Rare Case of Gout Leading to Septic Arthritis, Osteomyelitis, and Septic Shock in an Elderly Patient.

Gout is a common, chronic inflammatory arthritis that oftentimes accompanies an initial acute and painful attack characterized by intense pain and swelling. Although it may present in different sites such as the ankles, wrists, knees, elbows, and fingers, the lower extremities are the most common site of involvement. The pathophysiology of gout is complex, but typically, the deposition of monosodium urate crystals within the joint space and the subsequent acute inflammatory response play an important role. Following an acute attack, chronic gout can present with tophi or nests of monosodium urate crystals surrounded by macrophages and multinucleated giant cells that trigger granulomatous inflammation. Progressively, chronic gout can lead to several other complications including joint destruction, gout nephropathy, spinal compression, and secondary infections. In this case report, we present an elderly female patient with chronic gout and multiple tophi formations in all digits of both of her hands. The tophi led to an ulceration and secondary septic arthritis and osteomyelitis of the right second digit. By the time the patient presented and was admitted to the hospital, she was in septic shock. We will review the pathogenesis of gout and other cases of concomitant septic arthritis and gout, as well as medical management and necessary surgical intervention as a means of treatment.

Graph neural networks for the identification of novel inhibitors of a small RNA.

MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing graph-structured data, making them well-suited for the analysis of molecular structures. In this work, we explore the application of GNNs in ligand-based drug screening for small molecules targeting miR-21. By representing a known dataset of small molecules targeting miR-21 as graphs, GNNs can learn complex relationships between their structures and activities, enabling the prediction of potential miRNA-targeting small molecules by capturing the structural features and similarity between known miRNA-targeting compounds. The use of GNNs in miRNA-targeting drug screening holds promise for the discovery of novel therapeutic agents and provides a computational framework for efficient screening of large chemical libraries.