3 research outputs found

    Remembering Our Past: Teaching the History of Anatomy at Indiana University

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    Most students pursuing careers in anatomy or related disciplines have a limited understanding of how, over the centuries, the intricate structure of the human body came to be known. To provide students with the relevant historical perspective, we developed a team-taught survey course in the history of anatomical sciences—including gross anatomy, histology, neuroanatomy, and embryology—from antiquity to the present. Taught entirely via Zoom during the Spring semester of 2021, History of Anatomy (2 semester hours credit) met once per week for approximately 2 hours. Enrollment consisted of 5 undergraduate students majoring in Biology (2), Human Biology (2), or Anthropology (1), as well as 3 graduate students pursuing either a master’s degree in Clinical Anatomy (1) or a Ph.D. in Anatomy Education (2). Three of the students had no prior coursework in anatomy. Through assigned readings, lectures, and discussions, the class explored the work of the great anatomists and their discoveries. A particular emphasis was placed on the evolution of anatomy as a discipline and the cultural influences, scientific controversies, and ethical dilemmas facing its practitioners. Syllabus topics included critical appraisals of the role of gender, race, and ethnicity in anatomical discovery. A key feature of the course was the opportunity for students to engage in robust discussions about such controversial issues as: Eurocentric biases in our understanding of human anatomy and the untold story of Muslim contributions to anatomical knowledge well before Vesalius; Competing claims of priority for who “discovered” the pulmonary circulation; The underappreciated role of women in the history of anatomy and medicine; The ethical quandary of teaching anatomy from archival fetal specimens obtained before the era of informed consent; Accusations that famed anatomist William Hunter used the bodies of murdered pregnant women to create his anatomical atlas of the gravid uterus; Complicity of Eduard Pernkopf and other Nazi-era anatomists in the unethical use of executed victims to obtain images for a renowned anatomical atlas. All students were assessed through weekly homework (written responses to study questions), a mid-term writing assignment, and a term paper about an historical topic of the student’s choosing. Graduate students had the additional requirement of a class presentation about their term paper topic. The end-of-course evaluation suggested that the course was well-received by the students (mean Likert score = 4.63 on a 5-point scale; n = 6). Based on this positive reception, we plan to offer History of Anatomy again on a recurring basis. We believe that by knowing our history, both the good and the bad, future practitioners of anatomy and related disciplines will be less likely to perpetuate the biases and ethical transgressions of earlier eras.American Association for Anatomy Spring Meetin

    Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer’s Disease

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    The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, Friedreich’s ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies
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