Myocardial infarction is a cardiovascular disease (CVD) with high morbidity and mortality, which can trigger a cascade of cardiac pathophysiological changes, including fibrosis, inflammation, ischemia-reperfusion injury (IRI), and ventricular remodeling, ultimately leading to heart failure (HF). While conventional pharmacological treatments and clinical reperfusion therapy may enhance short-term prognoses and emergency survival rates, both approaches have limitations and adverse effects. Natural products (NPs) are extensively utilized as therapeutics globally, with some demonstrating potentially favorable therapeutic effects in preclinical and clinical pharmacological studies, positioning them as potential alternatives to modern drugs. This review comprehensively elucidates the pathophysiological mechanisms during myocardial infarction and summarizes the mechanisms by which NPs exert cardiac beneficial effects. These include classical mechanisms such as inhibition of inflammation and oxidative stress, alleviation of cardiomyocyte death, attenuation of cardiac fibrosis, improvement of angiogenesis, and emerging mechanisms such as cardiac metabolic regulation and histone modification. Furthermore, the review emphasizes the modulation by NPs of novel targets or signaling pathways in classical mechanisms, including other forms of regulated cell death (RCD), endothelial-mesenchymal transition, non-coding ribonucleic acids (ncRNAs) cascade, and endothelial progenitor cell (EPC) function. Additionally, NPs influencing a particular mechanism are categorized based on their chemical structure, and their relevance is discussed. Finally, the current limitations and prospects of NPs therapy are considered, highlighting their potential for use in myocardial infarction management and identifying issues that require urgent attention.
Humans ; Myocardial Infarction/genetics* ; Biological Products/therapeutic use* ; Animals ; Oxidative Stress/drug effects* ; Signal Transduction/drug effects*

Objective To establish a rapid analysis method for cyanide based on a ratiometric fluores-cent probe,providing a quantitative strategy for on-site visual and rapid detection of cyanide.Methods A dual-emission ratiometric fluorescent probe(AuNCs-FL)was constructed by using bovine serum al-bumin(BSA)-stabilized gold nanoclusters(AuNCs,fluorescence emission at 660 nm)as the responsive signal unit and fluorescein(FL,emission at 515 nm)as the internal reference.Results The etching effect of cyanide on AuNCs resulted in fluorescence quenching at 660 nm,while the fluorescence inten-sity of FL at 515 nm remained unchanged,enabling a rapid response analysis of cyanide shift from red to green fluorescence.The developed probe enabled rapid analysis of cyanide within 3 min,with a limit of detection(LOD)of 3.4 mg/L and a visual detection range of 10-100 mg/L.Conclusion The AuNCs-FL fluorescent probe is structurally simple,low-cost,and easy to operate,delivering rapid and accurate results.It also avoids the interference from sulfides encountered in commercial cyanide test kits,making it suitable for the on-site rapid detection of suspected powder samples in cyanide poisoning cases.

In recent years,emerging evidence has revealed the non-classical biological functions of platelets.Research on the regulation of platelet bioenergetic metabolism and its functional implications is of great significance for understanding the mecha-nisms of related diseases and developing novel therapeutic strate-gies.Current research is focusing on platelet-derived mitochon-dria because of their distinct biological characteristics.Notably,the ability of platelets to transfer mitochondria to recipient cells can reprogram the metabolism and function of these cells,poten-tially playing a pivotal role in diverse pathophysiological proces-ses.This review comprehensively summarizes the emerging roles of platelet-derived mitochondria transfer in various diseases in-cluding diabetes mellitus,cardiovascular disorders,neurodegen-erative diseases,and cancer.It further explores the application prospects in these diseases,providing a scientific basis for the development of precise therapeutic strategies.

In recent years,emerging evidence has revealed the non-classical biological functions of platelets.Research on the regulation of platelet bioenergetic metabolism and its functional implications is of great significance for understanding the mecha-nisms of related diseases and developing novel therapeutic strate-gies.Current research is focusing on platelet-derived mitochon-dria because of their distinct biological characteristics.Notably,the ability of platelets to transfer mitochondria to recipient cells can reprogram the metabolism and function of these cells,poten-tially playing a pivotal role in diverse pathophysiological proces-ses.This review comprehensively summarizes the emerging roles of platelet-derived mitochondria transfer in various diseases in-cluding diabetes mellitus,cardiovascular disorders,neurodegen-erative diseases,and cancer.It further explores the application prospects in these diseases,providing a scientific basis for the development of precise therapeutic strategies.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.