An animal model of severe acute respiratory distress syndrome for translational research.

Background: Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine. Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of transplanted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility.

A deep learning-based algorithm for pulmonary tuberculosis detection in chest radiography.

In tuberculosis (TB), chest radiography (CXR) patterns are highly variable, mimicking pneumonia and many other diseases. This study aims to evaluate the efficacy of Google teachable machine, a deep neural network-based image classification tool, to develop algorithm for predicting TB probability of CXRs. The training dataset included 348 TB CXRs and 3806 normal CXRs for training TB detection.