Microscopic computed tomography with AI-CNN-powered image analysis: the path to phenotype bleomycin-induced lung injury.

Bleomycin (BLM)-induced lung injury in mice is a valuable model for investigating the molecular mechanisms that drive inflammation and fibrosis and for evaluating potential therapeutic approaches to treat the disease. Given high variability in the BLM model, it is critical to accurately phenotype the animals in the course of an experiment. In the present study, we aimed to demonstrate the utility of microscopic computed tomography (µCT) imaging combined with an artificial intelligence (AI)-convolutional neural network (CNN)-powered lung segmentation for rapid phenotyping of BLM mice. µCT was performed in freely breathing C57BL/6J mice under isoflurane anesthesia on days 7 and 21 after BLM administration. Terminal invasive lung function measurement and histological assessment of the left lung collagen content were conducted as well. µCT image analysis demonstrated gradual and time-dependent development of lung injury as evident by alterations in the lung density, air-to-tissue volume ratio, and lung aeration in mice treated with BLM. The right and left lung were unequally affected. µCT-derived parameters such as lung density, air-to-tissue volume ratio, and nonaerated lung volume correlated well with the invasive lung function measurement and left lung collagen content. Our study demonstrates the utility of AI-CNN-powered µCT image analysis for rapid and accurate phenotyping of BLM mice in the course of disease development and progression.NEW & NOTEWORTHY Microscopic computed tomography (µCT) imaging combined with an artificial intelligence (AI)-convolutional neural network (CNN)-powered lung segmentation is a rapid and powerful tool for noninvasive phenotyping of bleomycin mice over the course of the disease. This, in turn, allows earlier and more reliable identification of therapeutic effects of new drug candidates, ultimately leading to the reduction of unnecessary procedures in animals in pharmacological research.

Potential Impact of Diabetes and Obesity on Alveolar Type 2 (AT2)-Lipofibroblast (LIF) Interactions After COVID-19 Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new emerging respiratory virus, caused evolving pneumonia outbreak around the world. In SARS-Cov-2 infected patients, diabetes mellitus (DM) and obesity are two metabolic diseases associated with higher severity of SARS-CoV-2 related complications, characterized by acute lung injury requiring assisted ventilation as well as fibrosis development in surviving patients. Different factors are potentially responsible for this exacerbated response to SARS-CoV-2 infection. In patients with DM, base-line increase in inflammation and oxidative stress represent preexisting risk factors for virus-induced damages. Such factors are also likely to be found in obese patients. In addition, it has been proposed that massive injury to the alveolar epithelial type 2 (AT2) cells, which express the SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2), leads to the activation of their stromal niches represented by the Lipofibroblasts (LIF). LIF are instrumental in maintaining the self-renewal of AT2 stem cells. LIF have been proposed to transdifferentiate into Myofibroblast (MYF) following injury to AT2 cells, thereby contributing to fibrosis. We hypothesized that LIF's activity could be impacted by DM or obesity in an age- and gender-dependent manner, rendering them more prone to transition toward the profibrotic MYF status in the context of severe COVID-19 pneumonia. Understanding the cumulative effects of DM and/or obesity in the context of SARS-CoV-2 infection at the cellular level will be crucial for efficient therapeutic solutions.