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.

Osteoporosis(OP) is a senile bone disease characterized by an imbalance between bone remodeling and bone formation. Targeting pathogenesis of kidney deficiency, spleen deficiency, and blood stasis, Bushen Jianpi Huoxue Decoction has a significant effect on the treatment of OP by tonifying kidney, invigorating spleen, and activating blood circulation. MicroRNA(miRNA) and the anti-apoptotic protein B-cell lymphoma-2-like protein 1(BCL2L1) are closely related to bone cell metabolism. Therefore, in this study, the binding of miR-140-5p to BCL2L1 was detected by dual luciferase assay and polymerase chain reaction(PCR). After silencing or overexpressing miR-140-5p, the apoptosis, autophagy, and osteogenic function of human fetal osteoblast cell line 1.19(HFOB1.19) were observed by flow cytometry and Western blot. Bushen Jianpi Huoxue Decoction-containing serum was prepared by intragastric administration of Bushen Jianpi Huoxue Decoction in rats. Different concentrations of Bushen Jianpi Huoxue Decoction-containing serum were used to treat HFOB1.19 with or without miR-140-5p mimic. The expression of osteogenic proteins in each group was observed, and the role of miR-140-5p/BCL2L1 in apoptosis and autophagy of HFOB1.19 was studied, along with the effect of Bushen Jianpi Huoxue Decoction on these processes. As indicated by the dual luciferase assay, miR-140-5p bound to BCL2L1. Flow cytometry and Western blot showed that miR-140-5p promoted apoptosis and inhibited autophagy in HFOB1.19. After intervention with high, medium, and low doses of Bushen Jianpi Huoxue Decoction-medicated serum, compared with the miR-140-5p NC group, the expression of osteocalcin(OCN), osteopontin(OPN), Runt-related transcription factor 2(RUNX2), and transforming growth factor beta 1(TGF-β1) decreased in the miR-140-5p mimic group, while the expression of bone morphogenetic protein 2(BMP2) showed no significant difference under high-dose intervention. Therefore, miR-140-5p/BCL2L1 can promote apoptosis and inhibit autophagy in HFOB1.19. Bushen Jianpi Huoxue Decoction can affect the osteogenic effect of miR-140-5p through BMP2.
MicroRNAs/metabolism* ; Autophagy/drug effects* ; Apoptosis/drug effects* ; Humans ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Cell Line ; bcl-X Protein/metabolism* ; Osteoblasts/metabolism* ; Rats ; Osteoporosis/physiopathology* ; Male ; Rats, Sprague-Dawley ; Osteogenesis/drug effects*

This study aims to explore the mechanism of lung and gut protection by Tanreqing Capsules on the mice infected with influenza virus based on "the lung-gut axis". A total of 110 C57BL/6J mice were randomized into control group, model group, oseltamivir group, and low-and high-dose Tanreqing Capsules groups. Ten mice in each group underwent body weight protection experiments, and the remaining 12 mice underwent experiments for mechanism exploration. Mice were infected with influenza virus A/Puerto Rico/08/1934(PR8) via nasal inhalation for the modeling. The lung tissue was collected on day 3 after gavage, and the lung tissue, colon tissue, and feces were collected on day 7 after gavage for subsequent testing. The results showed that Tanreqing Capsules alleviated the body weight reduction and increased the survival rate caused by PR8 infection. Compared with model group, Tanreqing Capsules can alleviate the lung injury by reducing the lung index, alleviating inflammation and edema in the lung tissue, down-regulating viral gene expression at the late stage of infection, reducing the percentage of neutrophils, and increasing the percentage of T cells. Tanreqing Capsules relieved the gut injury by restoring the colon length, increasing intestinal lumen mucin secretion, alleviating intestinal inflammation, and reducing goblet cell destruction. The gut microbiota analysis showed that Tanreqing Capsules increased species diversity compared with model group. At the phylum level, Tanreqing Capsules significantly increased the abundance of Firmicutes and Actinobacteria, while reducing the abundance of Bacteroidota and Proteobacteria to maintain gut microbiota balance. At the genus level, Tanreqing Capsules significantly increased the abundance of unclassified＿f＿Lachnospiraceae while reducing the abundance of Bacteroides, Eubacterium, and Phocaeicola to maintain gut microbiota balance. In conclusion, Tanreqing Capsules can alleviate mouse lung and gut injury caused by influenza virus infection and restore the balance of gut microbiota. Treating influenza from the lung and gut can provide new ideas for clinical practice.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Lung/metabolism* ; Mice, Inbred C57BL ; Capsules ; Orthomyxoviridae Infections/virology* ; Gastrointestinal Microbiome/drug effects* ; Male ; Humans ; Female ; Influenza A virus/physiology* ; Influenza, Human/virology*

