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Enhancing Medical Student Engagement Through Cinematic Clinical Narratives: Multimodal Generative AI-Based Mixed Methods Study.
JMIR Med Educ
January 2025
Department of Medical Education, University of Idaho, 875 Perimeter Drive MS 4061, WWAMI Medical Education, Moscow, ID, 83844-9803, United States, 1 5092090908.
Background: Medical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teaching methods can lead to passive learning and poor long-term retention of critical concepts.
Objective: This study aims to enhance the teaching of clinical pharmacology in medical school by using a multimodal generative artificial intelligence (genAI) approach to create compelling, cinematic clinical narratives (CCNs).
Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.
J Alzheimers Dis
January 2025
Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA.
Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.
Objective: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults.
Methods: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.
Expert advice from ResearchMatch volunteers: Recruitment Innovation Center use cases and innovation.
J Clin Transl Sci
November 2024
Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA.
Involving participants in the design of clinical trials should improve the overall success of a study. For this to occur, streamlined mechanisms are needed to connect the populations potentially impacted by a given study or health topic with research teams in order to inform trial design in a meaningful and timely manner. To address this need, we developed an innovative mechanism called the "ResearchMatch Expert Advice Tool" that quickly obtains volunteer perspectives from populations with specific health conditions or lived experiences using the national recruitment registry, ResearchMatch.
View Article and Find Full Text PDFMicrodose Cocktail Study Reveals the Activity and Key Influencing Factors of OATP1B, P-Gp, BCRP, and CYP3A in End-Stage Renal Disease Patients.
Clin Pharmacol Ther
January 2025
Drug Clinical Trial Center, Peking University Third Hospital, Beijing, China.
OATP1B, P-gp, BCRP, and CYP3A are the most contributing drug-metabolizing enzymes or transporters (DMETs) for commonly prescribed medication. Their activities may change in end-stage renal disease (ESRD) patients with large inter-individual variabilities (IIVs), leading to altered substrate drug exposure and ultimately elevated safety risk. However, the changing extent and indictive influencing factors are not quantified so far.
View Article and Find Full Text PDFEvaluating the effect of noise reduction strategies in CT perfusion imaging for predicting infarct core with deep learning.
Neuroradiol J
January 2025
Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA.
This study evaluates the efficacy of deep learning models in identifying infarct tissue on computed tomography perfusion (CTP) scans from patients with acute ischemic stroke due to large vessel occlusion, specifically addressing the potential influence of varying noise reduction techniques implemented by different vendors. We analyzed CTP scans from 60 patients who underwent mechanical thrombectomy achieving a modified thrombolysis in cerebral infarction (mTICI) score of 2c or 3, ensuring minimal changes in the infarct core between the initial CTP and follow-up MR imaging. Noise reduction techniques, including principal component analysis (PCA), wavelet, non-local means (NLM), and a no denoising approach, were employed to create hemodynamic parameter maps.
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