Antioxidant

The current study used HEK293 and HeLa cell lines to compare liposomes and niosomes as nanocarriers for gene transfer. After preparing and characterizing liposomal and niosomal formulations containing GFP-encoding plasmid DNA, the effectiveness and cytotoxicity of transfection were evaluated in vitro. According to ANOVA and post hoc analysis, liposomes demonstrated a considerably greater transfection efficiency than niosomes in both cell lines (p 0.05). These results show that niosomes provide a safer substitute with decreased cytotoxicity, indicating a trade-off between efficiency and safety, even though liposomes perform better at delivering genes. Therefore, the study offers a comparison framework for choosing appropriate nanocarriers for upcoming gene therapy applications.

Background: Musculoskeletal disorders (MSDs), such as osteoarthritis, fractures, ligament injuries, and chronic back pain, affect over 1.3 billion people worldwide, posing significant clinical and economic burdens. Conventional orthopedic physiotherapy is essential for restoring mobility and reducing disability but is limited by subjectivity, variability in treatment outcomes, and challenges in personalization. Artificial intelligence (AI) and machine learning (ML)have emerged as transformative tools to overcome these gaps. Methodology: This mini-review synthesizes recent advances in AI/ML applications across diagnostic imaging, gait and posture analysis, wearable sensor technologies, predictive analytics, rehabilitation robotics, and tele-physiotherapy. Clinical applications, case studies, and technological innovations are evaluated to highlight their impact on patient assessment, treatment planning, and rehabilitation. Challenges such as data privacy, limited datasets, integration into clinical workflows, and algorithmic bias are also discussed. Results: AI enhances diagnostic precision through automated medical imaging analysis and computer vision–based gait assessment. Wearable sensors combined with ML enable continuous monitoring and adaptive therapy adjustments, while predictive models improve early detection of injury risks and disease progression. AI-assisted rehabilitation tools—including robotic systems, VR/AR platforms, and gamified therapy—enhance patient engagement, adherence, and recovery outcomes. Clinical applications demonstrate improvements in post-operative rehabilitation, chronic back pain management, arthritis grading, sports injury recovery, and remote physiotherapy delivery. Despite barriers, federated learning, IoT integration, multimodal AI, and fully autonomous physiotherapy assistants are emerging as future solutions. Conclusion: AI and ML are revolutionizing orthopedic physiotherapy by enabling precision diagnosis, personalized treatment, and adaptive rehabilitation strategies. While challenges in privacy, clinical adoption, and algorithmic robustness remain, ongoing innovations promise to establish AI as a cornerstone of musculoskeletal care. These technologies are poised to enhance patient-centered rehabilitation, improve global accessibility, and shape the future of physiotherapy practice.

This study represents glucose-responsive insulin delivery by a microneedle patch for improved treatment of T1D. In direct response to increased blood glucose levels, insulin is released from the biodegradable glucose-responsive polymers comprising the patch. Results from the in-vitro experiment on synthetic skin models proved very effective; dose-dependent insulin release was measured, with a maximum of up to 90% after 6 hours at 250 mg/dL glucose. In-vivo tests on diabetic rats showed that it reduced blood glucose levels by 50%, which is far better than conventional insulin delivery modes. Histological studies showed no evidence of skin damage. This microneedle patch may provide a more effective and user-friendly alternative to insulin delivery systems.