Traditional Chinese Medicine (Ethics No.: SDUTCM20221025010). The modulating effect of Compound Ning Shen Essential Oil on anxiety behavior of rats was evaluated by behavioral related indexes. The serum levels of corticotropin-releasing hormone (CRH), adrenotropic corticotropic hormone (ACTH) and melatonin (MT) were measured by enzyme-linked immunoassay (ELISA). Rat serum and hippocampus were taken for nuclear magnetic resonance (¹H NMR) metabolomics to detect the changes of endogenous metabolites in rat serum hippocampus, to designate the differential metabolites and to construct metabolic pathways. The results showed that the exercise distance in the open field experiment and the number of times and time to enter the open arm in the elevated cross maze experiment of the rats in the model group were significantly reduced (P < 0.05, P < 0.01). The behavioral indexes of rats improved to different degrees after the administration of Ning Shen Essential Oil. The serum CRH and ACTH levels of rats in the model group increased significantly (P < 0.05, P < 0.01), and the MT level decreased significantly (P < 0.01); After the intervention, serum CRH and ACTH levels were reduced to different degrees, and MT levels could be regressed. ¹H NMR metabolomics screened 10 potential biomarkers related to insomnia, which involved in 6 potential metabolic pathways. A total of 35 components of Ning Shen Essential Oil were detected by GC-MS, the main component targets of Ning Shen Essential Oil and insomnia disease targets were intersected, a total of 172 intersecting genes were screened, and 26 core targets were identified. The study demonstrated that Ning Shen Essential Oil had protective effects against PCPA-induced insomnia in rats, which was probably correlated with regulation of the hypothalamic-pituitary-adrenal axis (HPA) related hormones and metabolism of amino acids, lipids and choline.

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.