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 effects of Gan Jiang-Huang Qin-Huang Lian-Ren Shen Decoction(GJHQHLRSD)on the pyroptosis,pathway of colonic epithelial cells in mouse models of ulcerative colitis(UC).METHODS Among the 63 C57BL/6J mice,13 were randomly selected and assigned to the model group,and the others were divided into the control group,the positive Sulfasalazine Enteric-Coated Tablets group(0.6 g/kg),and low,medium,and high dose GJHQHLRSD groups(3.9,7.8,15.6 g/kg),with 10 mice in each group.The UC mouse model was established using DSS,and the corresponding drugs were administered by gavage.The mice had their general condition observed;their disease activity index(DAI)score assessed;their colon length measured;their histopathological damage of the colon analyzed using HE staining;their colonic IL-1β,IL-8,and TNF-α levels measured by ELISA method;their colonic NLRP3,GSDMD,pro-IL-1β,pro-caspase-1,and IL-1βprotein expression detected by Western blot method;and their cell pyroptosis detected by TUNEL and GSDMD fluorescence double staining.RESULTS Compared with the control group,the model group exhibited significant decrease in body weight and a shortened colon length(P＜0.01);increases in DAI score,levels of IL-1β,IL-8,TNF-α,as well as the protein expressions of NLRP3,GSDMD,and active-caspase-1(P＜0.05,P＜0.01);significant increase of colonic GSDMD and TUNEL positivity;indicating increased tissue damage and inflammatory response.Compared with the model group,the groups intervened with GJHQHLRSD showed a significant increase in body weight and colonic elongation(P＜0.05,P＜0.01);decreases in DAI score,levels of IL-1β,IL-8,TNF-α,as well as the protein expressions of NLRP3,GSDMD,and active-caspase-1(P＜0.05,P＜0.01);a gradient decrease in positivity of GSDMD and TUNEL;indicating a significantly reduced colonic pathological damage.CONCLUSION GJHQHLRSD can improve the DSS-induced inflammatory reaction of colonic mucosa in UC mice,and its mechanism mainly involves the NLRP3/caspase-1,thereby the regulation of the cell pyroptosis process.

OBJECTIVE: To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019. METHOD: Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model. RESULTS: Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00). CONCLUSION The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.
Humans ; China/epidemiology* ; Incidence ; Bayes Theorem ; Spatio-Temporal Analysis ; Tuberculosis/epidemiology* ; Socioeconomic Factors

Aim To explore the effect of the classical famous prescription Dahuang Zhechong pill(DHZCP)on relieving liver cirrhosis by regulating the stress fiber remodeling mediated by mechanistic signaling pathway and to explore the underlying mechanism.Methods Mice were randomly divided into the control group,model group,DHZCP low-dose group,DHZCP high-dose group,and Colchicine-positive control group.The liver cirrhosis mouse model was constructed by intrap-eritoneal injection of olive oil-solubilized CCl4.HE staining and serologic markers were used to reflect liver injury.Masson staining was used to evaluate collagen deposition in liver tissue.ELISA was applied to detect vasoactive molecules and cancer indicators.Atomic force microscopy was employed to detect liver tissue stiffness.Color Doppler diagnostic instrument was used to assess portal blood flow velocity.Western blot was utilized to detect ROCK2 expression and phosphoryla-tion of YAP,Cofilin,and MLC.Results The liver tis-sues in the model group had obvious inflammatory cell infiltration and collagen deposition,accompanied by significant elevation of serum transaminases and fibrosis indexes.Similarly,vasoactive molecules and cancer in-dicators were elevated,and the mechanoregulatory pro-tein ROCK2 expression and phosphorylation of Cofilin and MLC were elevated,with YAP being strongly de-phosphorylated.Both low and high doses of DHZCP re-versed the pathological changes,serological indices,and inhibited the activation of the stress fiber(SF)re-modeling mechanistic signaling pathway.Conclusion DHZCP effectively ameliorates liver tissue lesions in mice with liver cirrhosis,and its mechanism may be re-lated to the inhibition of SF remodeling mechanistic signaling pathway.