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Background: Medications known to improve outcomes in heart failure (HF) are either not prescribed or prescribed at sub-therapeutic doses. The addition of clinical pharmacists to the HF team positively impacts optimizing prognostic medications for a patient with HF with reduced ejection fraction (HFrEF).
Objective: To assess the intervention of the clinical pharmacist as part of the multidisciplinary (MD) team in up-titration to achieve target doses of key therapeutic agents for HFrEF.
Methods: This was a prospective one group pretest-posttest interventional study; a comparison of the target dose achievement of key therapeutic agents for HFrEF was performed before and after clinical pharmacist interventions.
Results: Out of 110 HFrEF patients, 57.3% were males, and the mean age of patients was 55.8 years (SD 12.6). Cardiomyopathy was the leading cause of HF. At baseline, 86% were on angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (ACEIs/ARBs/ARNi) and 93.6% on beta blockers (BBs). At the end of study, the proportion of patients achieved the target dose was significantly increased (0 vs 77.4%, 6.8 vs 85.4%, and 0 vs 55.6%) for ACEIs, ARBs and ARNi, respectively, and (8.6% vs 66.1%; P = 0.001) for BBs. Moreover, the up-titration process was associated with significant improvement in most clinical as ejection fraction and New York Heart Association (NYHA) scale and laboratory characteristics.
Conclusion: As a part of the MD team in the outpatient HF clinic, the clinical pharmacists increased the percentage of HFrEF patients achieving the target or maximal doses of key therapeutic agents and improving clinical and laboratory parameters.
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http://dx.doi.org/10.2147/IPRP.S341621 | DOI Listing |
Nano Lett
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Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha 410082, China.
Interleukin-12 (IL-12) is a robust proinflammatory cytokine that activates immune cells, such as T cells and natural killer cells, to induce antitumor immunity. However, the clinical application of recombinant IL-12 has been limited by systemic immune-related adverse events (irAEs) and rapid degradation. To address these challenges, we employed mRNA technology to encode a tumor-activated IL-12 "lock" fusion protein that offers both therapeutic efficacy and systemic safety.
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September 2025
Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla.
Importance: Janus kinase (JAK) inhibitors are highly effective medications for several immune-mediated inflammatory diseases (IMIDs). However, safety concerns have led to regulatory restrictions.
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Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277, Jiefang Avenue, Wuhan, Hubei, 430022, China.
Major depression disorder (MDD) is a mental condition that significantly threatens both physical and psychological health. This study aimed to discern variances in plasma metabolic profiles between MDD sufferers and healthy counterparts. Additionally, we tracked the hospitalization journey of MDD patients to investigate the normalization of metabolic irregularities through conventional treatment in the form of self-control.
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Neuropsychology and Applied Cognitive Neuroscience Laboratory, State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Reward processing involves several components, including reward anticipation, cost-effort computation, reward consumption, reward sensitivity, and reward learning. Recent research has highlighted the cerebellum's role in reward processing. This study aimed to investigate the effects of cerebellar stimulation on reward processing using high-definition transcranial direct current stimulation (HD-tDCS).
View Article and Find Full Text PDFMol Divers
September 2025
Department of Biotechnology, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492001, India.
Traditional drug discovery methods like high-throughput screening and molecular docking are slow and costly. This study introduces a machine learning framework to predict bioactivity (pIC₅₀) and identify key molecular properties and structural features for targeting Trypanothione reductase (TR), Protein kinase C theta (PKC-θ), and Cannabinoid receptor 1 (CB1) using data from the ChEMBL database. Molecular fingerprints, generated via PaDEL-Descriptor and RDKit, encoded structural features as binary vectors.
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