Results 161 to 170 of about 295,419 (335)
Evaluation of Antidepressant Activity of Plocama pendula root extract
Dr.Kuppala Manohar Babu +4 more
openalex +2 more sources
Phenolic Profile and Bioactive Prospects of Wild Annona Species From Angola
ABSTRACT Annona species (Annonaceae family) are valued for their nutritional and medicinal importance, especially in traditional medicine. This study investigated the phenolic profiles of the Angolan Annona muricata, Annona squamosa, and Annona senegalensis leaves, stem barks, and seeds hydroethanolic, infusion, and decoction extracts, also evaluating ...
Josefa Rangel +9 more
wiley +1 more source
Repeated Unpredictable Stress and Antidepressants Differentially Regulate Expression of the Bcl-2 Family of Apoptotic Genes in Rat Cortical, Hippocampal, and Limbic Brain Structures [PDF]
Therese A. Kosten +4 more
openalex +1 more source
Abstract This Phase 1, randomized, placebo‐controlled, double‐blind study assessed the pharmacokinetic profile of rimegepant (25, 75, or 150 mg once daily for 14 days) in healthy Japanese and Caucasian adults. Exposures were modestly increased in Japanese participants compared with Caucasian participants following a single dose of rimegepant (Day 1 ...
Rajinder Bhardwaj +6 more
wiley +1 more source
Potential adverse drug events can be signaled in Clinical Decision Support Systems (CDSSs). This study validated a Swedish CDSS (Janusmed Risk Profile) by investigating associations between calculated risk classifications of drugs with QT‐prolonging potential and registered related clinical outcomes.
Ola Nordqvist +6 more
wiley +1 more source
Assessing the impact of pre‐test education on patient knowledge, perceptions, and expectations of pharmacogenomic testing to guide antidepressant use [PDF]
Nicholette T. Sloat +3 more
openalex +1 more source
Paediatric developmental safety and the ICH E11A extrapolation of safety
British Journal of Clinical Pharmacology, EarlyView.
Gilbert J. Burckart +6 more
wiley +1 more source
Applying Machine Learning to Predict Complex Clinical Course in Youth With Eating Disorders
ABSTRACT Objective To compare the predictive performance of supervised machine learning models to logistic regression in identifying youth with eating disorders at risk of a complex clinical course based on clinical characteristics from the first treatment episode.
Stephanie Ryall +3 more
wiley +1 more source

