Improving our understanding of autism, ADHD, dyslexia and other neurodevelopmental conditions requires collaborations between genetics, psychiatry, the social sciences and other fields of research.
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| http://dx.doi.org/10.7554/eLife.98461 | DOI Listing |
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Reevaluating gender-affirming care: biological foundations, ethical dilemmas, and the complexities of gender dysphoria.
J Sex Marital Ther
January 2025
Department of Interdisciplinary Studies, University of South Florida, Tampa, FL, USA.
This paper critically examines the scientific and ethical underpinnings of gender-affirming care, particularly for minors. While major medical organizations endorse such interventions as medically necessary, the evidence supporting their long-term safety and efficacy remains limited. Research into hormonal, neuroanatomic, and genetic influences reveals a strong biological basis for gender identity, challenging social constructionist arguments.
View Article and Find Full Text PDFInterdisciplinary systems may restore the healthcare professional-patient relationship in electronic health systems.
J Am Med Inform Assoc
January 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, United States.
Objective: To develop a framework that models the impact of electronic health record (EHR) systems on healthcare professionals' well-being and their relationships with patients, using interdisciplinary insights to guide machine learning in identifying value patterns important to healthcare professionals in EHR systems.
Materials And Methods: A theoretical framework of EHR systems' implementation was developed using interdisciplinary literature from healthcare, information systems, and management science focusing on the systems approach, clinical decision-making, and interface terminologies.
Observations: Healthcare professionals balance personal norms of narrative and data-driven communication in knowledge creation for EHRs by integrating detailed patient stories with structured data.
Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding.
View Article and Find Full Text PDFDeveloping a Research Center for Artificial Intelligence in Medicine.
Mayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
View Article and Find Full Text PDFStem cells adapt to their local mechanical environment by rearranging their cytoskeleton, which underpins the evolution of their shape and fate as well as the emergence of tissue structure and function. Here, in the second part of a two-part experimental series, we aimed to elucidate spatiotemporal cytoskeletal remodeling and resulting changes in morphology and mechanical properties of cells and their nuclei. Akin to mechanical testing of the most basic living and adapting unit of life, i.
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