Machine Learning on 50,000 Manuscripts Shows Increased Clinical Research by Academic Cardiac Surgeons.

INTRODUCTION: Academic cardiac surgeons are productive researchers and innovators. We sought to perform a comprehensive machine learning (ML)-based characterization of cardiac surgery research over the past 40 y to identify trends in research pursuits. METHODS: US-based academic websites were queried for surgeon profiles. Publications since 1980 were obtained from Web of Science, and publication classifications (e.g., "human", "animal") were collected through the National Institutes of Health iCite tool. Publications were deemed "basic or translational" if >50% of their classification was under "animal" or "molecular or cell", and "clinical" if otherwise. ML-based clustering was performed on publication titles and Medical Subject Heading terms to identify research topics. RESULTS: A total of 944 cardiac surgeons accounted for 48,031 unique publications. Average citations per year have decreased since 1980 (P < 0.001). The percentage of basic or translational publications by cardiac surgeons has decreased over time (P < 0.001), comprising of only 8% of publications in 2022. Adult cardiac surgeons, those who received an F32, K08, or R01, and those with a PhD were more likely to publish basic or translational research. Top areas of basic or translational research were myocardial reperfusion, aortic aneurysms or remodeling, and transplant immunology. Major areas of clinical research included aortic disease, aortic valve disease, and mechanical circulatory support. Collaboration analysis revealed that 55% of publications were single-center, and the yearly percentage of these publications has decreased over time (P < 0.001). CONCLUSIONS: Cardiac surgeons are performing less basic or translational research relative to clinical research than ever before. The majority of publications over the past 40 y did not involve cross-center collaboration. Continued support for clinical research is needed, while also encouraging collaborative basic or translational science to foster innovation in patient care.

Cutting Edge: Steroid Responsiveness in Foxp3+ Regulatory T Cells Determines Steroid Sensitivity during Allergic Airway Inflammation in Mice.

Glucocorticoids are a highly effective first-line treatment option for many inflammatory diseases, including asthma. Some patients develop a steroid-resistant condition, yet, the cellular and molecular mechanisms underlying steroid resistance remain largely unknown. In this study, we used a murine model of steroid-resistant airway inflammation and report that combining systemic dexamethasone and intranasal IL-27 is able to reverse the inflammation. Foxp3+ regulatory T cells (Tregs) were required during dexamethasone/IL-27 treatment of steroid-resistant allergic inflammation, and importantly, direct stimulation of Tregs via glucocorticoid or IL-27 receptors was essential. Mechanistically, IL-27 stimulation in Tregs enhanced expression of the agonistic glucocorticoid receptor-α isoform. Overexpression of inhibitory glucocorticoid receptor-ß isoform in Tregs alone was sufficient to elicit steroid resistance in a steroid-sensitive allergic inflammation model. Taken together, our results demonstrate for the first time, to our knowledge, that Tregs are instrumental during steroid resistance and that manipulating steroid responsiveness in Tregs may represent a novel strategy to treat steroid refractory asthma.