Design and optimization of tamarind seed polysaccharide-based scaffold for tissue engineering applications using statistical modeling and machine learning, and it's in-vitro validation.
Journal:
International journal of biological macromolecules
PMID:
39889978
Abstract
This study explores the development and optimization of a novel biomaterial scaffold for tissue engineering, composed of Tamarind seed polysaccharide (TSP), Hydroxypropyl methylcellulose (HPMC), Chitosan (CS), and Sodium alginate (ALG). Scaffold properties, including swelling, degradation, porosity, mechanical strength, conductivity, and surface wettability, were analyzed. Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling, the optimal scaffold (THAC) composition was determined to be 30.12 % TSP, 21.69 % HPMC, 12.05 % ALG, and 36.14 % CS. The ANN model outperformed RSM in predictive accuracy (R > 0.9). Mechanical testing demonstrated a Young's modulus of 0.905 ± 0.103 MPa and a compression strength of 0.398 ± 0.028 MPa, indicative of its resilience for cartilage engineering. Conductivity analysis revealed enhanced ionic conductance in wet conditions, highlighting its electrolyte-responsive behavior. Surface wettability tests showed a hydrophilic nature, with a contact angle of 58.08 ± 6.84°, enabling superior water absorption. SEM analysis confirmed a uniform porous structure, while cytocompatibility studies with HeLa, MG -63 and ES-E14TG2a cells showed significant cell proliferation and attachment. These results underscore THAC's potential for applications in regenerative medicine, with the combination of RSM and ANN enabling precise tuning of scaffold properties for optimal performance in tissue engineering.