Asian Health, Science and Technology Reports

Chitosan-coated Eggshells Help Slow the Deterioration of Internal Egg Quality During Storage at Room Temperature under Tropical Conditions

Thanaset Thongsaiklaing, Samsuree Seenamnung, Elham Waehama — Thu, 01 Oct 2026 00:00:00 +0700

Egg deterioration during room-temperature storage is a major challenge in tropical regions lacking practical refrigeration. While chitosan coatings preserve eggs, research on how different concentrations perform under tropical ambient conditions remains limited. Therefore, this study evaluated the concentration-dependent effects of chitosan coatings on internal egg quality during extended room-temperature storage. A total of 360 freshly laid chicken eggs were randomly assigned to four treatments: non-coated eggs (NC), eggs coated with 1% acetic acid (AC), eggs coated with 1% chitosan solution (1CS), and eggs coated with 2% chitosan solution (2CS). Eggs were stored at tropical room temperature for 35 days, and egg weight loss, Haugh Unit (HU), and albumen pH were evaluated at weekly intervals. Egg weight loss increased progressively in all treatments throughout storage. Although chitosan-coated eggs tended to exhibit lower cumulative weight loss than the NC and AC groups, differences among treatments were not statistically significant (P > 0.05). In contrast, chitosan coating significantly affected internal egg quality parameters. Chitosan-coated eggs maintained significantly higher HU values during storage than non-coated or acetic acid-treated eggs (P < 0.05), demonstrating superior preservation of internal quality. Similarly, albumen pH increased in all treatments during storage; however, the increase was significantly slower in chitosan-coated eggs (P < 0.05). These results suggest that chitosan coatings may act as semi-permeable barriers, reducing moisture and gas exchange to slow deterioration in internal egg quality during storage. Notably, the 2% chitosan treatment provided the greatest preservation effect, maintaining substantially higher HU values at the end of the 35-day storage period than the control treatments. Visually, eggs coated with 2% chitosan maintained Grade B quality after 40 days of storage, suggesting extended marketability under tropical ambient conditions. However, longer storage periods and quantitative evaluations are needed to confirm the exact shelf-life extension. In conclusion, chitosan coating effectively delayed the deterioration of internal egg quality during room-temperature storage, with the 2% concentration providing the greatest protective effect. This study supports using concentration-dependent chitosan coatings as an eco-friendly way to preserve egg quality in tropical areas with limited refrigeration.

A Machine Learning-Based Student Dropout Risk Prediction and Early Warning System

Thu, 01 Oct 2026 00:00:00 +0700

Student dropout rates in higher education have become a significant problem for universities worldwide, impacting both student academic achievement and institutional stability. To address this problem, this research proposes the Student Dropout Risk Prediction and Early Warning System (SDRP-EWS), a machine learning framework designed to predict the risk of eventual student dropout. Utilizing multi-semester academic performance data from 20,919 undergraduate students at Sakon Nakhon Rajabhat University, Thailand (2014–2024), the study developed logistic regression, random forest, and XGBoost models. To support early and continuous monitoring, these models were evaluated at three distinct prediction horizons: following the first semester, after the second semester, and after three or more semesters. Cohen's d effect size analysis compared ROC-AUC distributions across engineered feature sets, confirming the practical advantage of the selected model configurations. The results show that XGBoost consistently outperformed other models across all prediction stages, achieving F1-scores for the dropout class ranging from 0.65 in the early stages up to 0.93 in later semesters with comprehensive academic histories. This study's primary contribution is integrating the top-performing multi-semester model into a functional, web-based prototype dashboard tailored for Thai higher education. By providing academic advisors with intuitive risk scores and key contributing factors, the system demonstrates strong potential as an evidence-based decision-support tool to facilitate proactive, targeted institutional interventions.

Predicting Health Insurance Claims in Thailand Using Bayesian Multilevel Logistic Regression and Socioeconomic Indicators

Mahatthakorn Plensamai — Tue, 06 Oct 2026 00:00:00 +0700

Socioeconomic disparities may influence the probability of health insurance claims and the financial sustainability of insurance systems. This study developed a Bayesian hierarchical logistic model to predict the probability that an individual had at least one recorded health insurance claim during the study period. Secondary individual-level data covering 2020–2023 were obtained from the National Statistical Office of Thailand and linked with province-level healthcare indicators from the Ministry of Public Health and regional economic indicators. The initial merged dataset contained 52,870 observations. After excluding records with missing outcomes, incomplete predictors, duplicate identifiers, and unmatched geographic information, the final analytic sample comprised 48,320 individuals from 77 provinces. Individuals were modeled as nested within provinces, and posterior distributions were estimated using Hamiltonian Monte Carlo. The Bayesian hierarchical model achieved an area under the curve of 0.862, exceeding logistic regression, random forest, and gradient boosting by 0.081, 0.039, and 0.031, respectively. It also produced the lowest root mean square error of 0.093 and Brier score of 0.072. Higher income, education, employment, healthcare access, and regional gross domestic product were associated with lower claim probability, whereas higher health expenditure was associated with greater claim probability. Posterior convergence and sensitivity analyses indicated stable estimates across alternative prior specifications. These findings demonstrate that Bayesian hierarchical modeling can improve claim-risk prediction while explicitly quantifying parameter and predictive uncertainty. The model may support risk classification, resource allocation, and equity-oriented health insurance policy in Thailand.

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Asian Health, Science and Technology Reports — Thu, 01 Oct 2026 00:00:00 +0700

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Asian Health, Science and Technology Reports — Tue, 06 Oct 2026 00:00:00 +0700

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Biomarkers for Occupational Chemical Exposure: A Narrative Review from Traditional Monitoring to Omics and Digital Health Applications

Chan Pattama Polyong, Thawatchai Eksanti, Pichitra Patipat, Kornwika Harasarn — Thu, 01 Oct 2026 00:00:00 +0700

Occupational exposure to hazardous chemicals remains a major public health challenge, particularly in developing countries where regulatory enforcement and surveillance systems are limited. This review synthesizes recent advances in the application of biomarkers for assessing occupational chemical exposure and related health effects. Relevant literature published between 2015 and 2025 was identified through searches of major scientific databases, including PubMed and Google Scholar, using keywords related to occupational exposure, biomarkers, biomonitoring, omics, and digital health. Studies addressing biomarkers of exposure, effect, and susceptibility in occupational settings were reviewed and synthesized. Biomarkers of exposure, effect, and susceptibility provide direct and sensitive measures that link external exposure with internal dose and biological responses. Evidence shows strong associations between benzene exposure and hematotoxicity, organophosphate pesticides and neurotoxicity, heavy metals and renal impairment, as well as organic solvents and hepatic injury. Importantly, the usefulness of biomarkers depends on their biological half-life and the exposure window they represent, ranging from recent exposure to cumulative long-term exposure. Validation criteria such as sensitivity, specificity, reproducibility, and clinical relevance are essential to ensure analytical reliability and health risk interpretation. Emerging technologies, including omics-based molecular markers, non-invasive matrices, multi-matrix monitoring, and biosensor-driven point-of-care testing, are revolutionizing biomonitoring practices. These innovations, together with the integration of genetic and environmental data, are shaping a new era of Precision Occupational Health, enabling early intervention and individualized prevention. Furthermore, advances in wearable sensors and digital health platforms support near real-time occupational health surveillance and more proactive risk management. In conclusion, the integration of validated biomarkers into occupational health surveillance systems provides a powerful strategy to enhance chemical risk management, safeguard workers’ health, and inform policy decisions globally.