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.

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.

Ankylosing spondylitis (AS) combined with lower cervical fracture is often categorized into unstable fracture, with a high incidence of neurological injury and a high rate of disability and morbidity. As factors such as shoulder occlusion may affect the accuracy of X-ray imaging diagnosis, it is often easily misdiagnosed at the primary diagnosis. Non-operative treatment has complications such as bone nonunion and the possibility of secondary neurological damage, while the timing, access and choice of surgical treatment are still controversial. Currently, there are no clinical practice guidelines for the treatment of AS combined with lower cervical fracture with or without dislocation. To this end, the Spinal Trauma Group of Orthopedics Branch of Chinese Medical Doctor Association organized experts to formulate Clinical guidelines for the treatment of ankylosing spondylitis combined with lower cervical fracture in adults ( version 2024) in accordance with the principles of evidence-based medicine, scientificity and practicality, in which 11 recommendations were put forward in terms of the diagnosis, imaging evaluation, typing and treatment, etc, to provide guidance for the diagnosis and treatment of AS combined with lower cervical fracture.

AIM To investigate the clinical effects of Jingu Xiaotong Powder combined with platelet-rich plasma on patients with knee osteoarthritis.METHODS Ninety-six patients were randomly assigned into control group(48 cases)for 8-week administration of platelet-rich plasma,and observation group(48 cases)for 8-week administration of both Jingu Xiaotong Powder and platelet-rich plasma.The changes in clinical effects,IL-17,SDF-1,TLR4,GSH-Px,NO,ox-LDL,WOMAC scores,TCM syndrome score,AIM2-SF score were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P<0.05).After the treatment,the two groups displayed decreased IL-17,SDF-1,TLR4,NO,ox-LDL,WOMAC scores,TCM syndrome score(P<0.05),and increased GSH-Px,AIM2-SF score(P<0.05),especially for the observation group(P<0.05).CONCLUSION For the patients with knee osteoarthritis,Jingu Xiaotong Powder combined with platelet-rich plasma can reduce IL-17,SDF-1,TLR4 levels,inhibit body inflammation,improve oxidative stress indices,alleviate pain,and enhance clinical efficacy and life quality.

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.

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.