Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry.

Background: Rheumatoid arthritis (RA) is a common condition treated with biological disease-modifying anti-rheumatic medicines (bDMARDs). However, many patients exhibit resistance, necessitating the use of machine learning models to predict remissions in patients treated with bDMARDs, thereby reducing healthcare costs and minimizing negative effects.

Objective: The study aims to develop machine learning models using data from the Kuwait Registry for Rheumatic Diseases (KRRD) to identify clinical characteristics predictive of remission in RA patients treated with biologics.

Insect detect: An open-source DIY camera trap for automated insect monitoring.

Insect monitoring is essential to design effective conservation strategies, which are indispensable to mitigate worldwide declines and biodiversity loss. For this purpose, traditional monitoring methods are widely established and can provide data with a high taxonomic resolution. However, processing of captured insect samples is often time-consuming and expensive, which limits the number of potential replicates.

Consensus-Based Overarching Principles and Recommendations on the Use of Biosimilars in the Treatment of Inflammatory Arthritis in the Gulf Region.

Background: Though biologic agents have significantly improved the treatment of inflammatory arthritis (rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis), high costs, stringent regulations, strict reimbursement criteria, and existing patents have limited patient access to treatments. While being highly similar in quality, safety, and efficacy to biologic reference products, biosimilars can reduce the financial burden and prevent underutilization of medication.

Objective: The objective of this initiative was to develop an evidence-based consensus of overarching principles and recommendations aimed at standardizing the use of biosimilars in treating inflammatory arthritis in the Gulf region.

Experimental Examination of Conventional, Semi-Automatic, and Automatic Volumetry Tools for Segmentation of Pulmonary Nodules in a Phantom Study.

The aim of this study is to examine the precision of semi-automatic, conventional and automatic volumetry tools for pulmonary nodules in chest CT with phantom N1 LUNGMAN. The phantom is a life-size anatomical chest model with pulmonary nodules representing solid and subsolid metastases. Gross tumor volumes (GTVs) were contoured using various approaches: manually (0); as a means of semi-automated, conventional contouring with (I) adaptive-brush function; (II) flood-fill function; and (III) image-thresholding function.