Mahidol University's Institutional Repository
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Recent Submissions
Technical Guidelines for the Evaluation of Scientific Evidence of Health and Function Claims for Food and Food Ingredients: A Federation of Asian Nutrition Societies (FANS) Consensus
(2026-04-01) Zhu J.; Sun G.; Wang Z.; Li C.; Bhaskaran K.; Chongviriyaphan N.; Hardinsayh; Tee E.S.; Yang Y.; Zhu J.; Mahidol University
Background: Health and function claims are central to advancing nutrition science and regulatory practice, particularly across Asia, where the harmonization of nutrition labeling standards faces unique and complex challenges. Fragmented national regulatory frameworks, combined with the region’s diverse range of food ingredients and traditional herbal products, have created substantial inconsistencies in scientific evaluation practices and cross-border trade. Objective: To establish a harmonized, evidence-based technical framework for evaluating the scientific evidence underpinning health and function claims for foods and food ingredients. Methods: The Task Force on Health & Function Claims of the Federation of Asian Nutrition Societies (FANS) developed this guideline through a structured process of expert consultation, iterative peer review, and formal consensus building. The framework comprises six core components: (1) core principles; (2) standardized procedures for evidence evaluation; (3) criteria for assessing literature quality; (4) criteria for grading evidence strength; (5) requirements for evidence report preparation and (6) evidence-based recommendations. Results: This guideline defines standardized terminology for health and function claims, establishes core principles for evidence-based nutrition practice, specifies the requirement of qualified evaluator, details comprehensive and replicable literature evaluation procedures, and adopts a four-tiered evidence strength grading system (Grades A–D). It prescribes transparent, verifiable pathways for claim substantiation and periodic reevaluation to uphold scientific rigor and ensure consumer protection. Conclusions: This guideline aligns with prevailing international standards while being specifically adapted to the Asian regional context. It provides a unified technical framework for the evaluation of health and function claims in Asia. It is designed to facilitate regulatory convergence across the region, safeguard consumer interests, and foster responsible innovation in the functional food sector, while laying a robust foundation for collaborative regional nutritional research and regulatory alignment.
Development of an antigen detection test kit (Melioidosis-ATK) for point-of-care diagnosis of melioidosis
(2026-01-01) Intaramat A.; Binmaeil H.; Bunsong T.; Deekae S.; Chayangsu S.; Fuangthong M.; Nguitragool W.; Burtnick M.N.; Brett P.J.; Chantratita N.; Intaramat A.; Mahidol University
Melioidosis is a fatal infectious disease caused by the environmental bacterium Burkholderia pseudomallei. Although it is highly endemic in tropical regions, melioidosis remains underdiagnosed. The disease presents with diverse clinical manifestations, but diagnosis relies on time-consuming bacterial culture. Many patients with poor outcomes are referred from community hospitals, where culture facilities are unavailable. Early and accurate diagnosis is critical for timely treatment and improved outcomes. We developed an antigen detection test kit (Melioidosis-ATK) that targets the 6-deoxy-heptan capsular polysaccharide (CPS) of B. pseudomallei. The diagnostic performance of the assay was evaluated using 88 clinical samples, including blood culture broth (obtained from incubated blood culture bottles) (N = 17), sputum (N = 33), pus (N = 17), urine (N = 14), and other body fluids (N = 7) from 88 patients with melioidosis, 204 samples from 195 patients with other bacterial infections, and 38 samples from 38 patients with negative blood culture. The assay demonstrated a limit of detection of 4.33 × 103 CFU/mL for B. pseudomallei and 0.2 ng/mL for purified CPS. The Melioidosis-ATK demonstrated high diagnostic performance, with a sensitivity of 100% for blood culture broth, 84.9% for sputum, 88.2% for pus, 100% for urine, and 85.7% for other body fluids. The assay also exhibited a specificity of 100% across all sample types evaluated. The Kappa coefficient of agreement between the antigen test and culture for all clinical specimens ranged from 0.77 to 1.00. These findings suggest that the Melioidosis-ATK is a promising point-of-care (POC) diagnostic tool for use in rural hospitals, offering rapid, simple, equipment-free testing with high performance and has the potential to improve patient outcomes. IMPORTANCE Melioidosis is a life-threatening but underrecognized infectious disease caused by Burkholderia pseudomallei, which predominantly occurs in regions with limited diagnostic capacity. A definitive diagnosis typically relies on bacterial culture, which is slow, technically demanding, and often unavailable in resource-constrained settings. To address this problem, we developed a rapid antigen detection assay (Melioidosis-ATK) that targets the 6-deoxy-heptan capsular polysaccharide (CPS) of B. pseudomallei and evaluated its performance using multiple clinical specimen types. The test demonstrated high sensitivity and specificity and can be performed as a point-of-care (POC) test. By enabling prompt diagnosis and timely initiation of effective therapy, this assay represents a practical tool to enhance clinical management and reduce disease-associated mortality in endemic regions.
