As the biggest tissue of human body, skin is the first barrier of resisting external aggression. Collagen is one of important parts of the skin, which could not only affect the aesthetics of skin, but also influence the health and normal function of skin. It is the great significance to find ways that could inhibit the loss of collagen. The mechanisms of the collagen degradation in skin are complex and multifaceted. Natural bioactive products have unique advantages in treating the loss of collagen, which have multi-targets and mechanisms. In this review, the mechanisms of skin collagen degradation are discussed, and the research progress of natural bioactive products in resisting skin aging through promoting collagen synthesis are reviewed, in order to provide references for futural research.

Xiaoke formula (XKF) is a classic formula for the treatment of insulin resistance (IR), but there is still unclear on bioactive equivalent combinatorial components (BECC) of XKF. In this study, based on the previous research of our team, three components, berberine, astragaloside IV and chlorogenic acid, were selected as the BECC of XKF, and their efficacy and mechanism were investigated. A high-fat diet-induced IR mouse model was used to detect blood glucose, insulin sensitivity, lipid metabolism, immune & inflammatory factors, etc., and staining of pathology sections was used to detect histopathological changes. Network pharmacology was used to predict the potential targets and signaling pathways of XKF and its BECC, and the results of the network were verified by Western blot. The animal welfare and experimental procedures followed the regulations of the Laboratory Animal Ethics Committee of Beijing MDKN Biotech Company (MDKN-2023-019). The results showed that BECC, which was composed of berberine, astragaloside IV and chlorogenic acid in the ratio of the original formula of XKF, was comparable to XKF in improving the glycemia, insulin sensitivity, histopathological damage, dyslipidemia, and immuno-inflammation in IR mice. The results of network pharmacology and Western blot suggested that the BECC of XKF and XKF might alleviate IR by promoting the activation of hepatic phosphatidylinositol 3-kinase (PI3K), phosphorylation of protein kinase B (AKT), and inhibiting the expression of glucose-6-phosphate phosphatase (G6PC) and phosphoenolpyruvate carboxykinase 1 (PCK1), the key limiting enzymes of hepatic gluconeogenesis. The above results suggest that berberine, astragaloside IV and chlorogenic acid can be used as the potential BECC of XKF to improve IR, and can regulate lipid metabolism, immuno-inflammation, and promote hepatic PI3K/AKT signaling to inhibit hepatic gluconeogenesis, regulate glucose homeostasis, and improve IR in mice.

With the rapid society development and broad recognition of "Healthy China", the demands for good life and health are increasing. Accordingly, the concept of "food and medicine homologous" have been attractive. The concept of "food and medicine homologous" has a long history in China, and is an essence of various ideas in traditional Chinese medicine, such as diet therapy, medicated diet, regimen and preventive treatment of disease, representing an important field in health science. Many studies have found that the active ingredients of "food and medicine homologous" substances are multiple types, multiple mechanisms and multiple targets, exerting their biological effects after oral administration and chemical or metabolic transformation. In this review, the chemical basis and biological principles of various "food and medicine homologous" substances were summarized as compounds, biological macromolecules and intestinal flora. By focusing on the intestinal flora, we discussed the detailed biological principles of several classic "food and medicine homologous" substances. The scientific significance of "food and medicine homologous" concept were also discussed. This review explores the concept of "food and medicine homologous" from the perspective of modern medicine, in order to provide insights for future drug development and human health.

Naturally derived metabolites are valuable resources for drug research and development, and play an important role in the treatment of diseases. As the "second genome" of the body, gut microbiota is rich in metabolic enzymes, which interacts with external substances such as drugs, thus affecting the progression of diseases. This article summarizes the interaction between gut microbiota-producing enzymes and natural medicines, and focuses on the impact of this interaction on disease progression, hoping to provide new ideas for the development and pharmacological mechanism of natural medicines.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Background: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development.Purpose: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. Methods: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. Results: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. Conclusion Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Non-infectious chronic diseases in human including diabetes, non-alcoholic fatty liver disease (NAFLD), atherosclerosis (AS), neurodegenerative diseases, osteoporosis, as well as malignant tumors may have some common pathogenic mechanisms such as non-resolved inflammation (NRI), gut microbiota dysfunction, endoplasmic reticulum stress, mitochondria dysfunction, and abnormality of the mammalian target of rapamycin (mTOR) pathway. These pathogenic mechanisms could be the basis for "homotherapy for heteropathy" in clinic. Some commonly used clinical drugs, such as metformin, berberine, aspirin, statins, and rapamycin may execute therapeutic effect on their targeted diseases,and also have the effect of "homotherapy for heteropathy". The mechanisms of the above drugs may include anti-inflammation, modulation of gut microbiota, suppression of endoplasmic reticulum stress, improvement of mitochondria function, and inhibition of mTOR. For virus infectious diseases, as some viruses need certain commonly used replicases, the inhibitors of the replicases become examples of "homotherapy for heteropathy" for antiviral therapy in clinic (for example tenofovir for both AIDS and HBV infection). Especially, in case of outbreak of new emerging viruses, these viral enzyme inhibitors such as azvudine and sofibuvir, could be rapidly used in controlling viral epidemic or pandemic, based on the principle of "homotherapy for heteropathy". In this review article, we show the research progress of the biological basis for "homotherapy for heteropathy" and the possible mechanisms of some well-known drugs, in order to provide insights and new references for innovative drug R&D.

Objective To explore the correlations of evaluations of right heart function parameters in patients with Ebstein anomaly(EA)using echocardiography and cardiac MRI.Methods Data of transthoracic echocardiography and cardiac MRI in 32 patients with EA confirmed by operation were retrospectively analyzed.The correlations of cardiac cavity size,right ventricular function and strain parameters obtained using echocardiography and the functional right ventricular(fRV)ejection fraction(EF)measured using MRI were explored.Results MRI fRV-EF in 32 cases of EA was(23.20± 7.61)％.Among echocardiographic parameters in 32 cases of EA,fractional area change(FAC)of fRV(r=0.347,P=0.015)was slightly,while global longitudinal strain(GLS)of fRV(r=0.801,P＜0.001)was highly positively correlated with MRI fRV-EF,respectively,whereas atrialized right ventricle(aRV)area/fRV area(r=-0.730,P=0.007)was highly negatively,aRV area/left ventricular area(r=-0.450,P=0.042)and right ventricular anterior-posterior diameter(r=-0.650,P=0.022)were both moderately negatively correlated with MRI fRV-EF.Both the left ventricular eccentricity index(r=-0.347,P=0.049)and Glasgow outcome scale extended(r=-0.336,P=0.024)obtained with echocardiography were slightly negatively correlated MRI fRV-EF.Conclusion Right heart function parameters in EA patients obtained with echocardiography were correlated with those of MRI fRV-GLS,among which aRV area/fRV area were highly positively correlated with MRI fRV-EF,hence having great value for evaluating right heart function in EA patients.