BackgroundStudents in secondary vocational health school are at the age of puberty and prone to depressive symptoms. Peer victimization and social media addiction are found to be crucial in influencing the development of depression， and positive mental health has been proven to alleviate depressive symptoms， whereas there remains a striking lack of research on the mediating role of positive mental health and social media addiction in the relationship between peer victimization and depressive symptoms among secondary vocational health school students. ObjectiveTo explore the relationship between peer victimization and depressive symptoms and investigate the mediating role of positive mental health and social media addiction， so as to provide references for the prevention of depression among secondary vocational health school students. MethodsFrom October to December 2020， a cluster sampling framework was utilized to recruit 7 307 students from a secondary vocational health school in Luzhou City， Sichuan Province. Assessments were performed using Multidimensional Peer Victimization Scale （MPVS）， Warwick-Edinburgh Mental Well-being Scale （WEMWBS）， Bergen Social Media Addiction Scale （BSMAS） and Patient Health Questionnaire Depression Scale-9 item （PHQ-9）. Spearman correlation analysis was calculated to determine correlations between scores of scales， Process 4.0 was employed to test the mediation effect， and the bias-corrected Bootstrap procedure was used to test the significance of the mediation effect. ResultsA total of 7 044 （96.40%） valid questionnaires were collected. And 4 391（62.34%）students were found to have depressive symptoms. Correlation analysis revealed that PHQ-9 score was positively correlated with BSMAS score and MPVS score （r=0.404， 0.506， P<0.01）. WEMWBS score was negatively correlated with PHQ-9 score， BSMAS score and MPVS score （r=-0.587， -0.259， -0.358， P<0.01）. BSMAS score was positively correlated with MPVS score （r=0.328， P<0.01）. Positive mental health played a mediating role in the relationship between peer victimization and depressive symptoms， with an indirect effect value of 0.130 （95% CI： 0.119~0.141）， accounting for 30.81% of the total effect. Social media addiction also mediated the relationship between peer victimization and depressive symptoms， with an indirect effect value of 0.052 （95% CI： 0.045~0.059）， accounting for 12.34% of the total effect. Positive mental health and social media addiction exhibited a chained mediation effect on the relationship between peer victimization and depressive symptoms， with an indirect effect value of 0.012 （95% CI： 0.010~0.014） and accounting for 2.84% of the total effect. ConclusionPeer victimization can affect the presence of depressive symptoms among secondary vocational health school students both directly and indirectly through either separate or chained mediation of positive mental health and social media addiction.

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: Severe stroke has high rates of mortality and morbidity. This study aimed to investigate the clinical course, causes of worsening, and outcomes of severe ischemic stroke. METHODS: This prospective, multicenter cohort study enrolled adult patients admitted ≤30 days after ischemic stroke from nine hospitals in China between September 2017 and December 2019. Severe stroke was defined as a score of ≥15 on the National Institutes of Health Stroke Scale (NIHSS). Clinical worsening was defined as an increase of 4 in the NIHSS score from baseline. Unfavorable functional outcome was defined as a modified Rankin scale score ≥3 at 3 months and 1 year after stroke onset, respectively. We performed Logistic regression to explore baseline features and reperfusion therapies associated with clinical worsening and functional outcomes. RESULTS: Among 4201 patients enrolled, 854 patients (20.33%) had severe stroke on admission. Of 3347 patients without severe stroke on admission, 142 (4.24%) patients developed severe stroke in hospital. Of 854 patients with severe stroke on admission, 33.95% (290/854) experienced clinical worsening (median time from stroke onset: 43 h, Q1-Q3: 20-88 h), with brain edema (54.83% [159/290]) as the leading cause; 24.59% (210/854) of these patients died by 30 days, and 81.47% (677/831) and 78.44% (633/807) had unfavorable functional outcomes at 3 months and 1 year respectively. Reperfusion reduced the risk of worsening (adjusted odds ratio [OR]: 0.24, 95% confidence interval [CI]: 0.12-0.49, P  <0.01), 30-day death (adjusted OR: 0.22, 95% CI: 0.11-0.41, P  <0.01), and unfavorable functional outcomes at 3 months (adjusted OR: 0.24, 95% CI: 0.08-0.68, P <0.01) and 1 year (adjusted OR: 0.17, 95% CI: 0.06-0.50, P  <0.01). CONCLUSIONS: Approximately one-fifth of patients with ischemic stroke had severe neurological deficits on admission. Clinical worsening mainly occurred in the first 3 to 4 days after stroke onset, with brain edema as the leading cause of worsening. Reperfusion reduced the risk of clinical worsening and improved functional outcomes. REGISTRATION ClinicalTrials.gov , NCT03222024.
Humans ; Male ; Female ; Prospective Studies ; Ischemic Stroke/mortality* ; Aged ; Middle Aged ; Aged, 80 and over ; Stroke ; Brain Ischemia