A mixed-methods evaluation of outreach service provision by the “Strengthening Migrant Access to Reproductive Health in Thailand” Initiative, 2020–2024
(2026-01-01) Hashmi A.; Aung K.K.; Wai N.S.; Misa P.; Thwin M.M.; Paw K.; Nosten S.; Jitham M.W.; Pateekhum C.; Pimpasorn W.; Wongchawengsup B.; Nosten F.; McGready R.; Hashmi A.; Mahidol University
Introduction – Despite considerable progress, pregnancy-related health outcomes are still below Sustainable Development Goal targets for many low-to-middle-income countries. This study evaluated the Strengthening Migrant Access to Reproductive Health in Thailand (SMARH-T) Initiative that included an outreach service provision (2020–2024) to address upstream determinants of prenatal care and a family planning service provision for undocumented migrant women and newborns along the Thailand–Myanmar border. Methods – This study employed a sequential explanatory mixed-methods design with a quantitative survey followed by qualitative interviews and focus group discussions. Participants were asked about their experiences with the initiative and its delivery of prenatal and family planning services. Implementation outcome frameworks were used to understand the acceptability, end-user satisfaction, appropriateness, feasibility, reach, and sustainability of the initiative. Results – A total of 407 migrant women were surveyed and 17 interviews and discussions with health providers, staff, and stakeholders (n = 98) were conducted. The outreach service provision allowed for comparable convenience (p < 0.001), travel time (<30 min, p < 0.001), and costs (
Sensor-driven modeling of complex systems using physics-informed supervised machine intelligence
(2026-06-01) Bhadola P.; Sable H.; Kumari P.; Kadian S.; Narayan R.; Chaudhary V.; Bhadola P.; Mahidol University
Modeling the intricate dynamics of complex systems is crucial for addressing global challenges ranging from pandemics to the climate crisis. While conventional first-principles models often struggle, advanced nanosensors employed in arrays and networks provide the high-dimensional and real-time data needed to understand these systems empirically. These datasets are often noisy, incomplete, and manipulated by stochastic, systematic, and nonlinear physical effects, making modeling and prediction challenging. Supervised machine learning (SML) has emerged as a powerful framework to address these challenges by determining explicit input–output relationships, unveiling fundamental system dynamics, enabling accurate prediction, and providing decision support. This review critically examines supervised approaches, including support vector machines algorithms, for sensor-driven modeling of complex systems, with particular emphasis on physics-informed machine learning, where physical laws and domain knowledge are embedded to enhance interpretability, robustness, and generalization. It highlights essential preprocessing methods, including data cleaning, feature engineering, and data fusion, where classical formulations like Kalman filtering, Fourier transforms, and weighted variance minimization integrate sensor physics with computational methods. Further, it discusses core supervised approaches, including regression and classification models, along with their physics-informed variants that integrate conservation laws, stochastic noise models, and network dynamics into learning algorithms as constraints. Through representative case studies across environmental monitoring, biomedical sensing, and industrial diagnostics, their practical utility has been established. Finally, open challenges in scalability, generalization, and integration with physics-informed machine intelligence are outlined with alternate solutions and prospects. This interdisciplinary integration of sensor physics and physics-guided SML represents a powerful paradigm of sensor intelligence for data-driven modeling of complex physical and biological systems.