Biology subjects

Asin, J.

Publications and source records attributed to Asin, J..

3 recordsLinked to original sources

bioRxiv2025

Regression-guided computational design of auxetic scaffolds for soft tissue applications

The mechanical performance of tissue-engineered scaffolds plays a critical role in their effectiveness for regenerative medicine applications. Auxetic metamaterials, characterized by a negative Poissons ratio, offer enhanced conformability and tunable mechanical behavior, making them promising candidates for scaffold design. This study presents a computational framework combining finite element method (FEM) simulations with regression-based predictive modeling to optimize auxetic scaffold architectures. A design of experiments (DOE) strategy enabled the training of FEM-accurate regression models capable of predicting mechanical responses from scaffold microstructural parameters. Sensitivity analysis guided the development of robust optimization strategies for identifying optimal geometries. Validation of the predictive framework was performed using experimentally derived, skin-representative mechanical properties from published literature, demonstrating strong agreement with FEM results. To facilitate broader use, we developed a software tool integrating this pipeline. It includes a manual mode for direct input of design and geometry parameters, and a predictive mode that returns optimized scaffold designs based on target properties. This integrated methodology supports robust, customizable scaffold design, advancing patient-specific approaches in soft tissue engineering.

bioengineering↗

bioRxiv2025

CONSERVED NUCLEAR MORPHOLOGY IDENTIFIES FUNCTIONAL RADIAL GLIA NEURAL PROGENITORS

Mechanical cues influence neural development, yet how tissue architecture is integrated into progenitor cell states remains poorly understood. Here, we show that defined microtopographies induce an early nuclear remodeling program associated with radial glia (RG)-like competence. Aligned microgrooves promote nuclear elongation, reduced Lamin A/C to B1 ratio, sustained {beta}-catenin activity, and distinct patterns of nuclear calcium dynamics, all of which merge before peak Pax6 expression. Pharmacological inhibition of mechanosensitive calcium signaling abolishes RG-associated marker induction while preserving nuclear remodeling, indicating that calcium-dependent pathways are required for Pax6 induction but are dispensable for the establishment of the underlying morphometric state. To quantitatively describe these transitions, we developed an interpretable model based on nuclear geometry and local cell density that identifies morphometric states associated with RG-like competence across experimental conditions. Application of this framework to embryonic mouse and human cortex revealed analogous signatures in native RG populations. Together, these findings indicate that tissue architecture influences RG-like competence through conserved nuclear morphometric states across developmental contexts.

developmental biology↗

bioRxiv2024

Deep Learning-based Modeling for Preclinical Drug Safety Assessment

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a timeintensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity. Here, we introduce TRACE, a model designed for toxicologic liver histopathology assessment capable of tackling a range of diagnostic tasks across multiple scales, including situations where labeled data is limited. TRACE was trained on 15 million histopathology images extracted from 46,734 digitized tissue sections from 157 preclinical studies conducted on Rattus norvegicus. We show that TRACE can perform various downstream toxicology tasks spanning histopathological response assessment, lesion severity scoring, morphological retrieval, and automatic dose-response characterization. In an independent reader study, TRACE was evaluated alongside ten board-certified veterinary pathologists and achieved higher concordance with the consensus opinion than the average of the pathologists. Our study represents a substantial leap over existing computational models in toxicology by offering the first framework for accelerating and automating toxicological pathology assessment, promoting significant progress with faster, more consistent, and reliable diagnostic processes. Live Demo: https://mahmoodlab.github.io/tox-foundation-ui/

bioinformatics↗