Deep Learning for Biomarker Discovery in Cancer Genomes.

Background: Genomic data is essential for clinical decision-making in precision oncology. Bioinformatic algorithms are widely used to analyze next-generation sequencing (NGS) data, but they face two major challenges. First, these pipelines are highly complex, involving multiple steps and the integration of various tools.

Determination diabetes mellitus disease markers in tear fluid by photothermal AFM-IR analysis.

The tear fluids from three healthy individuals and three patients with diabetes mellitus were examined using atomic force microscopy-infrared spectroscopy (AFM-IR) and Fourier transform infrared spectroscopy (FTIR). The dried tear samples showed different surface morphologies: the control sample had a dense network of heart-shaped dendrites, while the diabetic sample had fern-shaped dendrites. By using the AFM-IR technique we identified spatial distribution of constituents, indicating how diabetes affects the structural characteristics of dried tears.

Probing Mineral-Organic Interfaces in Soils and Sediments Using Optical Photothermal Infrared Microscopy.

Interactions among microbes, minerals, and organic matter are key controls on carbon, nutrient, and contaminant dynamics in soils and sediments. However, probing these interactions at relevant scales and through time remains an analytical challenge due to both their complex nature and the need for tools permitting nondestructive and real-time analysis at sufficient spatial resolution. Here, we demonstrate the ability and provide analytical recommendations for the submicron-scale characterization of complex mineral-organic microstructures using optical photothermal infrared (O-PTIR) microscopy.

Validation of the World Health Organization Rehabilitation Competency Framework: An illustration using physiotherapy.

Objective: The World Health Organization's Rehabilitation 2030 initiative represents a new strategic direction for the worldwide rehabilitation community and their Rehabilitation Competency Framework (RCF) was designed to describe the requirements of a rehabilitation workforce. This study aimed to identify and review global physiotherapy competencies and explore their congruence with the WHO-RCF.

Design: A document review and thematic analysis were conducted on competency documents sourced from World Physiotherapy member countries.

Counterfactual Diffusion Models for Mechanistic Explainability of Artificial Intelligence Models in Pathology.

Background: Deep learning can extract predictive and prognostic biomarkers from histopathology whole slide images, but its interpretability remains elusive.

Methods: We develop and validate MoPaDi (Morphing histoPathology Diffusion), which generates counterfactual mechanistic explanations. MoPaDi uses diffusion autoencoders to manipulate pathology image patches and flip their biomarker status by changing the morphology.