Objective To systematically evaluate the incidence rate and predictors of early(≤48 h) malignant ventricular arrhythmia (MVA) in patients with acute myocardial infarction (AMI), and to establish and validate a clinical prediction model to assist in identifying high-risk patients. Methods The clinical data of 278 patients with AMI in the hospital from February 2023 to February 2025 were retrospectively analyzed. Based on MVA occurrence within 48 hours post-AMI, patients were divided into the MVA group (n=225 cases) and non-MVA group (n=53 cases).. The clinical data in the two groups were collected, and the predictive variables were determined by univariate Logistic analysis and multivariate Logistic regression analysis to establish a prediction model for MVA. Results The proportion of patients with Killip grade III or IV in MVA group was higher than that in non-MVA group (P<0.05), and the levels of white blood cell count (WBC), creatine kinase isoenzyme (CKMB) and troponin I (TnI) were also higher than those in non-MVA group (P<0.05) while the standard deviation of normal to normal RR intervals (SDNN) and left ventricular ejection fraction (LVEF) were lower than those in non-MVA group (P<0.05). Multivariate Logistic regression analysis showed that Killip grade≥III, high levels of WBC, CKMB and TnI and low SDNN and LVEF were independent risk factors of early MVA in patients with AMI (P<0.05). Based on the above six factors, a risk nomogram prediction model was constructed, and the model verification results showed that the area under the ROC curve (AUC) was 0.884 (95%CI: 0.835-0.932), with good model discrimination. The calibration curve was close to the ideal curve (Hosmer-Lemeshoe P=0.768), and the model had good predictive efficiency. The decision curve showed that the model had a higher predicted net benefit value (threshold=0.1-0.97). Conclusion Cardiac function Killip grade≥III, high WBC, CKMB and TnI and low SDNN and LVEF are independent risk factors of early MVA in AMI patients. The clinical prediction model based on the above variables has certain predictive value on the risk of MVA in AMI patients.

Gastric cancer is a common malignant tumor with complex pathological mechanisms， a low early diagnosis rate， and a high mortality rate. However， surgical treatment， targeted therapy， and chemotherapy have their treatment limitations and toxic side effects. Therefore， exploring the pathogenesis and mechanism of gastric cancer and finding effective treatment methods are important. At present， researches has found that tumor epithelial cells exhibit individual differences in molecular characteristics and exhibit metabolic heterogeneity that affects cell phenotype and function. The interaction between metabolites and cytokines can inhibit the formation of the tumor immune microenvironment and promote malignant progression. Therefore， metabolic reprogramming is regarded as a key feature of tumors and plays an important role in the process of tumor occurrence and development. However， the continuous deterioration of gastric cancer may be closely related to changes in the energy metabolism of cancer cells. Gastric cancer cells may regulate the dysregulation of synthesis or decomposition pathways such as glucose metabolism， amino acid metabolism， lipid metabolism， and nucleotide metabolism and activate associated signaling pathways， key proteins， and genes， leading to proliferation， invasion， and metastasis of cancer cells. In recent years， there has been a close relationship between the effective intervention by traditional Chinese medicine in gastric cancer and the regulation of metabolic reprogramming. There has been some progress in the intervention research on effective ingredients and formulas of traditional Chinese medicine for cancer. This article summarized existing Chinese and foreign literature on how gastric cancer cells affect disease progression by regulating their related metabolic networks， such as glucose metabolism， amino acid metabolism， lipid metabolism， and nucleotide metabolism， as well as how effective ingredients and formulas of traditional Chinese medicine enhance anti-tumor effects through targeted metabolism. It reviewed metabolic reprogramming intervention in gastric cancer， providing a reference for research on metabolic reprogramming regulation by traditional Chinese medicine and new targets and strategies for the treatment and prognosis of gastric cancer.