In this paper, I will discuss recent studies using a cystic fibrosis pig model to better understand the origins of cystic fibrosis lung disease. Specifically, I will review our work investigating how loss of the cystic fibrosis transmembrane conductance regulator function (CFTR) impairs mucociliary transport in the cystic fibrosis airway. These studies reveal new insights into the early, underlying mechanisms of cystic fibrosis lung disease and could lead to novel therapeutic interventions.
Early in the pandemic, clinicians recognized an overlap between Long COVID symptoms and dysautonomia, suggesting autonomic nervous system (ANS) dysfunction. Our clinical experience at Johns Hopkins with primary dysautonomia suggested heritability of sympathetic dysfunction, manifesting primarily as hyperhidrosis and as other dysautonomia symptoms. Whole exome sequencing revealed mutations in genes regulating electrical signaling in the nervous system, thus providing a genetic basis for the sympathetic overdrive observed. We hypothesize that dysautonomia in Long COVID requires two molecular hits: a genetic vulnerability to prime the ANS and a SARS-CoV-2 infection, as an immune trigger, to further disrupt ANS function resulting in increased sympathetic activity. Indeed, Long COVID patients show signs of chronic inflammation and autoimmunity. We have translated this two-hit concept to the clinic using ion channel inhibitors to target genetic susceptibility and immunomodulators to treat inflammation. This multi-hit hypothesis shows promise for managing Long COVID and merits further study.
Artificial intelligence (AI) in the form of ChatGPT has rapidly attracted attention from physicians and medical educators. While it holds great promise for more routine medical tasks, may broaden one's differential diagnosis, and may be able to assist in the evaluation of images, such as radiographs and electrocardiograms, the technology is largely based on advanced algorithms akin to pattern recognition. One of the key questions raised in concert with these advances is: What does the growth of artificial intelligence mean for medical education, particularly the development of critical thinking and clinical reasoning? In this commentary, we will explore the elements of cognitive theory that underlie the ways in which physicians are taught to reason through a diagnostic case and compare hypothetico-deductive reasoning, often employing illness scripts, with inductive reasoning, which is based on a deeper understanding of mechanisms of health and disease. Issues of cognitive bias and their impact on diagnostic error will be examined. The constructs of routine and adaptive expertise will also be delineated. The application of artificial intelligence to diagnostic problem solving, along with concerns about racial and gender bias, will be delineated. Using several case examples, we will demonstrate the limitations of this technology and its potential pitfalls and outline the direction medical education may need to take in the years to come.
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