Search

Deficiency of Interleukin-1 receptor antagonist (DIRA) is a rare autosomal recessive autoinflammatory disease first reported in 2009. To date, 33 patients have been previously characterized and reported in literature. To the best of our knowledge, there have been no recent studies that have broadly evaluated recent advances in understanding of this challenging and life-threatening condition. Herein, we comprehensively reviewed the etiology and cytogenetic abnormalities of DIRA. We then investigated the current diagnostics used for identifying the variants of this disease. Furthermore, we report the demographics and characteristics broadly found in the current patient sample. Lastly, we discuss the treatments (antibiotics, corticosteroids, etc.) and the primary biologics (anakinra, canakinumab, adalimumab, and rilonacept) used in patients, along with current information on their clinical safety and efficacy. Overall, although further investigations are required for this disease, this review may be informative to clinicians treating and managing patients presenting with DIRA.

With the increasing development of artificial intelligence, large language models (LLMs) have been utilized to solve problems in natural language processing tasks. More recently, LLMs have shown unique potential in numerous applications within medicine but have been particularly investigated for their ability in clinical reasoning. Although the diagnostic accuracy of LLMs in forming differential diagnoses has been reviewed in general internal medicine applications, much is unknown in autoinflammatory disorders. From the nature of autoinflammatory diseases, forming a differential diagnosis is challenging due to the overlapping symptoms between disorders and even more difficult without genetic screening. In this work, the diagnostic accuracy of the Generative Pre-Trained Transformer Model-4 (GPT-4), GPT-3.5, and Large Language Model Meta AI (LLaMa) were evaluated in clinical vignettes of Deficiency of Interleukin-1 Receptor Antagonist (DIRA) and Familial Mediterranean Fever (FMF). We then compared these models to a control group including one internal medicine physician. It was found that GPT-4 did not significantly differ in correctly identifying DIRA and FMF patients compared to the internist. However, the physician maintained a significantly higher accuracy than GPT-3.5 and LLaMa 2 for either disease. Overall, we explore and discuss the unique potential of LLMs in diagnostics for autoimmune diseases.

Autoinflammatory bone disease is a new branch of autoinflammatory diseases caused by seemingly unprovoked activation of the innate immune system leading to an osseous inflammatory process. The inflammatory bone lesions in these disorders are characterized by chronic inflammation that is typically culture negative with no demonstrable organism on histopathology. The most common autoinflammatory bone diseases in childhood include chronic nonbacterial osteomyelitis (CNO), synovitis, acne, pustulosis, hyperostosis, osteitis syndrome, Majeed syndrome, deficiency of interleukin-1 receptor antagonist, and cherubism. In this article, the authors focus on CNO and summarize the distinct genetic autoinflammatory bone syndromes., (Copyright © 2013 Elsevier Inc. All rights reserved.)