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.

ObjectiveTo investigate the ameliorative effect of Xingpi capsules on functional dyspepsia（FD） and the potential mechanism. MethodsSixty SPF-grade male SD neonatal rats（7 days old） were randomly divided into the normal group（n=12） and the modeling group（n=48）， and the FD model was prepared by iodoacetamide gavage in the modeling group. After the model was successfully prepared， the rats in the modeling group were randomly divided into the model group， the low-dose and high-dose groups of Xingpi capsules（0.135， 0.54 g·kg^-1） and the domperidone group（3 mg·kg^-1）， with 12 rats in each group. Rats in the normal and model groups were gavaged with distilled water， and rats in the rest of the groups were gavaged with the corresponding medicinal solution， once a day for 7 d. The general survival condition of the rats was observed， and the water intake and food intake of the rats were measured， the gastric emptying rate and the small intestinal propulsion rate were measured at the end of the treatment， the pathological damage of the rat duodenum was examined by hematoxylin-eosin（HE） staining， and the expressions of colonic tight junction protein（Occludin） and zonula occludens protein-1（ZO-1） were detected by immunofluorescence. The differentially expressed genes in the duodenal tissues of the model group and the normal group， and the high-dose group of Xingpi capsules and the model group were detected by transcriptome sequencing after the final administration， and Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analyses were carried out. The transcriptomic results were validated by Western blot， immunofluorescence， and real-time fluorescence quantitative polymerase chain reaction（Real-time PCR）， and the active ingredients of Xingpi capsules were screened for molecular docking with the key targets. ResultsCompared with the normal group， the general survival condition of rats in the model group was poorer， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly reduced（P<0.05）， inflammatory infiltration was seen in duodenal pathology， and the fluorescence intensities of Occludin and ZO-1 in the colon were significantly reduced（P<0.01）. Compared with the model group， the general survival condition of rats in the high-dose group of Xingpi capsules improved significantly， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly increased（P<0.05）， the duodenal pathology showed a decrease in inflammatory infiltration， and the fluorescence intensities of colonic Occludin and ZO-1 were significantly increased（P<0.01）. Transcriptomic results showed that Xingpi capsules might exert therapeutic effects by regulating the phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt） through the key genes such as Slc5a1， Abhd6. The validation results showed that compared with the normal group， the phosphorylation levels of PI3K and Akt proteins， the protein expression level of interleukin（IL）-1β， and the fluorescence intensities of IL-6 and IL-1β were significantly increased in the model group（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3， Slc5a9 and other key genes were significantly increased（P<0.01）. Compared with the model group， the phosphorylation levels of PI3K and Akt， the protein expression level of IL-1β and the fluorescence intensities of IL-6 and IL-1β in the high-dose group of Xingpi capsules were significantly reduced（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3 and Slc5a9 were significantly reduced（P<0.05）. Weighted gene co-expression network analysis and molecular docking results showed that E-nerolidol and Z-nerolidol in Xingpi capsules were well bound to ABDH6 protein， and linarionoside A， valerosidatum and senkirkine were well bound to Slc5a1 protein. ConclusionXingpi capsules can effectively improve the general survival and gastrointestinal motility of FD rats， its specific mechanism may be related to the inhibition of PI3K/Akt signaling pathway to alleviate the low-grade inflammation of duodenum， and E-nerolidol， Z-nerolidol， linarionoside A， valerosidatum and senkirkine may be its key active ingredients.

