No abstract

SummaryBackgroundAvascular necrosis of the lunate bone (Kienböck’s disease), is a condition in which lunate bone, loses its blood supply, leading to necrosis of the bone.There is probably no single cause of Kienbock’s disease. Its origin may involve multiple factors, such as the blood supply (arteries), blood drainage (veins), and skeletal variations. Trauma, either isolated or repeated, may possibly be a factor in some cases. This case presented with multifactorial etiology.Case ReportIn the presented case, a patient with negative ulnar variant had injured her right wrist and presented at an orthopedic clinic due to nonspecific pain 6 months later. An arthro-MRI examination revealed necrosis of the lunate bone, scapholunate ligament tear and coexisting TFCC (triangular fibrocartilage complex) tear.ConclusionsEarly diagnosis and treatment can prevent progression of necrotic lesions and bone collapse. MRI examination seems to be the key diagnostic method in the early stage of the Kienböck’s disease with negative x-ray and CT images. Arthro-MRI examination also allows us to identify the underlying ligamentous injury. In cases of traumatic etiology, an additional CT test enables stating the final diagnosis.

No abstract

No abstract

No abstract

Drug repurposing represents an attractive alternative to the costly and time-consuming process of new drug development, particularly for serious, widespread conditions with limited effective treatments, such as Alzheimer’s disease (AD). Emerging generative artificial intelligence (GAI) technologies like ChatGPT offer the promise of expediting the review and summary of scientific knowledge. To examine the feasibility of using GAI for identifying drug repurposing candidates, we iteratively tasked ChatGPT with proposing the twenty most promising drugs for repurposing in AD, and tested the top ten for risk of incident AD in exposed and unexposed individuals over age 65 in two large clinical datasets: 1) Vanderbilt University Medical Center and 2) the All of Us Research Program. Among the candidates suggested by ChatGPT, metformin, simvastatin, and losartan were associated with lower AD risk in meta-analysis. These findings suggest GAI technologies can assimilate scientific insights from an extensive Internet-based search space, helping to prioritize drug repurposing candidates and facilitate the treatment of diseases.

No abstract

ObjectiveIdentifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach – Concept-Wide Association Study (ConceptWAS) that systematically scans a disease’s clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic.MethodsUsing the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms.ResultsWe processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including “absent sense of smell” (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21–7.50), “fever” (OR = 1.43, 95% CI = 1.28–1.59), “with cough fever” (OR = 2.29, 95% CI = 1.75–2.96), and “ageusia” (OR = 5.18, 95% CI = 3.02–8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC).ConclusionConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.