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.

Objective: To explore the association between lifestyle and fat mass index (FMI) in different positions of children and adolescents aged 7-18, so as to provide a scientific basis for health promotion in children and adolescents. Methods: A total of 1 531 students aged 7-18 was selected by intentional sampling from 4 schools in Tongzhou District, Beijing from September to December in 2020 and August in 2022. Questionnaire survey was used to collect lifestyle including dietary behavior, moderate to vigorous physical activity, smoke and drink behaviors, sleep time and sleep quality. Dual energy Xray absorptiometry was employed to assess fat mass, and calculated total, android, trunk, hip, gynoid and leg fat mass index (FMI). The ttest and Chisquare test were used to compare the differences of different lifestyle. Logistic regression was used to analysis association between lifestyle and body composition in different positions. Results: Compared with healthy lifestyle, unhealthy lifestyle had higher risk for hightrunk FMI (OR=1.40, P<0.05). After adjusted for sex and age, unhealthy lifestyle had higher risk for hightotal FMI, highandroid FMI, hightrunk FMI (OR=1.37, 1.37, 1.50, P<0.05), compared with healthy lifestyle. Stratified analysis found the associations between unhealthy lifestyle and hightotal FMI, highandroid FMI, hightrunk FMI, and highthigh FMI were only significant in girls with 7-12 years old (OR=2.13, 2.46, 2.13, 2.13, P<0.05). Conclusions Unhealthy lifestyle is associated with hightotal FMI, highandroid FMI and hightrunk FMI. A healthy lifestyle should be maintained during puberty, especially before puberty, to help children and adolescents reduce body fat and promote a balanced distribution of body composition.

OBJECTIVE: To examine the therapeutic effect of Fangji Fuling Decoction (FFD) on sepsis through network pharmacological analysis combined with in vitro and in vivo experiments. METHODS: A sepsis mouse model was constructed through intraperitoneal injection of 20 mg/kg lipopolysaccharide (LPS). RAW264.7 cells were stimulated by 250 ng/mL LPS to establish an in vitro cell model. Network pharmacology analysis identified the key molecular pathway associated with FFD in sepsis. Through ectopic expression and depletion experiments, the effect of FFD on multiple organ damage in septic mice, as well as on cell proliferation and apoptosis in relation to the mitogen-activated protein kinase 14/Forkhead Box O 3A (MAPK14/FOXO3A) signaling pathway, was analyzed. RESULTS: FFD reduced organ damage and inflammation in LPS-induced septic mice and suppressed LPS-induced macrophage apoptosis and inflammation in vitro (P<0.05). Network pharmacology analysis showed that FFD could regulate the MAPK14/FOXO signaling pathway during sepsis. As confirmed by in vitro cell experiments, FFD inhibited the MAPK14 signaling pathway or FOXO3A expression to relieve LPS-induced macrophage apoptosis and inflammation (P<0.05). Furthermore, FFD inhibited the MAPK14/FOXO3A signaling pathway to inhibit LPS-induced macrophage apoptosis in the lung tissue of septic mice (P<0.05). CONCLUSION FFD could ameliorate the LPS-induced inflammatory response in septic mice by inhibiting the MAPK14/FOXO3A signaling pathway.
Mice ; Animals ; Mitogen-Activated Protein Kinase 14/metabolism* ; Wolfiporia ; Lipopolysaccharides/pharmacology* ; Sepsis/complications* ; Signal Transduction ; Inflammation/drug therapy* ; Oxygen Radioisotopes

The anti-inflammatory efficacy of Callicarpa nudiflora extract were evaluated upon lipopolysaccharide (LPS)-induced infective inflammation in rats. Pathological changes of lung and colon tissues observed with hematoxylin-eosin (H&E) staining, together with inflammatory factors in serum, including nitric oxide (NO), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), were applied to assess the mitigating effects of C. nudiflora water extract on model rats. The experimental protocol strictly adhered to the guidelines of the Ethics Committee of Animal Research of Guangdong Pharmaceutical University (Approval: gdpulac2022132). Quality markers with anti-inflammatory activities were recognized by network pharmacology analysis. Subsequently, a reliable method for simultaneously quantifying six components was established. As a result, the pathological injury on lung and colon tissues of model rats induced by LPS were improved by C. nudiflora water extract. Compared with model group, C. nudiflora extract had decreased the release of proinflammatory factors in serum, including TNF-α and IL-6, and reduced the NO (P < 0.01). Network pharmacological analysis obtained 83 chemical components, 466 component targets, 584 disease targets and 129 action targets, which were mainly concentrated in 624 biological processes such as inflammatory response and positive regulation of nitric oxide biosynthesis, as well as 162 pathways such as phosphatidylinositol-3-hydroxykinase-protein kinase (PI3K-Akt) signal pathway and Janus kinase-signal transducer and activator of transcription 3 (JAK-STAT3) signal pathway. Upon analyzing their specificity, effectiveness, detectability, and quantity transfer rule from herb to extract, 6-hydroxyluteolin 7-glucoside, cynaroside, caffeic acid, acteoside, isoacteoside, and forsythoaside B were identified as the quality markers. Their anti-inflammatory activities were verified with LPS-induced inflammation model of RAW264.7 cells. Then contents of these 6 quality markers in 16 batches of C. nudiflora were determined. Thereby, the anti-inflammatory efficacy of C. nudiflora extract were confirmed in vivo, 6-hydroxyluteolin 7-glucoside, cynaroside, caffeic acid, acteoside, isoacteoside, and forsythoaside B were identified as anti-inflammatory ingredients, which can be used as quality control indicators for the herb and related formulation.

In this study, fluvoxamine maleate sustained-release pellet system tablets were prepared and were used to evaluate their release behaviors in vitro. Fluvoxamine maleate pellets were prepared using centrifugal-spherization method and coated by fluidized bed as bottom-spray. The multi-unit sustained-release pellets and appropriate excipients for prescription volumes were mixed uniformly and then compressed to tablets. Screening and determining the optimal formulation of drug loaded pellets through L8 (2⁴) Taguchi experiment. Using Minitab software to design a DOE experiment with 2⁴ partial factors, including material temperature, fan speed, atomization pressure, and spray rate to optimize the bottom spray coating process. Taking monostearate glycerol ester with a particle size of 24-40 mesh as the main diluent for tableting to relieve the delamination phenomenon between pellets and excipients during tablet pressing and reduce mechanical damage to the coating film. By examining the powder fluidity indexes such as angle of repose, bulk density, tapped density, and Hausner ratio of mixed particles, it was found that the flowability and compressibility are good and suitable for direct compression. Evaluate the basic properties of the sustained-release tablets, investigate the in vitro release behavior and study the release mechanism. The results of in vitro release test showed that the self-made sustained-release tablets could disintegrate into independent pellet units in phosphate buffer at pH 6.8 and release slowly within 24 h, which conformed to the first-order drug release model. The fluvoxamine maleate sustained-release pellet system tablets meet the requirements of preparation design and has a great commercial prospect.

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.