Thymol as a Component of Chitosan Systems—Several New Applications in Medicine: A Comprehensive Review

Thymol, a plant-derived monoterpene phenol known for its broad biological activity, has often been incorporated into chitosan-based biomaterials to enhance therapeutic efficacy. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we conducted a systematic literature review from 2018 to 2023, focusing on the biomedical implications of thymol-loaded chitosan systems. A review of databases, including PubMed, Scopus, and Web of Science was conducted using specific keywords and search criteria. Of the 90 articles, 12 were selected for the review. Thymol-loaded chitosan-based nanogels (TLCBS) showed improved antimicrobial properties, especially against multidrug-resistant bacterial antagonists. Innovations such as bipolymer nanocarriers and thymol impregnated with photosensitive chitosan micelles offer advanced bactericidal strategies and show potential for bone tissue regeneration and wound healing. The incorporation of thymol also improved drug delivery efficiency and biomechanical strength, especially when combined with poly(dimethylsiloxane) in chitosan–gelatin films. Thymol–chitosan combinations have also shown promising applications in oral delivery and periodontal treatment. This review highlights the synergy between thymol and chitosan in these products, which greatly enhances their therapeutic efficacy and highlights the novel use of essential oil components. It also highlights the novelty of the studies conducted, as well as their limitations and possible directions for the development of integrated substances of plant and animal origin in modern and advanced medical applications

Data-Driven Structural Health Monitoring in Laminated Composite Structures: Characterisation of Impact Damage

There is a high level of uncertainty for detecting damage, such as barely visible impact damage, hidden manufacturing defects, and subsurface cracks in laminated composites, which is a main limiting factor in wider use of these materials. This highlights the necessity of developing innovative structural health monitoring (SHM) strategies to meet the safety and reliability of current composite structures. In this research, a wide range of laminated composite specimens were designed, manufactured and tested under drop-weight impact with several impact energies to generate visual evidence of such impact events. The dataset was then used to train a user developed artificial intelligence (AI)-based algorithm to identify and predict damaged areas. The results showed that the developed algorithm could well identify the impact damage on both front and back faces of the specimens. The results obtained from the new AI-based platform were in good agreement with visual observations. The research highlights the importance of a high quality dataset in training the AI-based algorithms for visual SHM