ABSTRACT
AIM OF THE STUDY: This study aimed to determine the effect of Epimedium brevicornu Maxim. (EF) on osteoporosis (OP) and its underlying molecular mechanisms, and to explore the existence of the "Gut-Bone Axis". MATERIAL AND METHODS: The impact of EF decoction (EFD) on OP was evaluated using istopathological examination and biochemical assays. Targeted metabolomics was employed to identify key molecules and explore their molecular mechanisms. Alterations in the gut microbiota (GM) were evaluated by 16S rRNA gene sequencing. The role of the GM was clarified using an antibiotic cocktail and faecal microbiota transplantation. RESULTS: EFD significantly increased the weight (14.06%), femur length (4.34%), abdominal fat weight (61.14%), uterine weight (69.86%), and insulin-like growth factor 1 (IGF-1) levels (59.48%), while reducing serum type I collagen cross-linked carboxy-terminal peptide (CTX-I) levels (15.02%) in osteoporotic mice. The mechanism of action may involve the regulation of the NLRP3/cleaved caspase-1/IL-1ß signalling pathway in improving intestinal tight junction proteins and bone metabolism. Additionally, EFD modulated the abundance of related GM communities, such as Lactobacillus, Coriobacteriaceae, bacteria of family S24-7, Clostridiales, and Prevotella, and increased propionate and butyrate levels. Antibiotic-induced dysbiosis of gut bacteria disrupted OP regulation of bone metabolism, which was restored by the recovery of GM. CONCLUSIONS: Our study is the first to demonstrate that EFD works in an OP mouse model by utilising GM and butyric acid. Thus, EF shows promise as a potential remedy for OP in the future.
ABSTRACT
INTRODUCTION: The COVID-19 patients in the convalescent stage noticeably have pulmonary diffusing capacity impairment (PDCI). The pulmonary diffusing capacity is a frequently-used indicator of the COVID-19 survivors' prognosis of pulmonary function, but the current studies focusing on prediction of the pulmonary diffusing capacity of these people are limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting PDCI in the COVID-19 patients using routinely available clinical data, thus assisting the clinical diagnosis. METHODS: Collected from a follow-up study from August to September 2021 of 221 hospitalized survivors of COVID-19 18 months after discharge from Wuhan, including the demographic characteristics and clinical examination, the data in this study were randomly separated into a training (80%) data set and a validation (20%) data set. Six popular machine learning models were developed to predict the pulmonary diffusing capacity of patients infected with COVID-19 in the recovery stage. The performance indicators of the model included area under the curve (AUC), Accuracy, Recall, Precision, Positive Predictive Value(PPV), Negative Predictive Value (NPV) and F1. The model with the optimum performance was defined as the optimal model, which was further employed in the interpretability analysis. The MAHAKIL method was utilized to balance the data and optimize the balance of sample distribution, while the RFECV method for feature selection was utilized to select combined features more favorable to machine learning. RESULTS: A total of 221 COVID-19 survivors were recruited in this study after discharge from hospitals in Wuhan. Of these participants, 117 (52.94%) were female, with a median age of 58.2 years (standard deviation (SD) = 12). After feature selection, 31 of the 37 clinical factors were finally selected for use in constructing the model. Among the six tested ML models, the best performance was accomplished in the XGBoost model, with an AUC of 0.755 and an accuracy of 78.01% after experimental verification. The SHAPELY Additive explanations (SHAP) summary analysis exhibited that hemoglobin (Hb), maximal voluntary ventilation (MVV), severity of illness, platelet (PLT), Uric Acid (UA) and blood urea nitrogen (BUN) were the top six most important factors affecting the XGBoost model decision-making. CONCLUSION: The XGBoost model reported here showed a good prognostic prediction ability for PDCI of COVID-19 survivors during the recovery period. Among the interpretation methods based on the importance of SHAP values, Hb and MVV contributed the most to the prediction of PDCI outcomes of COVID-19 survivors in the recovery period.
Subject(s)
COVID-19 , Pulmonary Diffusing Capacity , Humans , Female , Middle Aged , Male , Follow-Up Studies , Area Under Curve , Machine LearningABSTRACT
BACKGROUND: Dermatophyte caused by Trichophyton mentagrophytes is a global disease with a growing prevalence that is difficult to cure. Perilla frutescens (L.) Britt. is an edible and medicinal plant. Ancient books of Traditional Chinese Medicine and modern pharmacological studies have shown that it has potential anti-fungi activity. This is the first study to explore the inhibitory effects of compounds from P. frutescens on Trichophyton mentagrophytes and its mechanism of action coupled with the antifungal activity in vitro from network pharmacology, transcriptomics and proteomics. METHODS: Five most potential inhibitory compounds against fungi in P. frutescens was screened with network pharmacology. The antifungal activity of the candidates was detected by a broth microdilution method. Through in vitro antifungal assays screening the compound with efficacy, transcriptomics and proteomics were performed to investigate the pharmacological mechanisms of the effective compound against Trichophyton mentagrophytes. Furthermore, the real-time polymerase chain reaction (PCR) was applied to verify the expression of genes. RESULTS: The top five potential antifungal compounds in P. frutescens screened by network pharmacology are: progesterone, luteolin, apigenin, ursolic acid and rosmarinic acid. In vitro antifungal assays showed that rosmarinic acid had a favorable inhibitory effect on fungi. The transcriptomic findings exhibited that the differentially expressed genes of fungus after rosmarinic acid intervention were mainly enriched in the carbon metabolism pathway, while the proteomic findings suggested that rosmarinic acid could inhibit the average growth of Trichophyton mentagrophytes by interfering with the expression of enolase in the glycolysis pathway. Comparison of real-time PCR and transcriptomics results showed that the trends of gene expression in glycolytic, carbon metabolism and glutathione metabolic pathways were identical. The binding modes and interactions between rosmarinic acid and enolase were preliminary explored by molecular docking analysis. CONCLUSION: The key findings of the present study manifested that rosmarinic acid, a medicinal compound extracted from P. frutescens, had pharmacological activity in inhibiting the growth of Trichophyton mentagrophytes by affecting its enolase expression to reduce metabolism. Rosmarinic acid is expected to be an efficacious product for prevention and treatment of dermatophytes.
