Objective:To investigate the temporal expression of Growth Differentiation Factor-15 (GDF15) in the serum of patients with Acute Coronary Syndrome (ACS) and explore the clinical significance of GDF15 in protecting cardiomyocytes in ACS.Methods:A retrospective study was conducted on 289 ACS patients admitted to the emergency departments from February to October 2023. Data on gender, age, troponin T (TnT), creatine kinase isoenzyme (CK-MB), GDF15, and B-type natriuretic peptide (BNP) within 30 minutes of admission were recorded. Differences in these indicators among different groups were compared. Receiver Operating Characteristic (ROC) curves were plotted to evaluate the diagnostic value of GDF15, TnT, and BNP for ACS. Among the patients, 15 exhibited a temporal expression pattern of GDF15, and their blood samples were re-measured using a GDF15 fluorescent quantitative immunochromatographic assay kit. Fifteen patients without temporal expression were randomly selected as controls, and their samples were also re-measured to exclude detection errors. Fifteen patients with temporal expression were included in the temporal expression group, and 15 without temporal expression were included in the non-temporal expression group. Laboratory indicators such as fasting blood glucose, glycated hemoglobin, triglycerides, creatinine, and uric acid were compared between the groups. Additionally, patient age, gender, body mass index (BMI), coronary angiography results, echocardiography, Gensini score, left ventricular ejection fraction (LVEF), and GRACE risk score were recorded to assess their correlation with GDF15 temporal expression. Statistical analysis was performed using SPSS 27 software, with continuous data expressed as mean ± standard deviation (Mean ± SD) and compared using t-tests and χ2 tests. Results:The overall trend in ACS patients showed a higher proportion of males than females (73.36% vs. 26.64%). The oldest group was the Unstable Angina (UA) group, with a mean age of (63.98 ± 15.19) years, while the youngest group was the non-ACS chest pain group, with a mean age of (54.29 ± 16.39) years. A higher proportion of patients in the UA, ST-segment elevation myocardial infarction (STEMI), and non-ST-segment elevation myocardial infarction (NSTEMI) groups had a history of smoking. The combination of GDF15 and TnT showed high diagnostic value for ACS, with an area under the ROC curve (AUC) of 0.843, consistent with previous studies. Among all ACS patients, 15 exhibited a temporal expression pattern of GDF15, where GDF15 levels peaked at 4 hours, gradually decreased, and peaked again at 24 hours. Patients in the temporal expression group had higher LVEF and left ventricular end-systolic diameter compared to the non-temporal expression group. The Gensini score was lower in the temporal expression group, and the GRACE risk score was significantly lower in the temporal expression group (00.7±14.72) compared to the non-temporal expression group (116.1±23.46), with a statistically significant difference ( P = 0.0115). There were no significant differences in general characteristics (age, gender, BMI) or clinical biochemical indicators (fasting blood glucose, glycated hemoglobin, triglycerides, total cholesterol, high-density lipoprotein, low-density lipoprotein, creatinine, uric acid) between the temporal and non-temporal expression groups ( P > 0.05). Conclusions:GDF15 demonstrates significant diagnostic and prognostic predictive value in ACS. Patients with temporally dynamic expression of serum GDF15 exhibit milder myocardial injury and a lower probability of mortality. These findings provide novel therapeutic targets and research directions for further exploring the role of GDF15 in ACS management.

Objective:To develop a risk prediction model for early cardiac arrest in emergency sepsis utilizing a machine learning algorithm to enhance the quality and efficiency of patient treatment.Methods:This study focused on patients with sepsis who received treatment at the emergency room of the First Medical Center of Chinese PLA General Hospital from January 1, 2020 to June 1, 2023. The basic clinical characteristics such as vital signs and laboratory results were collected. Patients who fulfilled the specified inclusion criteria were allocated randomly into a training group and a testing group with a ratio of 8:2. A CatBoost model was constructed using Python software, and the prediction efficiency of the model was assessed by calculating the area under the receiver operating characteristic curve (AUC). Furthermore, the performance of the model was compared to that of other widely employed clinical scores.Results:This study included a cohort of 2 131 patients diagnosed with sepsis, among whom 449 experienced cardiac arrest. The CatBoost model demonstrated an AUC of 0.760, surpassing other scores. Notably, the top 10 predictors in the model were identified as age, lactate, interleukin -6, oxygen saturation, albumin, N-terminal pro-B-type natriuretic peptide, potassium, sodium, creatinine, and platelets.Conclusions:The utilization of this machine learning algorithm-based prediction model offers a more precise basis for predicting cardiac arrest in emergency sepsis patients, thereby potentially improving the treatment efficacy for this disease.

The relationship between C936T polymorphism at 3'-untranslated region of vascular endothelial growth factor (VEGF) gene and diabetic nephropathy (DN) was analysed in 194 type 2 diabetic patients. The frequencies of genotype CC and allele C were significantly higher in DN group than those in non-DN group and control group. Allele C and genotype CC of VEGF may be a genetic marker susceptible to DN.