To investigate the association between social capital and quality of life among type 2 diabetes patients in Anhui province, China. In a cross-sectional study, 436 adults with type 2 diabetes were interviewed. The two domains of Quality of life, physical component summary (PCS) and mental component summary (MCS), were measured using the Short-Form Health Survey (SF-36). A modified instrument scale was used to measure cognitive and structural social capital. Multiple logistic regression models were used to assess the associations between social capital and quality of life, adjusting for social economic status and risk factors for health. 24.3 % of participants (106) were in poor PCS and 25.0 % (109) in poor MCS. The proportions of participants who had low cognitive and structural social capital were 47.0 % (205) and 64.4 % (281), respectively. Results of logistic regression models showed that cognitive social capital was positively associated with PCS (OR = 1.84; 95 % CI: 1.12, 3.02) and MCS (OR = 1.65; 95 % CI: 1.03, 2.66). However, the associations between structural social capital and PCS (OR = 0.80, 95 % CI: 0.48, 1.34) and MCS (OR = 0.62; 95 % CI: 0.38, 1.01) were not statistically significant. It is the first study in China to investigate associations between quality of life and social capital in type 2 diabetes. Findings document that cognitive social capital is associated with the quality of life of type 2 diabetes patients. Our study suggests that the social capital theory may provide a new approach to increase physical resources in diabetes prevention and control, especially in Low and Middle Income countries (LMICs).

Background and objectives: Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients.Methods: A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient’ demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants’ prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.Results: Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.Conclusion: These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.

Stroke is a major global cause of death, disability, and socioeconomic burden. This study analyzed long-term trends in burden of stroke attributable to high systolic blood pressure (HSBP), to evaluate the effect of HSBP control in stroke prevention. The Global Burden of Disease (GBD) 2021 database was utilized. Crude and age-standardized incidence, prevalence, mortality, and disability-adjusted life-year (DALY) rates were employed as disease burden indicators. The Global and Different Socio-demographic Index (SDI) region trends in stroke burden were analyzed using Joinpoint analysis. Subgroup analyses were conducted by gender and age. The age-standardized incidence rate (AAPC=-0.81, 95%CI:-0.86 to -0.75), mortality rate (AAPC=-1.60, 95%CI: -1.77 to -1.43), prevalence rate (AAPC= -0.29, 95%CI: -0.30 to -0.28), and DALY rate (AAPC=-1.57, 95%CI:-1.67 to -1.47) exhibited a consistent downward trend from 1990 to 2021, but the absolute burden of stroke increased, especially in China. HSBP as the leading risk factor for stroke-related DALYs globally, accounting for 58.08% of stroke deaths and 57.25% of DALYs in 2021. Regional disparities were evident, lower SDI regions face disproportionate burden versus higher SDI regions Men exhibited higher HSBP-related stroke burden than women. Despite global improvements in hypertension management and a concomitant decline in HSBP-related stroke burden, disparities persist across SDI regions. Targeted interventions, including improved access to affordable antihypertensive medications, public health education, and lifestyle modifications, are crucial to reducing the burden of stroke.