Search bioRxiv⌕ Search

Biology subjects

Abu Saleh, M.

Publications and source records attributed to Abu Saleh, M..

2 recordsLinked to original sources

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↗

FibroTrack: A Standalone Deep Learning Platform for Automated Fibrosis Quantification in Muscle and Cardiac Histology

Accurate fibrosis quantification is essential for understanding muscle and cardiac disease, yet current manual and semi-automated methods remain slow, subjective, and poorly reproducible. We introduce FibroTrack, a standalone deep learning platform with a graphical user interface (GUI) that fully automates fibrosis analysis across Sirius Red (SR), Massons Trichrome (MT), and immunohistochemistry (IHC) stainings. FibroTrack uniquely integrates LAB (lightness, green-red, blue-yellow) color space normalization with a You Only Look Once version 11 (YOLOv11) segmentation model trained on 2,034 histological images. This approach achieved >97% precision and demonstrated excellent concordance with blinded pathologists (Spearman correlation, r = 0.87-0.96). Automated outputs include segmented images and structured spreadsheets, ensuring high reproducibility and scalability. By combining advanced color analysis with state-of-the-art segmentation in an accessible tool, FibroTrack provides a novel, accurate, and clinically relevant solution for high-throughput fibrosis quantification in both preclinical research and pathology practice.

pathology↗