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Whether high-frequency phase-locked oscillations facilitate integration ('binding') of information across widespread cortical areas is controversial. Here we show with intracranial electroencephalography that cortico-cortical co-ripples (~100-ms-long ~90 Hz oscillations) increase during reading and semantic decisions, at the times and co-locations when and where binding should occur. Fusiform wordform areas co-ripple with virtually all language areas, maximally from 200 to 400 ms post-word-onset. Semantically specified target words evoke strong co-rippling between wordform, semantic, executive and response areas from 400 to 800 ms, with increased co-rippling between semantic, executive and response areas prior to correct responses. Co-ripples were phase-locked at zero lag over long distances (>12 cm), especially when many areas were co-rippling. General co-activation, indexed by non-oscillatory high gamma, was mainly confined to early latencies in fusiform and earlier visual areas, preceding co-ripples. These findings suggest that widespread synchronous co-ripples may assist the integration of multiple cortical areas for sustained periods during cognition.
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Medical trainee well-being is often met with generalized solutions that overlook substantial individual variations in mental health predisposition and stress reactivity. Precision medicine leverages individual environmental, genetic, and lifestyle factors to tailor preventive and therapeutic interventions. In addition, an exclusive focus on clinical mental illness tends to disregard the importance of supporting the positive aspects of medical trainee well-being. We introduce a novel precision well-being framework for medical education that is built on a comprehensive and individualized view of mental health, combining measures from mental health and positive psychology in a unified, data-driven framework. Unsupervised machine learning techniques commonly used in precision medicine were applied to uncover patterns within multidimensional mental health data of medical students. Using data from 3,632 US medical students, clusters were formulated based on recognized metrics for depression, anxiety, and flourishing. The analysis identified three distinct clusters. Membership in the 'Healthy Flourishers' well-being phenotype was associated with no signs of anxiety or depression while simultaneously reporting high levels of flourishing. Students in the 'Getting By' cluster reported mild anxiety and depression and diminished flourishing. Membership in the 'At-Risk' cluster was associated with high anxiety and depression, languishing, and increased suicidality. Nearly half (49%) of the medical students surveyed were classified as 'Healthy Flourishers', whereas 36% were grouped into the 'Getting-By' cluster and 15% were identified as 'At-Risk'. Findings show that a substantial portion of medical students report diminished well-being during their studies, with a significant number struggling with mental health challenges. This novel precision well-being framework represents an integrated empirical model that classifies individual medical students into distinct and meaningful well-being phenotypes based on their holistic mental health. This approach has direct applicability to student support and can be used to evaluate the effectiveness of personalized intervention strategies stratified by cluster membership.
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The Spring 2023 Webinar Audio Seminar (WAS) of the International Association of Medical Science Educators (IAMSE), titled "Widening the Road to Health Professions Education: Expanding Access for Diverse and Underserved Populations," was designed to help health science educators explore innovative practices in recruiting and enrolling students from underserved populations into health sciences programs. From March 2, 2023, to March 30, 2023, this five-part webinar series was broadcast live to institutions and educators worldwide. This series helped participants learn about creating pathways for students to meet the unique needs of their communities.
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Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.
Assuntos
Inteligência Artificial , Estudantes de Medicina , Humanos , Projetos Piloto , Currículo , Atenção à SaúdeRESUMO
Epilepsy imposes a substantial burden on the Democratic Republic of Congo (DRC). These challenges encompass the lack of comprehensive disease surveillance, an unresolved understanding of its pathophysiology, economic barriers limiting access to essential care, the absence of epilepsy surgical capabilities, and deeply ingrained societal stigmas. Notably, the national prevalence of epilepsy remains undetermined, with research primarily concentrating on infectious factors like Onchocerca volvulus, leaving other potential causes underexplored. Most patients lack insurance, incurring out-of-pocket expenses that often lead them to opt for traditional medicine rather than clinical care. Social stigma, perpetuated by common misconceptions, intensifies the social isolation experienced by individuals living with epilepsy. Additionally, surgical interventions are unavailable, and the accessibility of anti-seizure medications and healthcare infrastructure remains inadequate. Effectively tackling these interrelated challenges requires a multifaceted approach, including conducting research into region-specific factors contributing to epilepsy, increasing healthcare funding, subsidizing the costs of treatment, deploying mobile tools for extensive screening, launching awareness campaigns to dispel myths and reduce stigma, and promoting collaborations between traditional healers and medical practitioners to enhance local understanding and epilepsy management. Despite the difficulties, significant progress can be achieved through sustained and compassionate efforts to understand and eliminate the barriers faced by epilepsy patients in the region. This review outlines essential steps for alleviating the epilepsy burden in the DRC. PLAIN LANGUAGE SUMMARY: There are not enough resources to treat epilepsy in the DRC. PWEs struggle with stigma and the lack of money. Many of them still use traditional medicine for treatment and hold wrong beliefs about epilepsy. That is why there is a need for more resources to make the lives of PWEs better in the DRC.