Transactions of the American Clinical and Climatological Association最新文献

Gene-environmental interactions create risk profiles for sporadic cancer development in patients with colorectal cancer (CRC). For instance, a person's socioeconomic status over their lifetime can affect their level of physical activity and type of diet, and their exposure to tobacco and alcohol may affect their gut microbiome and ultimate risk for developing CRC. Metabolic disease can independently or further change the gut microbiome and alter the typical timing of CRC development, such as is observed and linked with early-onset disease. Patients with microsatellite unstable tumors where DNA mismatch repair is defective have altered immune environments as a result of tumor hypermutability and neoantigen generation, allowing for immune checkpoint inhibitor susceptibility; in such cases, the genetics of the tumor changed the environment. The environment can also change the genetics, where interleukin-6-generated inflammation can inactivate MSH3 protein function that is associated with CRCs which are more metastatic, and patients show poor outcomes. Some specific aspects of the local microbial environment that may be influenced by diet and metabolism are associated with CRC risk, such as Fusobacterium nucleatum infection, and may affect the initiation, perpetuation, and spread of CRC. Overall, both the macro- and microenvironments associated with a person play a major role in CRC formation, progression, and metastases.

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.

Despite decreases in overall stroke incidence and mortality in the United States, racial and ethnic disparities continue unabated. Of note, the long-standing disproportionate burden of stroke on African Americans compared to other racial and ethnic groups persists, and national projections indicate this toll will likely worsen over the next decade. Why have we not been able to bend the stroke disparities curve for African Americans? Well, this is mainly because traditional stroke risk factors, such as hypertension, diabetes, etc., account for just half of the Black vs. non-Hispanic White stroke disparity. As such, there is increasing interest in evaluating understudied factors like upstream social determinants of health, including geography, psychosocial stress, and environmental pollution; identifying potential mediators; and testing multilevel interventions to address them. This paper highlights emerging avenues that may help decode the excess stroke risk in African Americans, focusing on zip codes, color codes, and epigenetic codes.

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.