Antidiabetic

The current research is dedicated to the creation of a pediatric cough syrup formula with less synthetic preservatives based on the usage of natural antimicrobial agents. The use of synthetic preservatives in pediatric formulations e.g. sodium benzoate in excess amount leads to concerns about possible toxicity and hypersensitivity. To resolve this, a control syrup that had standard concentration of sodium benzoate (0.1) was used as a control group compared with an optimized formulation that had low concentration (0.03) of sodium benzoate with added honey, clove oil and citric acid. The recipes were tested in terms of physicochemical stability, antimicrobial activity and sensorial properties within 90 days. The challenge testing with the Escherichia coli, Bacillus subtilis, Candida albicans and Aspergillus niger showed successful inhibition of microbial growth by the natural-based preservative system similar to the synthetic preservative. Physicochemical characteristics, such as pH, viscosity, color, odor, and taste were at acceptable pharmacopeial values and this means that they were stable and palatable. Statistical analysis also established there was no significant difference (p less than 0.05) in formulations in microbial reduction and stability performance. The findings indicate that the partial replacement of synthetic preservatives with natural agents is likely to increase the safety of the formulations in question without affecting its quality or effectiveness.

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.