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| http://dx.doi.org/10.14744/AnatolJCardiol.2017.7805 | DOI Listing |
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Bioequivalence and Food Effect Assessment Between 2 Oral Tablet Formulations of Rivaroxaban 20 mg in Healthy Chinese Subjects.
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Clinical Trial Center, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, Zhejiang, China.
To evaluate the bioequivalence and safety of oral rivaroxaban tablets between a test formulation and a reference formulation in healthy Chinese subjects, a randomized, open, 2-formulation, 4-cycle, complete repeat crossover study was conducted under both fasting and fed states. Thirty-six healthy participants were enrolled separately for the fasting and fed groups, and each subject received a single oral dose of 20 mg of the test or reference formulation of rivaroxaban tablets per cycle. Blood samples were collected at specified intervals, and rivaroxaban was analyzed using liquid chromatography-tandem mass spectrometry.
View Article and Find Full Text PDFClinical significance of a new diagnosis of atrial fibrillation in patients with vascular disease.
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Population Health Research Institute, McMaster University, 237 Barton Street East, Hamilton, ON, L8L 2X2, Ontario, Canada.
Background: The Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial enrolled patients with vascular disease, but excluded patients requiring oral anticoagulation.
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Institute of Clinical Pharmacology, Peking University First Hospital, Beijing, China.
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Gender inequalities in prescribing and initiation patterns of guideline-recommended drugs after acute myocardial infarction.
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Grupo de Investigación en Servicios Sanitarios de Aragón (GRISSA), Fundación Instituto de Investigación Sanitaria de Aragón (IIS Aragón), Zaragoza, Spain.
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View Article and Find Full Text PDFMachine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization.
Sci Rep
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Department of Oral & Maxillofacial Surgery and Diagnostic Sciences, Faculty of Dentistry, Taif University, 21944, Taif, Saudi Arabia.
This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance.
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