D.Lalitha Sree Vani Vani

The present study compares three colon-targeted drug delivery systems; Eudragit S100-coated polymeric tablets, PLGA nanoparticles, and alginate hydrogel microspheres, developed for the controlled release of 5-Fluorouracil (5-FU). Each formulation was prepared and optimized using distinct carriers and evaluated under simulated gastrointestinal (GI) conditions to assess their physicochemical characteristics, release behaviour, and stability. The formulations were characterized for particle size, surface charge, encapsulation efficiency, and swelling index. Morphological analysis confirmed smooth coating in polymeric tablets, spherical uniformity in nanoparticles, and a porous structure in hydrogels. In vitro dissolution studies revealed minimal drug release in gastric conditions (≤2% at pH 1.2) and sustained release at colonic pH (7.4). PLGA nanoparticles showed the most controlled release profile, achieving 92.1 ± 2.4% cumulative release at 24 hours, compared with 100.0 ± 3.1% for polymeric tablets and 85.4 ± 2.1% for hydrogels. Kinetic modeling indicated that all systems followed diffusion-dominated release, with nanoparticles best fitting the Higuchi model (R² = 0.981). Stability studies confirmed nanoparticle integrity under prolonged acidic and neutral exposure, while hydrogels exhibited partial deformation. Overall performance analysis identified PLGA nanoparticles as the most efficient system, demonstrating superior acid resistance, encapsulation efficiency, and colon-specific release. These findings suggest that nanoparticle-based carriers offer significant potential for achieving predictable, site-specific, and sustained drug delivery to the colon.

The application of digital twin technology in healthcare has emerged as a significant advancement, offering new possibilities for personalized medicine, surgical planning, and healthcare system optimization. By creating real-time, data-driven virtual models of patients, organs, and hospital environments, digital twins enable precise simulations, predictive analytics, and personalized treatment strategies. This review and meta-analysis evaluate the role of digital twins in improving clinical decision-making, patient outcomes, and operational efficiency while also addressing the technical, ethical, and regulatory challenges that hinder their widespread adoption. The analysis synthesizes data from studies examining digital twins in disease modeling, surgical rehearsals, and hospital resource management, highlighting their ability to enhance patient-specific interventions and optimize healthcare workflows. The results suggest that digital twins significantly improve diagnostic accuracy, treatment efficacy, and hospital efficiency, demonstrating their potential as transformative tools in modern medicine. However, challenges such as data integration complexities, patient privacy concerns, computational demands, and regulatory uncertainties must be addressed to ensure reliable implementation. This study underscores the importance of further research, cross-disciplinary collaboration, and the establishment of standardized regulatory frameworks to facilitate the responsible integration of digital twin technology in healthcare. With continued advancements and appropriate safeguards, digital twins can contribute to more precise, efficient, and patient-centered healthcare delivery, paving the way for a new era of medical innovation.