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Background: Online health information seeking is changing the way people engage with health care and the health system. Recent changes in practices related to seeking, accessing, and disseminating scientific research, and in particular health information, have enabled a high level of user engagement.
Objective: This study aims to examine an innovative model of research translation, The Huberman Lab Podcast (HLP), developed by Andrew Huberman, Professor of Neurobiology and Ophthalmology at the Stanford School of Medicine. The HLP leverages social media to deliver health information translated into specific, actionable practices and health strategies directly to the general public. This research characterizes the HLP as an Active Model of Research Translation and assesses its potential as a framework for replicability and wider adoption.
Methods: We applied conventional content analysis of the YouTube transcript data and directed content analysis of viewers' YouTube comments to 23 HLP episodes released from January to October 2021, reflecting the time of data analysis. We selected 7 episodes and a welcome video, to describe and identify key characteristics of the HLP model. We analyzed viewer comments for 18 episodes to determine whether viewers found the HLP content valuable, accessible, and easy to implement.
Results: The key HLP features are direct-to-the-consumer, zero-cost, bilingual, and actionable content. We identified 3 main organizing categories and 10 subcategories as the key elements of the HLP: (1) Why: Educate and Empower and Bring Zero Cost to Consumer Information to the General Public; (2) What: Tools and Protocols; Underlying Mechanisms; and Grounded in Science; (3) How: Linear and Iterative Knowledge Building Process; Lecture-Style Sessions; Interactive and Consumer Informed; Easily Accessible; and Building the Community. Analysis of viewers' comments found strong consumer support for the key HLP model elements.
Conclusions: This Active Model of Research Translation offers a way to synthesize scientific evidence and deliver it directly to end users in the form of actionable tools and education. Timely evidence translation using effective consumer engagement and education techniques appears to improve access and confidence related to health information use and reduces challenges to understanding and applying health information received from health providers. Framing complex content in an approachable manner, engaging the target audience, encouraging participation, and ensuring open access to the content meet current recommendations on innovative practices for leveraging social media or other digital platforms for disseminating science and research findings to the general public, and are likely key contributors to HLP impact and potential for success. The model offers a replicable framework for translating and disseminating scientific evidence. Similar active models of research translation can have implications for accessing health information and implementing health strategies for improved outcomes. Areas for further investigation are specific and measurable impacts on health, usability, and relevance of the model for reaching marginalized and high-risk populations.
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http://dx.doi.org/10.2196/46611 | DOI Listing |
Hum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFStroke
September 2025
Department of Medicine, University of Melbourne, Parkville, Victoria, Australia. (V.Y., B.C.V.C., L.C., L.O., M.W.P.).
Background: To assess the efficacy and safety of tenecteplase in patients presenting within 24 hours of symptom onset with a large vessel occlusion and target mismatch on perfusion computed tomography.
Methods: ETERNAL-LVO was a prospective, randomized, open-label, blinded end point, phase 3, superiority trial where adult participants with a large vessel occlusion, presenting within 24 hours of onset with salvageable tissue on computed tomography perfusion, were randomized to tenecteplase 0.25 mg/kg or standard care across 11 primary and comprehensive stroke centers in Australia.
Mol Ther Methods Clin Dev
June 2025
Precision Safety, Pharma Product Development, Roche Innovation Center Basel, CH-4070 Basel, Switzerland.
Adeno-associated virus (AAV) vectors are widely used in gene therapy, particularly for liver-targeted treatments. However, predicting human-specific outcomes, such as transduction efficiency and hepatotoxicity, remains challenging. Reliable models are urgently needed to bridge the gap between preclinical studies and clinical applications.
View Article and Find Full Text PDFFront Immunol
September 2025
Department of Pediatrics, Taichung Veterans General Hospital, Taichung, Taiwan.
Introduction: Human papillomavirus (HPV) infection has been implicated in autoimmune processes, yet concerns remain about the potential autoimmune risks of HPV vaccination. Juvenile idiopathic arthritis (JIA) is a chronic autoimmune condition that typically manifests in childhood. The relationship between HPV vaccination and the development of JIA remains uncertain.
View Article and Find Full Text PDFFront Immunol
September 2025
Department of Medicine, Division of Hematology, Bioclinicum and Center for Molecular Medicine, Karolinska Institute and Karolinska University Hospital Solna, Stockholm, Sweden.
Background: Metabolic reprogramming is an important hallmark of cervical cancer (CC), and extensive studies have provided important information for translational and clinical oncology. Here we sought to determine metabolic association with molecular aberrations, telomere maintenance and outcomes in CC.
Methods: RNA sequencing data from TCGA cohort of CC was analyzed for their metabolic gene expression profile and consensus clustering was then performed to classify tumors into different groups/subtypes.