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.

The chemical constituents in dried roots of Atractylodes macrocephala were investigated in this study. Through utilizing normal-phase silica gel and ODS column chromatography, TLC, and semi-preparative HPLC, 4 new sesquiterpenoids were purified from the ethyl acetate extract of A. macrocephala. By various spectroscopic techniques, such as 1D and 2D NMR, HR-ESI-MS, IR, UV, and CD, their structures were identified as atractylmacron A (1), 9β-hydroxyasterolide (2), atractylenolide H (3), atractylenolide J (4). Compound 1 possesses a rare 6/7 bicyclic skeleton.

Objective: To investigate the expression of peroxiredoxin 4 (PRDX4) in oral squamous cell carcinoma (OSCC) and its effect on the proliferation, migration, and invasion of OSCC cells. Methods: The Cancer Genome Atlas(TCGA) database was used to analyze the expression of PRDX4 in OSCC. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western Blot (WB) were used to detect the mRNA and protein expression of PRDX4 in OSCC cell lines and normal oral mucosal epithelial cells. PRDX4 was knocked down in CAL-27 cells and divided into two groups: the si-PRDX4 group and si-NC group. SCC-9 cells overexpressing PRDX4 were divided into two groups: the PRDX4 overexpression group (transfected with pcDNA3.1-PRDX4 plasmid) and the vector group (the control group; transfected with pcDNA3.1-NC plasmid). A cell counting kit-8 (CCK-8) and plate colony formation assay were used to detect cell proliferation. Transwell assay and cell scratch test were used to detect cell invasion and migration ability. WB was used to detect the effects of knockdown or overexpression of PRDX4, p38MAPK agonist or inhibitor on the expression of p38MAPK-related signaling pathway proteins, and epithelial mesenchymal transition proteins in OSCC cells. Results: PRDX4 was highly expressed in OSCC tissues and cell lines. The results of qRT-PCR and WB showed that PRDX4 was highly expressed in OSCC cell lines compared with normal oral mucosal epithelial cells. The CCK-8 assay showed that the si-PRDX4 group had significantly lower OD values than the si-NC group at 24, 48, and 72 h (P<0.05). The PRDX4 overexpression group had a significantly higher OD value than the vector group at 24, 48, and 72 h (P<0.05). The plate colony formation assay showed that the si-PRDX4 group had a significantly lower number of colonies than the si-NC group (P<0.05). The number of colonies formed in the PRDX4 overexpression group was significantly higher than that in the vector group (P<0.05). The cell scratch test showed that the wound healing area of the si-PRDX4 group was less than that of the si-NC group (P<0.05). The scratch healing area of the PRDX4 overexpression group was significantly higher than that of the vector group (P<0.05). The Transwell invasion assay showed that the number of transmembrane cells in the si-PRDX4 group was lower than that in the si-NC group (P<0.05). The number of transmembrane cells in the PRDX4 overexpression group was significantly higher than that in the vector group (P<0.05). The WB results showed that knockdown and overexpression of PRDX4 could downregulate and upregulate the expression of the p38MAPK signaling pathway and epithelial-mesenchymal transition related proteins, respectively, and the addition of p38MAPK agonist and inhibitor could significantly reverse the expression of related proteins. Conclusion PRDX4 is highly expressed in OSCC. Knocking down the expression of PRDX4 in OSCC cells can downregulate the expression of p38 MAPK signal axis and EMT-related signal proteins, thereby inhibiting the proliferation, migration, invasion, and epithelial-mesenchymal transition of cells.

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.

Based on the traditional Chinese medicine theory that "all pain， itching， and sores are related to the heart"， this paper proposes treating recurrent aphthous ulcers from the perspective of the heart. It suggests that excessive heart fire and tissue erosion due to flaming fire in the heart meridian constitute the core pathogenesis of this condition. Hyperactive heart fire is identified as the key pathogenic factor， while heart yin deficiency， obstruction of the heart collaterals， and malnourishment of the heart spirit are considered significant contributing factors. Clinically， the treatment follows the principle of clearing heart fire as the main strategy， supplemented by nourishing yin， activating collaterals， and calming the spirit. The self-formulated Qingxin Yuchuang Formulation （清心愈疮方） serves as the base prescription， with flexible modifications incorporating the Yuyin Formulation （育阴方）， Huoxue Formulation （活血方）， and Yu'an Formulation （郁安方） to address specific syndromes involving heart yin deficiency， collateral blockage， and emotional disturbance.

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.