Mustafin, M. | bioRxiv Science

bioRxiv2026

The overlooked role of muscle regeneration failure in post-implantation complications: a thorough investigation into mechanisms of recurrent urethral stricture

Bioresorbable collagen membranes rarely achieve complete organ regeneration, often necessitating secondary operations. In this study, urethral defects were modeled in 60 Chinchilla rabbits; 30 were reconstructed using collagen membrane patches. Histological, immunohistochemical and in situ PCR analyses were performed at multiple time points up to 270 days post-implantation to assess inflammatory (TGF-{beta}1, Wnt2, iNOS) and regenerative (collagen I/III, -SMA, E-cadherin) markers. A biopsy from a patient with recurrent urethral stricture was analyzed using the same methodology. At three months after implantation, the mucosal layer had recovered, however, the underlying muscle layer remained incompletely regenerated. The muscle bundles were surrounded by -SMA-positive myofibroblast-rich connective tissue with upregulated profibrotic markers. Comparable patterns of impaired muscle regeneration and high TGF-{beta}1 expression were found in the human specimen. Our findings suggest that while muscle layer regeneration is essential for structural restoration, it may also trigger a sustained profibrotic cascade.

bioengineering↗

bioRxiv2025

Deep Learning-enabled Sperm Morphology Analysis of Bovine Sperm for label-free Imaging Flow Cytometry

Data analysis of sperm morphology is critical for evaluating bull fertility, yet it is often performed using light microscopy and staining techniques in a highly subjective and manual manner. In this study, we introduce a scalable, high-resolution approach combining label-free Imaging Flow Cytometry (IFC) with deep learning for automated classification of bovine sperm morphology. We analyzed 436,374 single-cell images obtained from three prominent bull breeds in Kazakhstan - Kazakh Whitehead, Auliekol, and Simmental from fresh and cryopreserved sperm - providing a uniquely large and diverse dataset. The dataset was used for training and evaluation of deep learning models, among which the convolutional neural network (CNN) MobileNetV4 yielded superior results, achieving 92.3% accuracy and a 0.91 F1-score after training with a Layer-wise Pretraining and Fine-Tuning (LP-FT) strategy. The model classified spermatozoa into eight distinct morphological categories. The CNN-based pipeline ensured consistent, observer-independent classification across all samples. Testing across different conditions and breeds resulted in a 5-10% drop in generalization performance, highlighting the impact of domain-specific biases and underscoring the need for larger, standardized datasets. The proportion of morphologically abnormal spermatozoa varied between seasons and after cryopreservation. This study highlights the advantages of integrating IFC and artificial intelligence (AI) algorithms for robust, high-throughput, and objective label- free spermatozoa morphology assessment in fresh and cryopreserved sperm, offering a promising tool for improving fertility diagnostics and breeding strategies in veterinary practice.

systems biology↗