In the aging process, the presence of interleukin (IL)-17-producing CD4+CD28-NKG2D+T cells (called pathogenic CD4+ T cells) is strongly associated with inflammation and the development of various diseases. Thus, their presence needs to be monitored. The emergence of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy empowered with machine learning is a breakthrough in the field of medical diagnostics. This study aimed to discriminate between the elderly with a low percentage (LP; ≤3%) and a high percentage (HP; ≥6%) of pathogenic CD4+CD28-NKG2D+IL17+ T cells by utilizing ATR-FTIR coupled with machine learning algorithms. ATR spectra of serum, exosome, and HDL from both groups were explored in this study. Only exosome spectra in the 1700–1500 cm⁻¹ region exhibited possible discrimination for the LP and HP groups based on principal component analysis (PCA). Furthermore, partial least square-discriminant analysis (PLS-DA) could differentiate both groups using the 1700–1500 cm⁻¹ region of exosome ATR spectra with 64% accuracy, 69% sensitivity, and 61% specificity. To obtain better classification performance, several spectral models were then established using advanced machine learning algorithms, including J48 decision tree, support vector machine (SVM), random forest (RF), and neural network (NN). Herein, NN was considered to be the best model with an accuracy of 100%, sensitivity of 100%, and specificity of 100% using serum spectra in the region of 1800–900 cm⁻¹. Exosome spectra in the 1700–1500 and combined 3000–2800 and 1800–900 cm⁻¹ regions using the NN algorithm gave the same accuracy performance of 95% with a variation in sensitivity and specificity. HDL spectra with the NN algorithm also showed excellent test performance in the 1800–900 cm⁻¹ region with 97% accuracy, 100% sensitivity, and 95% specificity. This study demonstrates that ATR-FTIR coupled with machine learning algorithms can be used to study immunosenescence. Furthermore, this approach can possibly be applied to monitor the presence of pathogenic CD4+ T cells in the elderly. Due to the limited number of samples used in this study, it is necessary to conduct a large-scale study to obtain more robust classification models and to assess the true clinical diagnostic performance.

The beneficial physiological effects of traditional Thai massage (TTM) have been previously documented. However, its effect on immune status, particularly in the elderly, has not been explored. This study aimed to investigate the effects of multiple rounds of TTM on senescent CD4+ T cell subsets in the elderly. The study recruited 12 volunteers (61–75 years), with senescent CD4+ T cell subsets, who received six weekly 1-h TTM sessions or rest, using a randomized controlled crossover study with a 30-day washout period. Flow cytometry analysis of surface markers and intracellular cytokine staining was performed. TTM could attenuate the senescent CD4+ T cell subsets, especially in CD4+28^null NKG2D+ T cells (n = 12; p < 0.001). The participants were allocated into two groups (low < 2.75% or high ≥ 2.75%) depending on the number of CD4+28^null NKG2D+ T cells. After receiving TTM over 6 sessions, the cell population of the high group had significantly decreased (p < 0.001), but the low group had no significant changes. In conclusion, multiple rounds of TTM may promote immunity through the attenuation of aberrant CD4+ T subsets. TTM may be provided as a complementary therapy to improve the immune system in elderly populations.

Expanded newborn screening (NBS) programs are essential for early detection and treatment. This study highlights the implementation of an expanded NBS program for inborn errors of metabolism (IEMs) and congenital hypothyroidism (CH) in rural Thailand, focusing on Health Regions 7 and 8 as a model for resource-limited settings. Using the KKU-IEM web-based platform, the program streamlined workflows, integrating logistics, real-time sample tracking, and electronic data management. Regular training sessions, continuous feedback, and systematic monitoring improved outcomes. Starting from October 2022, the program covered 98.6% of 123,692 live births, identifying 101 CH cases (1 in 1208 live births) and 20 IEM cases (1 in 6100 live births). The CH incidence was slightly higher than Thailand’s national average, while the IEM incidence was double that found in a previous Bangkok pilot study. Six cases highlighted maternal conditions affecting outcomes. Process improvements reduced the average reporting time from 9.13 days in 2023 to 8.4 days in 2024, with a 19% reduction in Bueng Kan Province. Efficiencies were driven by electronic ordering, real-time tracking, and stakeholder collaboration. This program demonstrates a scalable model for rural settings, emphasizing technology integration, collaboration, and quality control. Future efforts should refine diagnostics, expand disease coverage, and enhance long-term outcomes.

Expanded newborn screening (NBS) programs are essential for early detection and treatment. This study highlights the implementation of an expanded NBS program for inborn errors of metabolism (IEMs) and congenital hypothyroidism (CH) in rural Thailand, focusing on Health Regions 7 and 8 as a model for resource-limited settings. Using the KKU-IEM web-based platform, the program streamlined workflows, integrated logistics, real-time sample tracking, and electronic data management. Regular training sessions, continuous feedback, and systematic monitoring improved outcomes. From October 2022, the program covered 98.6% of 123,692 live births, identifying 101 CH cases (1 in 1,208 live births) and 20 IEM cases (1 in 6,100 live births). CH incidence was slightly higher than Thailand’s national average, while IEM incidence was double that of a Bangkok pilot study. Six cases highlighted maternal conditions affecting outcomes. Process improvements reduced average reporting time from 9.13 days in 2023 to 8.4 days in 2024, with a 19% reduction in Bueng Kan province. Efficiencies were driven by electronic ordering, real-time tracking, and stakeholder collaboration. This program demonstrates a scalable model for rural settings, emphasizing technology integration, collaboration, and quality control. Future efforts should refine diagnostics, expand disease coverage, and enhance long-term outcomes.