Liaw, P. C. | Search bioRxiv

bioRxiv2026

Early sex-specific organ transcriptional divergence without physiological differences in a murine model of fecal-induced peritonitis

Sepsis is defined as a dysregulated response to infection, leading to life-threatening organ dysfunction that particularly affects parenchymal organs. Clinical studies remain inconclusive regarding the impact of biological sex on sepsis, and preclinical studies are predominantly performed in male animals. We examined early (8 h) septic responses in male and female mice using a fecal-induced peritonitis (FIP) model. Blood biochemical parameters, body temperature, and murine sepsis scores provided evidence of a septic response in animals randomized to FIP compared to controls, but showed no physiological differences between male and female mice. Transcriptomic analysis of the liver, kidney, and lung showed consistent inflammatory activation in response to sepsis as compared to controls. Notably, in the kidney and lung, female mice exhibited stronger immune activation and a heightened inflammatory response compared to males. Thus, biological sex differences in the septic response can be detected in early acute sepsis without apparent physiological differences.

pathology↗

bioRxiv2025

Artificial Intelligence-enabled Histological Analysis in Preclinical Respiratory Disease Models: A Scoping Review

Histological analysis is a cornerstone of preclinical respiratory disease research, enabling assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale due to low throughput and inter-observer variability. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows, but its use and evaluation in preclinical respiratory models have not been synthesized. We conducted a scoping review following Joanna Briggs Institute guidelines, searching MEDLINE and Embase (inception-January 2025) for preclinical studies using AI to analyze histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate. Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Hematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Preprocessing methods (e.g., stain normalization) were common, but annotation and training practices were inconsistently reported. Performance was generally high (accuracy [≥]90%; 7 studies) though validation metrics varied, and external validation was absent. Most studies used "black box" models, with minimal application of explainability techniques. Reproducibility measures, such as sharing datasets or code were rarely reported. AI tools are poised to transform histological analysis in preclinical respiratory research. By addressing gaps in validation, transparency, and standardization, the field can harness these technologies to deliver robust, efficient, and scalable workflows. Registration: Open Science Framework https://doi.org/10.17605/OSF.IO/NM94E

cell biology↗