Shunxin decoction improves diastolic function in rats with heart failure with preserved ejection fraction induced by abdominal aorta constriction through cyclic guanosine monophosphate-dependent protein kinase Signaling Pathway.

OBJECTIVE: To determine whether Shunxin decoction improves diastolic function in rats with heart failure with preserved ejection fraction (HFpEF) by regulating the cyclic guanosine monophosphate-dependent protein kinase (cGMP-PKG) signaling pathway. METHODS: Except for control group 8 and sham surgery group 8, the remaining 32 male Sprague-Dawlay rats were developed into HFpEF rat models using the abdominal aorta constriction method. These rats in the HFpEF model were randomly divided into the model group, the Shunxin high-dose group, the Shunxin low-dose group, and the Qiliqiangxin capsule group. The three groups received high-dose Shunxin decoction, low-dose Shunxin decoction, and Qiliqiangxin capsule by gavage, respectively, for 14 d. After the intervention, the diastolic function of each rat was evaluated by testing E/A, heart index, hematoxylin-eosin staining, Masson, myocardial ultrastructure, and N-terminal pro-brain natriuretic peptide (NT-proBNP). The Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) software was used to predict targets for which Shunxin decoction acts on the cGMP-PKG pathway. Natriuretic peptide receptor A (NPRA) and guanylate cyclase (GC) were detected by immunohistochemistry, and eNOS, phosphodiesterase 5A (PDE5A), and cGMP-dependent protein kinase 1(PKG I) were determined by Western blotting. RESULTS: Compared to the model group, the thickness of the interventricular septum at the end of diastole (IVSd) and the thickness of the posterior wall at the end of diastole (PWd) of the Shunxin decoction high-dose group, Shunxin decoction low-dose group, and Qiliqiangxin capsule group were all significantly reduced ( < 0.01). Furthermore, Shunxin decoction high-dose group E/A value was decreased ( < 0.01). Compared to the model group, the expression of NPRA and GC increased in the Shunxin decoction low-dose group and the Qiliqiangxin capsule group ( < 0.01). Compared to the model group, the expressions of eNOS and PKG I increased ( < 0.05) in the Shunxin decoction high-dose group. The expression of PDE5A expression decreased in the myocardium of the Shunxin decoction high-dose group, Shunxin decoction low-dose group, and Qiliqiangxin capsule group compared to the model group ( < 0.01). CONCLUSIONS: Shunxin decoction can improve diastolic function in rats with HFpEF. It increases the expression of NPRA, GC, and eNOS in the myocardial cell cGMP-PKG signaling pathway, upregulates cGMP expression, decreases PDE5A expression to reduce the cGMP degradation. Thus, the cGMP continually stimulates PKG I, reversing myocardial hypertrophy and improving myocardial compliance in HFpEF rats.

A machine learning-based risk warning platform for potentially inappropriate prescriptions for elderly patients with cardiovascular disease.

Potentially inappropriate prescribing (PIP), including potentially inappropriate medications (PIMs) and potential prescribing omissions (PPOs), is a major risk factor for adverse drug reactions (ADRs). Establishing a risk warning model for PIP to screen high-risk patients and implementing targeted interventions would significantly reduce the occurrence of PIP and adverse drug events. Elderly patients with cardiovascular disease hospitalized at the Sichuan Provincial People's Hospital were included in the study. Information about PIP, PIM, and PPO was obtained by reviewing patient prescriptions according to the STOPP/START criteria (2nd edition). Data were divided into a training set and test set at a ratio of 8:2. Five sampling methods, three feature screening methods, and eighteen machine learning algorithms were used to handle data and establish risk warning models. A 10-fold cross-validation method was employed for internal validation in the training set, and the bootstrap method was used for external validation in the test set. The performances were assessed by area under the receiver operating characteristic curve (AUC), and the risk warning platform was developed based on the best models. The contributions of features were interpreted using SHapley Additive ExPlanation (SHAP). A total of 404 patients were included in the study (318 [78.7%] with PIP; 112 [27.7%] with PIM; and 273 [67.6%] with PPO). After data sampling and feature selection, 15 datasets were obtained and 270 risk warning models were built based on them to predict PIP, PPO, and PIM, respectively. External validation showed that the AUCs of the best model for PIP, PPO, and PIM were 0.8341, 0.7007, and 0.7061, respectively. The results suggested that angina, number of medications, number of diseases, and age were the key factors in the PIP risk warning model. The risk warning platform was established to predict PIP, PIM, and PPO, which has acceptable accuracy, prediction performance, and potential clinical application perspective.