Case Series: Extracutaneous Findings of Eosinophilic Fasciitis Patients.

Objective: Eosinophilic fasciitis (EF) is a connective tissue disorder characterized by cutaneous changes similar to scleroderma, usually associated with peripheral eosinophilia. This case series highlights clinical findings, particularly extracutaneous involvement, in EF patients to enhance clinician awareness of this rare condition.

Methods: EF patients' skin and visceral organ involvement, musculoskeletal findings, laboratory tests (including acute phase reactants, autoantibodies, protein electrophoresis, etc.

Machine Learning for Individualized Risk Estimation in Anterior Lumbar Interbody Fusion.

Background And Objectives: Although the anterior approach to the spine for anterior lumbar interbody fusion (ALIF) has been shown to be an effective procedure, there are different surgical risks compared with conventional posterior fusion. ALIF patients could potentially receive more personalized care plans that minimize the risk of negative outcomes by forecasting short-term postoperative results before the surgical procedure. The objective of this research was to evaluate the performance of machine learning (ML) algorithms in predicting short-term unfavorable postoperative outcomes after ALIF and to develop an easy-to-use and readily available instrument for this purpose.

Machine Learning-Driven Prognostication in Traumatic Subdural Hematoma: Development of a Predictive Web Application.

Background And Objectives: Our focus was on creating an array of machine learning (ML) models to predict unfavorable in-hospital outcomes after acute traumatic subdural hematoma (atSDH). Our subsequent aim was to deploy these models in an accessible web application, showcasing their practical value.

Methods: Data from the American College of Surgeons Trauma Quality Program database were used to identify patients with atSDH.

Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support.

Large Language Models (LLMs) are transforming patient education in medication management by providing accessible information to support healthcare decision-making. Building on our recent scoping review of LLMs in patient education, this perspective examines their specific role in medication guidance. These artificial intelligence (AI)-driven tools can generate comprehensive responses about drug interactions, side effects, and emergency care protocols, potentially enhancing patient autonomy in medication decisions.

Resolvin D2 restores monocyte anisocytosis and mediates a shift toward classical monocytes ex vivo in blood samples from patients after major burns.

Circulating monocytes contribute to the defense against pathogens and play a crucial role in maintaining immune homeostasis. While there is substantial evidence regarding the triggers of monocyte activation, our understanding of how monocyte function is restored toward homeostasis after activation remains limited. Here, we assessed the changes in monocyte anisocytosis upon activation in blood, measured by monocyte distribution width (MDW), a biomarker for sepsis.