Comparison of Chart Review and Administrative Data in Developing Predictive Models for Readmissions in Chronic Obstructive Pulmonary Disease.

This study aimed to evaluate the performance of machine learning models for predicting readmission of patients with Chronic Obstructive Pulmonary Disease (COPD) based on administrative data and chart review data. The study analyzed 4,327 patient encounters from the University of Chicago Medicine to assess the risk of readmission within 90 days after an acute exacerbation of COPD. Two random forest prediction models were compared.

Double-stranded RNA orbivirus disrupts the DNA-sensing cGAS-sting axis to prevent type I IFN induction.

Cyclic GMP-AMP synthase (cGAS) is a DNA sensing cellular receptor that induces IFN-I transcription in response to pathogen and host derived cytosolic DNA and can limit the replication of some RNA viruses. Some viruses have nonetheless evolved mechanisms to antagonize cGAS sensing. In this study, we evaluated the interaction between Bluetongue virus (BTV), the prototypical dsRNA virus of the Orbivirus genus and the Sedoreoviridae family, and cGAS.

Recommendations on NMOSD treatment in Latin America: Utilizing consensus-based RAND/UCLA methodology.

Introduction: Neuromyelitis optica spectrum disorder (NMOSD) is a serious condition affecting people worldwide, including Latin America (LATAM). Healthcare disparities and economic limitations make effective treatment access challenging. It is crucial to consider the best practice therapeutic decision-making, including emerging long-term preventive therapies, to ensure patients in LATAM and elsewhere can effectively manage their disease all over the world.

Brain aging rejuvenation factors in adults with genetic and sporadic neurodegenerative disease.

The largest risk factor for dementia is age. Heterochronic blood exchange studies have uncovered age-related blood factors that demonstrate 'pro-aging' or 'pro-youthful' effects on the mouse brain. The clinical relevance and combined effects of these factors for humans is unclear.

Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments.

Scapular morphological attributes show promise as prognostic indicators of retear following rotator cuff repair. Current evaluation techniques using single-slice magnetic-resonance imaging (MRI) are, however, prone to error, while more accurate computed tomography (CT)-based three-dimensional techniques, are limited by cost and radiation exposure. In this study we propose deep learning-based methods that enable automatic scapular morphological analysis from diagnostic MRI despite the anisotropic resolution and reduced field of view, compared to CT.