Heart failure potentially affects the cortical structure of the brain.

BACKGROUND: Heart failure (HF) has been reported to affect cerebral cortex structure, but the underlying cause has not been determined. This study used Mendelian randomization (MR) to reveal the causal relationship between HF and structural changes in the cerebral cortex. METHODS: HF was defined as the exposure variable, and cerebral cortex structure was defined as the outcome variable. Inverse-variance weighted (IVW), MR-Egger regression and weighted median (WME) were performed for MR analysis; MR-PRESSO and Egger's intercept was used to test horizontal pleiotropy; and "leave-one-out" was used for sensitivity analysis. RESULTS: Fifty-two single nucleotide polymorphisms (SNPs) were defined as instrumental variables (IVs), and there was no horizontal pleiotropy in the IVs. According to the IVW analysis, the OR and 95% CI of cerebral cortex thickness were 0.9932 (0.9868-1.00) (P=0.0402), and the MR-Egger intercept was -15.6× 10-5 (P = 0.7974) and the Global test pval was 0.078. The P-value of the cerebral cortex surface was 0.2205, and the MR-Egger intercept was -34.69052 (P= 0.6984) and the Global Test pval was 0.045. HF had a causal effect on the surface area of the caudal middle frontal lobule (P=0.009), insula lobule (P=0.01), precuneus lobule (P=0.049) and superior parietal lobule (P=0.044). CONCLUSIONS: HF was potentially associated with changes in cortical thickness and in the surface area of the caudal middle frontal lobule, insula lobule, precuneus lobule and superior parietal lobule.

The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis.

INTRODUCTION: Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to systematically evaluate the performance and value of ML models for predicting HF prognosis. METHODS: PubMed, Web of Science, Scopus, and Embase online databases were searched up to April 30, 2023, to identify studies on the use of ML models to predict HF prognosis. HF prognosis primarily encompasses readmission and mortality. The meta-analysis was conducted by MedCalc software. Subgroup analyses include grouping based on types of ML models, time interval, sample sizes, the number of predictive variables, validation methods, whether to conduct hyperparameter optimization and calibration, data set partitioning methods. RESULTS: A total of 31 studies were included. The most common ML models were random forest, boosting, support vector machine, neural network. The area under the receiver operating characteristic curve (AUC) for predicting HF readmission was 0.675 (95% CI 0.651-0.699, P<0.001), and the AUC for predicting HF mortality was 0.790 (95% CI 0.765-0.816, P<0.001). Subgroup analyses revealed that models with the prediction time interval of 1 year, sample sizes =10,000, the number of predictive variables =100, external validation, hyperparameter tuning, calibration adjustment, and data set partitioning using 10-fold cross-validation exhibited favorable performance within their respective subgroups. CONCLUSION: The performance of ML models in predicting HF readmission is relatively poor, while its performance in predicting HF mortality is moderate. The quality of the relevant studies is generally low, it is essential to enhance the predictive capabilities of ML models through targeted improvements in practical applications.

Qifu Yixin Formula Improves Heart Failure by Enhancing ß-Arrestin2 Mediated the SUMOylation of SERCA2a.

Purpose: This study aimed to elucidate the protective mechanism of Traditional Chinese Medicine (TCM) Qifu Yixin formula (QFYXF) to improve heart failure (HF) by promoting ß-arrestin2 (ß-arr2)-mediated SERCA2a SUMOylation. Materials and Methods: The transverse aortic constriction (TAC)-induced HF mice were treated with QFYXF or carvedilol for 8 weeks. ß-arr2-KO mice and their littermate wild-type (WT) mice were used as controls. Neonatal rat cardiomyocytes (NRCMs) were used in vitro. Cardiac function was evaluated by echocardiography and serum NT-proBNP. Myocardial hypertrophy and myocardial fibrosis were assessed by histological staining. ß-arr2, SERCA2a, SUMO1, PLB and p-PLB expressions were detected by Western blotting, immunofluorescence and immunohistochemistry. SERCA2a SUMOylation was detected by Co-IP. The molecular docking method was used to predict the binding ability of the main active components of QFYXF to ß-arr2, SERCA2a, and SUMO1, and the binding degree of SERCA2a to SUMO1 protein. Results: The HF model was constructed 8 weeks after TAC. QFYXF ameliorated cardiac function, inhibiting myocardial hypertrophy and fibrosis. QFYXF promoted SERCA2a expression and SERCA2a SUMOylation. Further investigation showed that QFYXF promoted ß-arr2 expression, whereas Barbadin (ß-arr2 inhibitor) or ß-arr2-KO reduced SERCA2a SUMOylation and attenuated the protective effect of QFYXF improved HF. Molecular docking showed that the main active components of QFYXF had good binding activities with ß-arr2, SERCA2a, and SUMO1, and SERCA2a had a high binding degree with SUMO1 protein. Conclusion: QFYXF improves HF by promoting ß-arr2 mediated SERCA2a SUMOylation and increasing SERCA2a expression.

