The role and regulatory mechanisms of macrophage polarization in cardiac transplantation have gained significant attention.Macrophages can polarize into either the M1(pro-inflammatory)or M2(anti-inflammatory)phenotype...The role and regulatory mechanisms of macrophage polarization in cardiac transplantation have gained significant attention.Macrophages can polarize into either the M1(pro-inflammatory)or M2(anti-inflammatory)phenotype in response to environmental cues.M1 macrophages facilitate transplant rejection by releasing inflammatory mediators and activating T cells,whereas M2 macrophages support graft survival by secreting antiinflammatory factors and promoting tissue repair.Mitochondrial quality control regulation plays a crucial role in macrophage polarization,which may influence graft survival and immune responses.This review provides an overview of the current understanding of mitochondrial quality control-regulated macrophage polarization in cardiac transplantation,its effects on graft outcomes,and potential therapeutic strategies to modulate this process to enhance transplant success rates.The review was conducted by systematically analyzing recent studies and integrating findings from key research articles to synthesize a comprehensive understanding of this emerging field.展开更多
Background:Cardiac surgery-associated acute kidney injury(CSA-AKI)is a major complication that increases morbidity and mortality after cardiac surgery.Most established predictive models are limited to the analysis of ...Background:Cardiac surgery-associated acute kidney injury(CSA-AKI)is a major complication that increases morbidity and mortality after cardiac surgery.Most established predictive models are limited to the analysis of nonlinear relationships and do not adequately consider intraoperative variables and early postoperative variables.Nonextracorporeal circulation coronary artery bypass grafting(off-pump CABG)remains the procedure of choice for most coronary surgeries,and refined CSA-AKI predictive models for off-pump CABG are notably lacking.Therefore,this study used an artificial intelligence-based machine learning approach to predict CSA-AKI from comprehensive perioperative data.Methods:In total,293 variables were analysed in the clinical data of patients undergoing off-pump CABG in the Department of Cardiac Surgery at the First Affiliated Hospital of Guangxi Medical University between 2012 and 2021.According to the KDIGO criteria,postoperative AKI was defined by an elevation of at least 50%within 7 days,or 0.3 mg/dL within 48 hours,with respect to the reference serum creatinine level.Five machine learning algorithms—a simple decision tree,random forest,support vector machine,extreme gradient boosting and gradient boosting decision tree(GBDT)—were used to construct the CSA-AKI predictive model.The performance of these models was evaluated with the area under the receiver operating characteristic curve(AUC).Shapley additive explanation(SHAP)values were used to explain the predictive model.Results:The three most influential features in the importance matrix plot were 1-day postoperative serum potassium concentration,1-day postoperative serum magnesium ion concentration,and 1-day postoperative serum creatine phos-phokinase concentration.Conclusion:GBDT exhibited the largest AUC(0.87)and can be used to predict the risk of AKI development after surgery,thus enabling clinicians to optimise treatment strategies and minimise postoperative complications.展开更多
基金supported by Guangxi Natural Science Foundation(2023GXNSFAA026128).
文摘The role and regulatory mechanisms of macrophage polarization in cardiac transplantation have gained significant attention.Macrophages can polarize into either the M1(pro-inflammatory)or M2(anti-inflammatory)phenotype in response to environmental cues.M1 macrophages facilitate transplant rejection by releasing inflammatory mediators and activating T cells,whereas M2 macrophages support graft survival by secreting antiinflammatory factors and promoting tissue repair.Mitochondrial quality control regulation plays a crucial role in macrophage polarization,which may influence graft survival and immune responses.This review provides an overview of the current understanding of mitochondrial quality control-regulated macrophage polarization in cardiac transplantation,its effects on graft outcomes,and potential therapeutic strategies to modulate this process to enhance transplant success rates.The review was conducted by systematically analyzing recent studies and integrating findings from key research articles to synthesize a comprehensive understanding of this emerging field.
基金This study was partly supported by Natural Science Foundation of China(No.82060082).
文摘Background:Cardiac surgery-associated acute kidney injury(CSA-AKI)is a major complication that increases morbidity and mortality after cardiac surgery.Most established predictive models are limited to the analysis of nonlinear relationships and do not adequately consider intraoperative variables and early postoperative variables.Nonextracorporeal circulation coronary artery bypass grafting(off-pump CABG)remains the procedure of choice for most coronary surgeries,and refined CSA-AKI predictive models for off-pump CABG are notably lacking.Therefore,this study used an artificial intelligence-based machine learning approach to predict CSA-AKI from comprehensive perioperative data.Methods:In total,293 variables were analysed in the clinical data of patients undergoing off-pump CABG in the Department of Cardiac Surgery at the First Affiliated Hospital of Guangxi Medical University between 2012 and 2021.According to the KDIGO criteria,postoperative AKI was defined by an elevation of at least 50%within 7 days,or 0.3 mg/dL within 48 hours,with respect to the reference serum creatinine level.Five machine learning algorithms—a simple decision tree,random forest,support vector machine,extreme gradient boosting and gradient boosting decision tree(GBDT)—were used to construct the CSA-AKI predictive model.The performance of these models was evaluated with the area under the receiver operating characteristic curve(AUC).Shapley additive explanation(SHAP)values were used to explain the predictive model.Results:The three most influential features in the importance matrix plot were 1-day postoperative serum potassium concentration,1-day postoperative serum magnesium ion concentration,and 1-day postoperative serum creatine phos-phokinase concentration.Conclusion:GBDT exhibited the largest AUC(0.87)and can be used to predict the risk of AKI development after surgery,thus enabling clinicians to optimise treatment strategies and minimise postoperative complications.