This paper aims to study the effect of Ziziphi Spinosae Semen extracts on chronic unpredictable mild stress(CUMS)-induced depression-like and insomnia behavior models of mice. The CUMS-induced depression-like and insomnia behavior model of mice was established by CUMS treatment for three weeks. The mice were randomly divided into control group, model group, positive drug diazepam group(2 mg·kg~(-1)), as well as low-dose group(1.95 g·kg~(-1)), medium-dose group(3.9 g·kg~(-1)), and high-dose group(7.8 g·kg~(-1)) of Ziziphi Spinosae Semen extracts, with 18 mice in each group. On the 15th day of modeling, the drug was administered intragastrically once a day for one week. Then, the pentobarbital sodium cooperative righting experiment, open field experiment, and elevated plus maze experiment were carried out, respectively. The contents of neurotransmitters 5-hydroxytryptamine(5-HT) and 5-hydroxyindoleacetic acid(5-HIAA) in serum and thalamus of mice, as well as the levels of corticotropin releasing hormone(CRH), adrenocorticotropic hormone(ACTH), and corticosterone(CORT) in serum, were determined by enzyme-linked immunosorbent assay(ELISA). The neuron damage in the hippocampus of mice was observed by hematoxylin-eosin(HE) staining and Nissl staining. Western blot was used to detect the expressions of tryptophan hydroxylase 2(TPH2), serotonin transporter(SERT), monoamine oxidase A(MAOA), five prime repressors under dual repression binding protein 1(Freud1), synaptic plasticity-related proteins [cellular gene FOS(C-FOS), postsynaptic density protein 95(PSD95), synapsin 1(SYN1), and activity-regulated cytoskeleton-associated gene(ARC)], blood-brain barrier(BBB) permeability-related proteins [zonula occludens 1(ZO-1), occludin, and claudin 1], inflammatory factors [NOD-, LRR-and pyrin domain-containing protein 3(NLRP3), apoptosis-associated speck-like protein(ASC), gasdermin D(GSDMD), caspase-3, and caspase-8], and antioxidant factors [nuclear factor erythroid 2-related factor 2(NRF2) and heme oxygenase 1(HO1)] in thalamic tissue of mice. The results indicated that compared with that in the model group, the sleep latency was significantly shortened, and the sleep duration was significantly prolonged in each dose group of Ziziphi Spinosae Semen extracts. The number of visits to the central area of the open field and the distance and time of visits were significantly increased in each dose group of Ziziphi Spinosae Semen extracts. In addition, the proportion of distance and time of entering the open arm area of the elevated plus maze was significantly increased in each dose group of Ziziphi Spinosae Semen extracts. The contents of 5-HT and 5-HIAA in serum and thalamus of mice increased to varying degrees in each dose group of Ziziphi Spinosae Semen extracts; the contents of CRH, ACTH, and CORT in serum of mice were significantly decreased. The protein expression of TPH2 was significantly increased. The protein expression of MAOA, SERT, and Freud1 was significantly decreased. Ziziphi Spinosae Semen extracts could also significantly reduce the protein expression of C-FOS but significantly increase the protein expression of PSD95, ARC, and SYN1. They could reduce the pathological damage of the hippocampus in mice and significantly increase the protein expression of ZO-1, occluding, and claudin 1. The protein expression of NLRP3, GSDMD, ASC, caspase-3, and caspase-8 in the thalamic tissue of mice was significantly decreased, and the protein expression of HO1 and NRF2 was significantly increased. In conclusion, Ziziphi Spinosae Semen extracts could effectively improve sleep disorders and depression-like behaviors in CUMS-induced model mice, which may be related to regulating the 5-HT anabolism process and hypothalamic-pituitary-adrenal(HPA) axis-related hormone levels, reducing pathological damage in the hippocampus, improving synaptic plasticity, repairing BBB integrity, and alleviating inflammatory response and oxidative stress damage.
Animals ; Ziziphus/chemistry* ; Mice ; Male ; Depression/psychology* ; Drugs, Chinese Herbal/administration & dosage* ; Sleep Initiation and Maintenance Disorders/psychology* ; Stress, Psychological/complications* ; Behavior, Animal/drug effects* ; Humans ; Disease Models, Animal

OBJECTIVE: To investigate the characteristics of gut microbiota changes in patients with acute myeloid leukemia (AML) undergoing induction chemotherapy and to explore the relationship between infectious complications and gut microbiota. METHODS: Fecal samples were collected from 37 newly diagnosed AML patients at four time points: before induction chemotherapy, during chemotherapy, during the neutropenic phase, and during the recovery phase. Metagenomic sequencing was used to analyze the dynamic changes in gut microbiota. Correlation analyses were conducted to assess the relationship between changes in gut microbiota and the occurrence of infectious complications. RESULTS: During chemotherapy, the gut microbiota α-diversity (Shannon index) of AML patients exhibited significant fluctuations. Specifically, the diversity decreased significantly during induction chemotherapy, further declined during the neutropenic phase (P < 0.05, compared to baseline), and gradually recovered during the recovery phase, though not fully returning to baseline levels.The abundances of beneficial bacteria, such as Firmicutes and Bacteroidetes, gradually decreased during chemotherapy, whereas the abundances of opportunistic pathogens, including Enterococcus, Klebsiella, and Escherichia coli, progressively increased.Analysis of the dynamic changes in gut microbiota of seven patients with bloodstream infections revealed that the bloodstream infection pathogens could be detected in the gut microbiota of the corresponding patients, with their abundance gradually increasing during the course of infection. This finding suggests that bloodstream infections may be associated with opportunistic pathogens originating from the gut microbiota.Compared to non-infected patients, the baseline samples of infected patients showed a significantly lower relative abundance of Bacteroidetes (P < 0.05). Regression analysis indicated that Bacteroidetes abundance is an independent predictive factor for infectious complications (P < 0.05, OR =13.143). CONCLUSION During induction chemotherapy in AML patients, gut microbiota α-diversity fluctuates significantly, and the abundance of opportunistic pathogens increase, which may be associated with bloodstream infections. Patients with lower baseline Bacteroidetes abundance are more prone to infections, and its abundance can serve as an independent predictor of infectious complications.
Humans ; Gastrointestinal Microbiome ; Leukemia, Myeloid, Acute/microbiology* ; Induction Chemotherapy ; Feces/microbiology* ; Male ; Female ; Middle Aged