Analysis of risk factors for the efficacy of tirofiban in the treatment of acute ischemic stroke.

OBJECTIVE: To analyse the risk factors for tirofiban efficacy in the early treatment of acute ischemic stroke. METHODS: The clinical data of 204 patients with acute ischemic stroke treated with tirofiban were retrospectively analysed. The early efficacy of tirofiban was assessed by a ≥ 4-point decline in the National Institutes of Health Stroke Scale (NIHSS) score or via the complete disappearance of neurological deficits at the end of ischemic stroke treatment, and patients were divided into an effective groupand an ineffective group. Univariate and multivariate logistic regression analyses were used to compare the differences in clinical data between the two groups. RESULTS: Multivariate logistic regression analysis showed that heavy drinking (OR 0.477, 95% CI 0.249-0.899, P = 0.023), elevated total cholesterol (OR 0.331, 95% CI 0.141-0.734, P = 0.008), NIHSS score at initiation of treatment (OR 1.130, 95% CI 1.026-1.253, P = 0.016) and time from onset to treatment (OR 0.839, 95% CI 0.700-0.979, P = 0.038) were independent risk factors affecting the early efficacy of tirofiban. CONCLUSION: The early curative effect of tirofiban in acute ischemic stroke patients with a heavy drinking history and elevated total cholesterol was poor. In patients with acute ischemic stroke, the higher the NIHSS score was within a certain range (8 < NIHSS ≤15 and the Org 10,172 Trial in the Treatment of Acute Stroke (TOAST) belongs to small-artery occlusion lacunar) at the initiation of treatment and the shorter the time from onset to treatment, the better the early curative effect was.

Safety and efficacy of low-dose and long-course tirofiban in large hemispheric infarction.

Background: The clinical efficacy and safety of tirofiban in the treatment of large hemispheric infarction (LHI) remain controversial. Methods: This study prospectively enrolled patients with acute LHI who were admitted to Putuo Hospital affiliated with Shanghai University of Traditional Chinese Medicine from June 2021 to December 2021. The patients were randomly assigned to the tirofiban group [3-4 µg/(kg·h)] or control group (clopidogrel 75 mg/d). Results: A total of 71 patients with acute LHI were selected: 36 in the tirofiban group and 35 in the control group. The reduction of the NIHSS score in the tirofiban group was 2.92 ± 9.31 at discharge, and that of the control group was -3.23 ± 12.06 (p = 0.021, OR, 0.006; 95% CI, 0.004-0.008). Six patients (16.7%) in tirofiban group and 14 patients (40%) in control group died during hospitalization (p = 0.029, OR, 0.300; 95% CI, 0.099-0.908). There was significant difference in Modified Rankin Scale (mRS) 5-6 scores at 90 days between the two groups (p = 0.023, OR, 0.327; 95% CI, 0.124-0.867). However, there was no significant difference in mRS 0-1 (p = 0.321, OR, 0.972; 95% CI, 0.920-1.027), mRS 2 (p = 0.572, OR, 2.00; 95% CI, 0.173-23.109), mRS 3 (p = 0.225, OR, 2.214; 95% CI, 0.601-8.161), or mRS 4(p = 0.284, OR, 1.859; 95% CI, 0.593-5.825) scores between the two groups. There was no difference in symptomatic intracranial hemorrhage (p = 0.29, OR, 0.305; 95% CI, 0.030-3.081), asymptomatic intracranial hemorrhage (p = 0.123, OR, 0.284; 95% CI, 0.053-1.518). There was a significant difference in systemic bleeding events during hospitalization (p = 0.044, OR, 0.309; 95% CI, 0.096-1.000). Conclusions: Low-dose and long-course tirofiban treatment may significantly improve the early neurological function and reduce the in-hospital mortality in LHI patients. Meanwhile, tirofiban does not increase the risk of any type of bleeding events.