With the rising incidence of diabetes， diabetic kidney disease （DKD） has become a significant global health burden. Although current prevention and treatment strategies can partially delay the progression of DKD， the risk of patients advancing to end-stage renal disease remains high. Since the concept of the "gut-kidney axis" was first introduced at the International Congress on Dialysis in 2011， research on the role of gut microbiota in the pathogenesis of DKD has received increasing attention. This review summarizes the current research on gut microbiota， explores the mechanisms through which it contributes to DKD development， and outlines clinical approaches for DKD prevention and treatment based on the "gut-kidney axis" theory. Evidence indicates that dietary interventions， intake of probiotics or prebiotics， use of metformin and novel antidiabetic drugs， and application of traditional Chinese medicine （TCM） compound formulas can effectively improve gut microbiota composition， influence metabolite production， and restore the intestinal mucosal barrier. These interventions can further regulate intestinal innate immunity and inflammatory responses， thereby modulating the progression of DKD. Despite challenges posed by the traditional oral administration of water-decocted TCM compound formulas and the complexity of their ingredients， increasing evidence suggests that TCM may indirectly affect the occurrence and development of DKD by modulating gut microbiota. This finding provides a new perspective on the potential mechanisms of TCM in DKD treatment and may offer novel strategies for DKD prevention and therapy.

Acute burns and chronic wounds frequently fail to heal owing to various reasons. Most drugs currently used for wound therapy in clinical practice have notable drawbacks, making their application a substantial concern. For instance, anti-inflammatory drugs can exert multisystem toxicity, and cellular therapies are costly and difficult to retain. In recent years, natural functional proteins derived from animals and plants have gained increasing attention owing to their unique biological activities, low cost, and broad application prospects in wound therapy. Herein, we isolated a new protein (JH015Y) from amphibians and demonstrated its excellent wound repair and regeneration properties compared with those of epidermal growth factor, both in vitro and in vivo. JH015 protein increased the proliferative ability of human keratinocytes and skin fibroblasts by 47.73 and 41.40%, respectively. In vivo, the medium-dose (0.5 mg/dose) groups of JH015Y protein demonstrated accelerated wound healing from day 4, with wound healing rates 1.26, 1.27, and 1.14 times that of the blank group in acute wounds, burn wounds, and diabetic ulcer, respectively. Histological analysis of Masson-stained sections indicated that the JH015Y protein contributed to collagen deposition on the wound surface, markedly reduced inflammatory cell infiltration, and exhibited low biological toxicity. Accordingly, the JH015Y protein is a promising biotherapeutic agent for accelerated wound repair and regeneration.

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.

Acute burns and chronic wounds frequently fail to heal owing to various reasons. Most drugs currently used for wound therapy in clinical practice have notable drawbacks, making their application a substantial concern. For instance, anti-inflammatory drugs can exert multisystem toxicity, and cellular therapies are costly and difficult to retain. In recent years, natural functional proteins derived from animals and plants have gained increasing attention owing to their unique biological activities, low cost, and broad application prospects in wound therapy. Herein, we isolated a new protein (JH015Y) from amphibians and demonstrated its excellent wound repair and regeneration properties compared with those of epidermal growth factor, both in vitro and in vivo. JH015 protein increased the proliferative ability of human keratinocytes and skin fibroblasts by 47.73 and 41.40%, respectively. In vivo, the medium-dose (0.5 mg/dose) groups of JH015Y protein demonstrated accelerated wound healing from day 4, with wound healing rates 1.26, 1.27, and 1.14 times that of the blank group in acute wounds, burn wounds, and diabetic ulcer, respectively. Histological analysis of Masson-stained sections indicated that the JH015Y protein contributed to collagen deposition on the wound surface, markedly reduced inflammatory cell infiltration, and exhibited low biological toxicity. Accordingly, the JH015Y protein is a promising biotherapeutic agent for accelerated wound repair and regeneration.