Butnaru, D. | 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↗

bioRxiv2026

FENNEC: Fine-Tuned Ensemble Neural Networks Accelerate Chemically Modified siRNA Design and Screening

Small interfering RNAs (siRNAs) are a clinically validated therapeutic modality, yet designing potent chemically modified siRNAs remains a costly and iterative process, limited by scarce public data. Computational prediction of siRNA efficacy is therefore essential for rational design and accelerated preclinical development. However, despite the critical role of chemical modifications in therapeutic performance, current state-of-the-art machine learning methods either are not designed to model the chemical diversity of therapeutic siRNAs, or exhibit poor generalization performance. Here, we present FENNEC (Fine-Tuned Ensemble of Neural Networks for siRNA Efficiency Characterization), a machine-learning framework for predicting siRNA activity across chemically diverse design spaces. To support this effort, we curated the largest patent-derived dataset to date of chemically modified siRNAs from 42 patents using OCR-based table extraction and stringent filtering. FENNEC combines temporal convolutional networks with thermodynamic descriptors, experimental covariates, and embeddings from RNA foundation models to capture both local chemical determinants and broader target-context information. Importantly, we show that language-model-derived embeddings provide meaningful higher-order representations of target transcripts, particularly in data-scarce settings. FENNEC achieved robust predictive performance across both gene-level and scaffold-level validation settings, with additional experimental validation on a novel AHSA1-targeting dataset further supporting its generalizability across chemically modified siRNAs. In benchmarking, FENNEC outperformed classical machine-learning and state-of-the-art deep learning models, demonstrating generalization to unseen chemistry. Model interpretation recovered established design principles, including position-specific effects of glycol nucleic acid, 2'-fluoro modifications, and phosphorothioate backbones. Furthermore, in silico perturbation analyses suggest that FENNEC can serve not only as a predictive model, but also as an oracle for the design and optimization of chemically modified siRNAs. Together, our work addresses a key gap in the field by enabling chemically aware deep learning for siRNA design, supported by a large and diverse collection of chemically modified siRNA measurements.

bioinformatics↗