ObjectiveTo investigate the ameliorative effect of Xingpi capsules on functional dyspepsia（FD） and the potential mechanism. MethodsSixty SPF-grade male SD neonatal rats（7 days old） were randomly divided into the normal group（n=12） and the modeling group（n=48）， and the FD model was prepared by iodoacetamide gavage in the modeling group. After the model was successfully prepared， the rats in the modeling group were randomly divided into the model group， the low-dose and high-dose groups of Xingpi capsules（0.135， 0.54 g·kg^-1） and the domperidone group（3 mg·kg^-1）， with 12 rats in each group. Rats in the normal and model groups were gavaged with distilled water， and rats in the rest of the groups were gavaged with the corresponding medicinal solution， once a day for 7 d. The general survival condition of the rats was observed， and the water intake and food intake of the rats were measured， the gastric emptying rate and the small intestinal propulsion rate were measured at the end of the treatment， the pathological damage of the rat duodenum was examined by hematoxylin-eosin（HE） staining， and the expressions of colonic tight junction protein（Occludin） and zonula occludens protein-1（ZO-1） were detected by immunofluorescence. The differentially expressed genes in the duodenal tissues of the model group and the normal group， and the high-dose group of Xingpi capsules and the model group were detected by transcriptome sequencing after the final administration， and Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analyses were carried out. The transcriptomic results were validated by Western blot， immunofluorescence， and real-time fluorescence quantitative polymerase chain reaction（Real-time PCR）， and the active ingredients of Xingpi capsules were screened for molecular docking with the key targets. ResultsCompared with the normal group， the general survival condition of rats in the model group was poorer， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly reduced（P<0.05）， inflammatory infiltration was seen in duodenal pathology， and the fluorescence intensities of Occludin and ZO-1 in the colon were significantly reduced（P<0.01）. Compared with the model group， the general survival condition of rats in the high-dose group of Xingpi capsules improved significantly， and the water intake， food intake， gastric emptying rate and small intestinal propulsion rate were all significantly increased（P<0.05）， the duodenal pathology showed a decrease in inflammatory infiltration， and the fluorescence intensities of colonic Occludin and ZO-1 were significantly increased（P<0.01）. Transcriptomic results showed that Xingpi capsules might exert therapeutic effects by regulating the phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt） through the key genes such as Slc5a1， Abhd6. The validation results showed that compared with the normal group， the phosphorylation levels of PI3K and Akt proteins， the protein expression level of interleukin（IL）-1β， and the fluorescence intensities of IL-6 and IL-1β were significantly increased in the model group（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3， Slc5a9 and other key genes were significantly increased（P<0.01）. Compared with the model group， the phosphorylation levels of PI3K and Akt， the protein expression level of IL-1β and the fluorescence intensities of IL-6 and IL-1β in the high-dose group of Xingpi capsules were significantly reduced（P<0.05， P<0.01）， and the mRNA levels of Slc5a1， Abhd6， Mgam， Atp1a1， Slc7a8， Cdr2， Chrm3 and Slc5a9 were significantly reduced（P<0.05）. Weighted gene co-expression network analysis and molecular docking results showed that E-nerolidol and Z-nerolidol in Xingpi capsules were well bound to ABDH6 protein， and linarionoside A， valerosidatum and senkirkine were well bound to Slc5a1 protein. ConclusionXingpi capsules can effectively improve the general survival and gastrointestinal motility of FD rats， its specific mechanism may be related to the inhibition of PI3K/Akt signaling pathway to alleviate the low-grade inflammation of duodenum， and E-nerolidol， Z-nerolidol， linarionoside A， valerosidatum and senkirkine may be its key active ingredients.

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.

Obesity is a worldwide health problem. An imbalance in energy metabolism is an important cause of obesity and related metabolic diseases. Our previous studies showed that inhibition of miR-429 increased the protein level of uncoupling protein 1 (UCP1) in beige adipocytes; however, whether local inhibition of miR-429 in subcutaneous adipose tissue affects diet-induced obesity and related metabolic disorders remains unclear. The aim of this study was to investigate the effect of local overexpression of miR-429 sponge in subcutaneous adipose tissue on obesity and related metabolic disorders. The control adeno-associated virus (AAV) or AAV expressing the miR-429 sponge was injected into mouse inguinal white adipose tissue. Seven days later, the mice were fed a high-fat diet for 10 weeks to induce obesity. The effects of the miR-429 sponge on body weight, adipose tissue weight, plasma glucose and lipid levels, and hepatic lipid content were explored. The results showed that the overexpression of miR-429 sponge in subcutaneous white adipose tissue reduced body weight and fat mass, decreased fasting blood glucose and plasma cholesterol levels, improved glucose tolerance, and alleviated hepatic lipid deposition in mice. Mechanistic investigation showed that the inhibition of miR-429 significantly upregulated the expression of UCP1 in adipocytes and adipose tissue. These results suggest that local inhibition of miR-429 in subcutaneous white adipose tissue ameliorates obesity and related metabolic disorders potentially by upregulating UCP1, and miR-429 is a potential therapeutic target for the treatment of obesity and related metabolic disorders.
Animals ; MicroRNAs/physiology* ; Obesity/metabolism* ; Mice ; Adipose Tissue, White/metabolism* ; Metabolic Diseases ; Subcutaneous Fat/metabolism* ; Male ; Uncoupling Protein 1/metabolism* ; Diet, High-Fat ; Mice, Inbred C57BL

