Purpose: To identify the motivations of adolescent students applying into medical pipeline programs that are focused on populations underrepresented in medicine.
Methods: The Doctors of Tomorrow (DoT) program is a medical pipeline program between the University of Michigan Medical School and Cass Technical High School in Detroit, Michigan, USA. As a component of the application process, ninth-grade students complete multiple free response essays that allow students to articulate their reasons for applying and their goals for participation in the program. Between 2013 and 2019, 323 ninth-grade students applied to DoT and 216 were accepted. The authors qualitatively analyzed all applications using theoretical coding methods to identify common themes discussed by students regarding their motivations for applying. The authors used Dedoose 8.3.17 (Los Angeles, CA) for qualitative analysis.
Results: Four main themes emerged after coding and thematic analysis: (1) Career Aspiration, (2) Exposure to the Medical Field, (3) Breadth of Mentorship, and (4) Longitudinal Professional Development. 'Health Disparities in Minority Communities,' a code used when students commented on issues of race, social determinants of health, and health disparities as motivators, was not identified as frequently as the other codes, despite it being a main topic within the pipeline program.
Conclusions: Applicants to medical school pipeline programs articulate similar intrinsic motivations that can be used to inform what drives students to apply. Pipeline programs should consider these intrinsic motivations, while also creating structured activities from which students can learn and gain tangible benefits when designing curricula. While ninth-grade students acknowledge health disparities in minority communities, their current level of personal experience may not lead them to identify these disparities as significant motivators, and pipeline leaders should be aware of this when designing lesson plans concerning these topics.
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http://dx.doi.org/10.1016/j.jnma.2021.05.001 | DOI Listing |
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Gastroenterol Clin North Am
March 2025
Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi 110029, India. Electronic address:
Organ failure (OF) is a sinister development in the clinical course of acute pancreatitis, and its prediction is crucial for triaging the patient. Persistent systemic inflammatory response syndrome and raised interleukin-6 levels have a good predictive accuracy. Pathophysiology involves the release of damage-associated molecular patterns as a consequence of pancreatic injury, recruitment of inflammatory cells, and the release of proinflammatory cytokines and chemokines causing cytokine storm.
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January 2025
Applied Translational Neurogenomics Group, Vlaams Instituut voor Biotechnology (VIB) Center for Molecular Neurology, VIB, Antwerp, Belgium.
Objective: This study aims to improve genetic diagnosis in childhood onset epilepsy with neurodevelopmental problems by utilizing RNA sequencing of fibroblasts to identify pathogenic variants that may be missed by exome sequencing and copy number variation analysis.
Methods: We enrolled 41 individuals with childhood onset epilepsy and neurodevelopmental problems who previously had inconclusive genetic testing. Fibroblast samples were cultured and analyzed using RNA sequencing to detect aberrant expression, aberrant splicing, and monoallelic expression using the Detection of RNA Outlier Pipeline (DROP) pipeline.
J Virol
January 2025
MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom.
The unprecedented sequencing efforts during the COVID-19 pandemic paved the way for genomic surveillance to become a powerful tool for monitoring the evolution of circulating viruses. Herein, we discuss how a state-of-the-art artificial intelligence approach called protein language models (pLMs) can be used for effectively analyzing pathogen genomic data. We highlight examples of pLMs applied to predicting viral properties and evolution and lay out a framework for integrating pLMs into genomic surveillance pipelines.
View Article and Find Full Text PDFFront Artif Intell
January 2025
Language Intelligence and Information Retrieval (LIIR) Lab, Department of Computer Science, KU Leuven, Leuven, Belgium.
The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for efficient data mining. Natural language processing (NLP) methods have been pivotal in this endeavor, aiming to extract crucial clinical concepts embedded within free-form text.
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