Shimron, R. B. | bioRxiv Science

bioRxiv2026

An AI-assisted platform for quantitative histopathological analysis in interstitial lung disease

Interstitial lung diseases (ILDs) are heterogeneous pulmonary disorders characterized by chronic inflammation and/or fibrosis. 30-40% of ILD patients develop fibrotic disease that is associated with progressive respiratory decline and poor prognosis, particularly in idiopathic pulmonary fibrosis. Current antifibrotic therapies slow disease progression but do not reverse fibrosis, highlighting the need for improved therapeutic strategies. Robust histopathological evaluation in preclinical models is essential for drug development; however, conventional scoring systems are semi-quantitative, labor-intensive, subject to inter-observer variability, and rely on limited field sampling. Here, we introduce FibroSight, a standalone platform for compartment-resolved quantification of lung remodeling in Sirius Red-stained sections. By integrating deep learning- based structural segmentation with color-based feature extraction, FibroSight enables highly automated whole-lobe analysis without requiring complex computational setup. The platform quantifies complementary remodeling parameters, including parenchymal collagen fraction, parenchymal tissue density, nuclear area fraction, parenchymal airspace fraction, and airway- and vascular-associated remodeling. Validated in the bleomycin-induced fibrosis model, FibroSight-derived metrics strongly correlated with expert Ashcroft scoring and showed stronger associations with histological severity than corresponding outputs from a semi-automated ImageJ-based workflow. The platform further distinguished inflammatory from fibrotic remodeling in influenza-induced lung injury and demonstrated translational proof-of-concept applicability in human ILD biopsy specimens. By enabling scalable, reproducible, and multi-compartment histological quantification, FibroSight provides a practical framework for objective assessment of lung remodeling. This approach expands conventional fibrosis evaluation by integrating fibrotic, inflammatory, airway, and vascular-associated readouts, supporting more precise analysis of disease mechanisms and therapeutic responses in preclinical and translational ILD research.

pathology↗

bioRxiv2026

Hypertension-mediated cardiac fibrosis is a mechanical process initiated by smooth muscle cells

Hypertension represents the most prevalent chronic cardiovascular condition, typically culminating in pathological cardiac remodeling characterized by hypertrophy and extensive fibrosis. Although the cellular phenotypes associated with these changes are well-documented, the precise mechanisms by which hypertensive stress is sensed and transduced into a fibrotic program remain poorly defined. To elucidate these mechanisms, we investigated the role of Lysyl oxidase (LOX), an extracellular matrix (ECM)-modifying enzyme that is upregulated during hypertensive stress and associated with cardiovascular diseases. By employing cell-type specific Cre-Lox technology to conditionally delete Lysyl oxidase in either smooth muscle cells (SMCs) or fibroblasts, the primary ECM-secreting cell populations, we demonstrate that fibroblast-specific Lox deletion had no significant impact on the progression of cardiac fibrosis. Conversely, SMC-specific Lox deletion selectively inhibited the fibrotic response without affecting other remodeling parameters, such as cardiac hypertrophy. Notably, in the SMC-specific Lox knockout hearts, fibrosis was restricted to the perivascular niche and failed to propagate into the cardiac interstitium. We find that this transition is a mechanical, ECM-dependent process initiated by SMCs. Our results identify SMCs, rather than fibroblasts, as the primary sensors and initiators of the hypertensive fibrotic response. These findings demonstrate that fibrosis can be uncoupled from other hypertensive manifestations and identify SMC-mediated ECM modification as a potential therapeutic target for treating hypertensive heart disease.

cell biology↗