This study aims to integrate data mining techniques of single cell transcriptomics and spatial transcriptomics, along with animal experiment validation, so as to systematically characterize the protective effects of Jianpi Tongluo Formula(JTF) on the cartilage in knee osteoarthritis(KOA) and elucidate the underlying molecular mechanisms. Single cell transcriptomics and spatial transcriptomics datasets(GSE254844 and GSE255460) of the cartilage tissue obtained from KOA patients were analyzed to map the single cell-spatial heterogeneity and identify key pathogenic factors. After that, a KOA rat model was established via knee joint injection of papain. The intervention effects of JTF on the expression features of these key factors were assessed through real-time quantitative polymerase chain reaction(PCR), Western blot, and immunohistochemical staining. As a result, the integrated single cell and spatial transcriptomics data identified distinct cell subsets with different pathological changes in different regions of the inflamed cartilage tissue in KOA, and their differentiation trajectories were closely related to the inflammatory fibrosis-like pathological changes of chondrocytes. Accordingly, the expression levels of the two key effect targets, namely nuclear receptor coactivator 4(NCOA4) and high mobility group box 1(HMGB1) were significantly reduced in the articular surface and superficial zone of the inflamed joints when JTF effectively alleviated various pathological changes in KOA rats, thus reversing the abnormal chondrocyte autophagy level, relieving the inflammatory responses and fibrosis-like pathological changes, and promoting the repair of chondrocyte function. Collectively, this study revealed the heterogeneous characteristics and dynamic changes of inflamed cartilage tissue in different regions and different cell subsets in KOA patients. It is worth noting that NCOA4 and HMGB1 were crucial in regulating chondrocyte autophagy and inflammatory reaction, while JTF could reverse the regulation of NCOA4 and HMGB1 and correct the abnormal molecular signal axis in the target cells of the inflamed joints. The research can provide a new research idea and scientific basis for developing a personalized therapeutic schedule targeting the spatiotemporal heterogeneity characteristics of KOA.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Osteoarthritis, Knee/pathology* ; Humans ; Male ; Cartilage, Articular/metabolism* ; Chondrocytes/metabolism* ; Rats, Sprague-Dawley ; Female ; Protective Agents/administration & dosage* ; Single-Cell Analysis ; Middle Aged ; HMGB1 Protein/metabolism*

Compounds isolated from Epimedium include the total flavonoids of Epimedium , icariin, and its metabolites (icaritin, icariside I, and icariside II), which have similar molecular structures. Modern pharmacological research and clinical practice have proved that Epimedium and its active components have a wide range of pharmacological effects, especially in improving sexual function, hormone regulation, anti-osteoporosis, immune function regulation, anti-oxidation, and anti-tumor activity. To date, we still need a comprehensive source of knowledge about the pharmacological effects of Epimedium and its bioactive compounds on the male reproductive system. However, their actions in other tissues have been reviewed in recent years. This review critically focuses on the Epimedium , its bioactive compounds, and the biochemical and molecular mechanisms that modulate vital pathways associated with the male reproductive system. Such intrinsic knowledge will significantly further studies on the Epimedium and its bioactive compounds that protect the male reproductive system and provide some guidances for clinical treatment of related male reproductive disorders.
Male ; Epimedium/chemistry* ; Humans ; Genitalia, Male/drug effects* ; Flavonoids/therapeutic use* ; Animals