University of Ruhuna

Abstract Tea is the most popular beverage consumed next to water worldwide. Various types of tea, including green, black, dark, white, and oolong, are produced from the young, harvestable shoots of the tea plant, with each type undergoing a distinct manufacturing process. Tea is rich in bioactive constituents, including L-theanine, methylxanthines (caffeine, theophylline, and theobromine), polyphenols, γ-aminobutyric acid (GABA), saponins, polysaccharides, etc. Several studies indicate that tea and its phytochemicals exert diverse health-promoting effects. Its antioxidant activity, primarily attributed to polyphenolic compounds, has been associated with a reduced risk of non-communicable diseases, including cardiovascular disorders, certain cancers, neurodegenerative conditions, infectious diseases, and metabolic abnormalities. Tea also demonstrates immunomodulatory and anti-inflammatory effects and can modulate gut microbiota composition, functioning as a prebiotic. These biological activities have been observed in teas across both in vitro and in vivo studies. Additionally, tea consumption has been linked to psychological benefits, such as stress reduction and enhanced cognitive function. Although numerous studies have investigated the health-promoting properties of individual tea types, a comprehensive and systematically structured synthesis comparing green, black, dark, white, and oolong teas based on recent evidence remains limited. This systematic review provides a synthesis of peer-reviewed studies published between 2019 and 2025, focusing on the biological activities and therapeutic potential of major tea varieties and their bioactive compounds.

Pigment biofortification in rice, particularly through enhanced anthocyanin accumulation, is increasingly recognized in Asia for its potential to improve nutritional quality and antioxidant capacity while addressing regional health challenges. However, the regulatory network underlying anthocyanin biosynthesis in rice remains incompletely characterized, especially regarding MYB transcription factors that may control pigment accumulation in diverse germplasm. In this study, a genome-wide analysis of MYB transcription factors in Oryza sativa subsp. japonica identified 109 OsMYB genes. Phylogenetic comparison with known anthocyanin regulators from Arabidopsis thaliana and Oryza rufipogon revealed conserved clades containing potential pigment-related regulators. Integrative analyses including gene structure, conserved motif identification, promoter cis-element profiling, and miRNA target prediction were conducted to explore their regulatory potential. Expression profiling during grain development in black and mixed rice identified 32 OsMYB genes as potential candidates associated with anthocyanin accumulation. Promoter analysis revealed enrichment of light-responsive, stress-responsive, and developmental regulatory elements, suggesting coordinated regulation by environmental and developmental signals. Predicted interactions with several Osa-miRNAs further indicate potential post-transcriptional control of these genes. Among the candidates, OsMYB65 showed strong differential expression in pigmented rice grains and possessed multiple regulatory elements associated with anthocyanin biosynthesis, highlighting it as a promising regulator. Overall, this integrative genome-wide analysis identifies a prioritized set of MYB transcription factors that may regulate anthocyanin biosynthesis in rice, providing valuable targets for functional validation and pigment-oriented crop improvement.

Fungal nail infections (onychomycosis) remain challenging to treat due to prolonged therapy, poor cure rates, and safety concerns associated with oral antifungal agents. Although topical formulations are the preferred treatment option, their clinical effectiveness is constrained by the dense keratinized nail barrier. While formulation-driven strategies and device-based therapies have expanded the therapeutic approaches for onychomycosis, their clinical efficacy remains suboptimal, pointing towards the need for pre-clinical evaluation of their performance using more clinically relevant models during the development stage. Nail-integrated model systems provide a platform for evaluating the efficacy of novel antifungal topical treatments in a controlled disease-mimicking environment, thereby enhancing the likelihood of clinical success. This review critically evaluates in vitro and ex vivo models, including keratin-supplemented media, keratin biomembranes, animal hooves, and human nails, focusing on their physiological relevance, advantages and inherent limitations. Ex vivo experimental model designs reported in recent studies are discussed to spotlight their application in evaluating topical formulations and device-based interventions. Ex vivo human nail models, when rationally designed to reflect the structural and pathological architecture of onychomycosis, offer robust, clinically predictive platforms for formulation and device optimization, dose and treatment frequency and comparative efficacy assessment. Infected animal hooves serve as a surrogate for human nails and provide reproducible experimental models for screening the antifungal efficacy under standardized conditions. The available ex vivo data support the utility of these models in predicting clinical performance of antifungal formulations and devices during pre-clinical development; however, broader industrial adoption will require standardized and harmonized protocols, as well as further clinical validation.

Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due to limited access to dual-energy X-ray absorptiometry (DXA) in many areas. Artificial intelligence (AI) offers new opportunities to improve osteoporosis screening and management, but unvalidated tools pose risks of inconsistent care. This consensus was developed to provide regionally harmonized guidance on the safe, effective, and equitable use of AI in osteoporosis care. Purpose The aim of this work was to establish expert consensus recommendations on the role of AI in osteoporosis screening and management in the Asia-Pacific region. Key objectives were to define appropriate applications of AI (e.g., imaging-based bone assessment and fracture risk prediction) and specify minimum standards for validation and reporting, addressing region-specific implementation challenges and ensuring that AI use aligns with clinical guidelines and ethical principles. Methods This consensus was developed through multidisciplinary collaboration among experts across the Asia-Pacific region. Each participant reviewed draft statements, contributed feedback during virtual meetings, and provided insights based on clinical experience and current evidence. Consensus was reached iteratively until full agreement was achieved for all statements. The process integrated global best practices and regional adaptations, drawing from peer-reviewed studies, international AI guidelines, and local fracture registry data. The final recommendations emphasize the validation, transparency, and ethical implementation of AI within regional healthcare systems, ensuring compatibility with local regulations. Ultimately, twelve consensus statements were established to guide the responsible use of AI for osteoporosis screening and management in the Asia-Pacific region. Results The panel produced 12 consensus statements covering the role of AI as an adjunct for opportunistic osteoporosis screening rather than a diagnostic tool, requirements for imaging quality and AI model transparency, standards for validation and performance reporting, integration of AI with clinical risk stratification, demonstration of clinical utility in real-world settings, adherence to data protection laws and ethical AI principles, training of clinicians in AI use, strategies for implementation and monitoring (including post-market surveillance and feedback loops), and recognition of technical, clinical, and equity limitations of AI. All 12 statements give extensive recommendations for using AI to improve osteoporosis management while ensuring patient safety, accuracy, and equity. Conclusion This first Asia-Pacific consensus on AI in osteoporosis concludes that AI, when appropriately validated and implemented, can help bridge the osteoporosis care gap by identifying high-risk patients who would otherwise remain undiagnosed, thus facilitating earlier intervention. It emphasizes that AI should complement-not replace-standard diagnostic methods and clinical judgment. The guidance emphasizes validation, transparency, and ethical oversight to facilitate early intervention while minimizing risks associated with unvalidated or premature AI adoption.

The advancement of Intelligent Transportation Systems (ITS) relies fundamentally on reliable Vehicle-to-Everything (V2X) connectivity. While interest in V2X communication has surged with the rise of autonomous driving, experimental validation remains constrained by the limited availability and high cost of hardware-based testbeds. Consequently, much of the existing research depends on simulations that often fail to capture real-world complexities. This paper presents the design, implementation, and evaluation of a modular, cost-effective V2X testbed constructed entirely from commodity hardware components. The testbed supports both Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication and employs a lightweight, streamlined Basic Safety Message (BSM) structure. To demonstrate its practicality, we implement three representative V2X applications: collision detection, intelligent traffic management, and parking availability detection. Additionally, a Kalman filter-based trajectory prediction model is integrated to improve vehicle state estimation with minimal computational overhead. Experimental results across BSM broadcast frequencies of 1 to 10 Hz and vehicle speeds of 5 to 30 km/h demonstrate end-to-end latency below 3.5 ms and packet delivery ratios above 94