The combination of physiology and machine learning for prediction of CPAP pressure and residual AHI in OSA.

Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea (OSA); however some people have residual respiratory events or require significantly higher CPAP pressure while on therapy. Our objective was to develop predictive models for CPAP outcomes and assess whether the inclusion of physiological traits enhances prediction. We constructed predictive models from baseline information for subsequent residual apnea-hypopnea index (AHI) and optimal CPAP pressure.

Comprehensive characterization of MCL-1 in patients with colorectal cancer: Expression, molecular profiles, and outcomes.

Myeloid cell leukemia 1 (MCL-1) is a member of the B-cell lymphoma 2 protein family and has anti-apoptotic functions. Deregulation of MCL-1 has been reported in several cancers, including lung and breast cancer. In the present study, the association of MCL-1 expression with molecular features in colorectal cancer (CRC) has been highlighted.

Evaluation and Comparison of the Comprehensiveness of Canadian and American Ophthalmology Residency Program Websites.

Purpose: We descriptively and comparatively evaluated the comprehensiveness of Canadian and US-accredited ophthalmology residency program websites as of August 28, 2024.

Methods: Using Canadian Resident Matching Service ( = 15) and US Fellowship and Residency Electronic Interactive Database ( = 125), we assessed website content across seven criteria: recruitment, faculty, residents, education/research, teaching, benefits, and community. Two independent reviewers used a 40-point system, with Kruskal-Wallis and post-hoc pairwise tests for analysis by country and funding model.

Concordance-based Predictive Uncertainty (CPU)-Index: Proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT.

In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and personalized AI time-to-event models. Through its application in refining lung cancer screening (LCS) predictions generated by an individualized AI time-to-event model trained with fused data of low dose CT (LDCT) radiomics with patient demographics, we demonstrate its effectiveness, resulting in improved risk assessment compared to the Lung CT Screening Reporting & Data System (Lung-RADS). Subgroup-based Lung-RADS faces challenges in representing individual variations and relies on a limited set of predefined characteristics, resulting in variable predictions.

Urinary proteomics identifies distinct immunological profiles of sepsis associated AKI sub-phenotypes.

Background: Patients with sepsis-induced AKI can be classified into two distinct sub-phenotypes (AKI-SP1, AKI-SP2) that differ in clinical outcomes and response to treatment. The biologic mechanisms underlying these sub-phenotypes remains unknown. Our objective was to understand the underlying biology that differentiates AKI sub-phenotypes and associations with kidney outcomes.