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Background: Influenza is associated with an increased risk for cardiovascular events in patients with heart failure (HF). This study aimed to investigate the prevalence of influenza vaccination among Japanese patients with HF enrolled in the PARALLEL-HF (Prospective comparison of ARNI with ACEi to determine the noveL beneficiaL trEatment vaLue in Japanese Heart Failure patients) trial and the association between receiving influenza vaccination and cardiovascular events including death or HF hospitalization.

Methods And Results: In PARALLEL-HF, in which 223 patients with HF and reduced ejection fraction (HFrEF) were randomized to the angiotensin-receptor neprilysin inhibitor (sacubitril/valsartan) or enalapril, 97 (43%) received influenza vaccination.

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Background: Effects of the renin-angiotensin-aldosterone system (RAAS) inhibitors enalapril and telmisartan on circulating RAAS in dogs with proteinuric chronic kidney disease (pCKD) are undescribed.

Objectives: To characterize the RAAS in untreated dogs with pCKD compared to healthy, life-stage- and sex-matched controls, and in dogs with pCKD after 30 days of treatment with enalapril or telmisartan.

Animals: Dogs with pCKD (n = 36) and healthy controls (n = 20).

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Modulating Mitochondrial Dynamics Mitigates Cognitive Impairment in Rats with Myocardial Infarction.

Curr Neuropharmacol

June 2024

Neurophysiology Unit, Cardiac Electrophysiology Research and Training Center, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand.

Background: We have previously demonstrated that oxidative stress and brain mitochondrial dysfunction are key mediators of brain pathology during myocardial infarction (MI).

Objective: To investigate the beneficial effects of mitochondrial dynamic modulators, including mitochondrial fission inhibitor (Mdivi-1) and mitochondrial fusion promotor (M1), on cognitive function and molecular signaling in the brain of MI rats in comparison with the effect of enalapril.

Methods: Male rats were assigned to either sham or MI operation.

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Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature.

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Purpose: To identify associations between systemic drugs and the incidence of diabetic macular oedema (DME). Of interest was to find beneficial and/or deleterious associations of used drugs.

Methods: A historic cohort design based on administrative data.

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