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.

Rice bran is a rich source of health-promoting nutrition and bioactive compounds; nevertheless, the properties of rice brans depend on cultivars, ages, and preparation methods, drawing the potential of raw materials for health benefits. Therefore, this research aimed to investigate the health-promoting properties of fermented rice bran extracts from cultivar black rice (H7F) and germinated brown rice (G13F), focusing on their prebiotic, antipathogenic bacteria activity and safety demonstrated in vitro and in vivo study models, respectively. Here, the screening of metabolites’ change after rice bran fermentation by ATR-FTIR spectra revealed specific peaks corresponding to the composited components of protein, carbohydrate, and lipid. Then, in the in vitro study, the prebiotic capability of H7F and G13F extracts was demonstrated by a growth-promoting effect on Lactobacillus delbrueckii subsp. lactis under specific acidic conditions. Furthermore, antipathogenic bacterial activity against Escherichia coli and Staphylococcus aureus was presented at 25 mg/mL of MIC values and 50 mg/mL of MBC of both fermented rice bran extracts, eliminating the bacteria by interfering with the biofilm formation. For safety, an acute and chronic toxicity study using Wistar rats was conducted, in which changes in the body and organ weights, histopathology of organs, blood chemistry, and hematological parameters were observed after H7F and G13F treatment. Desirably, they showed no toxicity, with a significant reduction in blood cholesterol levels in the chronic treatment of H7F and G13F. Conclusively, the overall results evidenced the health benefits of H7F and G13F related to their prebiotic and antipathogenic bacteria properties and hypocholesterolemia potential with a high level of safety. Therefore, the fermented rice bran extracts were demonstrated as potential materials for the further development of functional ingredients and health products.

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.

High-density lipoprotein (HDL) and exosomes are promising sources of biomarkers. However, the limited sample volume and access to the ultracentrifuge equipment are still an issue during HDL and exosome isolation. This study aimed to isolate HDL and exosomes using an ultracentrifugation-free method with various small serum volumes. HDL was isolated from 200 µL (HDL200) and 500 µL (HDL500) of sera. Three different volumes: 50 µL (Exo50), 100 µL (Exo100), and 250 µL (Exo250) were used for exosome isolation. HDL and exosomes were isolated using commercial kits with the modified method and characterized by multiple approaches. The HDL levels of HDL200 and HDL500 were not significantly different (p > 0.05), with percent recoveries of >90%. HDL200 and HDL500 had the same protein pattern with a biochemical similarity of 99.60 ± 0.10%. The particle sizes of Exo50, Exo100, and Exo250 were in the expected range. All isolated exosomes exhibited a similar protein pattern with a biochemical similarity of >99%. In conclusion, two different serum volumes (200 and 500 µL) and three different serum volumes (50, 100, and 250 µL) can be employed for HDL and exosome isolation, respectively. The possibility of HDL and exosome isolation with small volumes will accelerate biomarker discoveries with various molecular diagnostic approaches.

Overweight and obesity remain global health issues that should not be overlooked. They may impair immune health and alter the function of natural killer (NK) cells, immune effectors that may help regulate immune surveillance and associate metabolic inflammation. Therefore, the present case‑control study investigated the immunological profiles of NK cells, both overall and by subpopulations [neural cell adhesion molecule 1 (NCAM1/CD56) staining: Weak (CD56Dim) and strong (CD56Bright)], in 125 Thai participants stratified into five groups by lipid profiles [normal lipid (NL) and dyslipidemia (DL) and body mass index (BMI; normal weight [NW], overweight [OW] and obesity class‑I (OB‑I)]. NK cells' immunological profiles were determined by flow cytometry. It was found that total NK cell percentages were significantly higher in the DL/OB‑I group than in the NL/NW group (P<0.01) and the NL/OW group (P<0.05). Interferon‑gamma (IFN‑γ)‑producing CD56Bright NK cell percentages were also highest in the DL/OB‑I group. BMI correlated positively with total NK cell percentage (R=0.214; P<0.05), CD56Dim NK cell percentage (R=0.217, P<0.05), and IFN‑γ‑producing CD56Bright NK cell percentage (R=0.463; P<0.01). Altogether, the findings demonstrated that NK cell percentage and activation increased markedly with BMI and dyslipidemia. The immunological profiles of NK cells were particularly altered in the DL/OB‑I group, characterized by higher percentages and cytokine‑producing capacity. Therefore, BMI and lipid profiles must be controlled to preserve immunological status and prevent subsequent low‑grade inflammation.

Surface‑enhanced Raman spectroscopy (SERS) is a powerful laser‑based technique with high sensitivity and rapid detection. The present study aimed to establish a model for distinguishing Mycobacterium abscessus (nontuberculous mycobacteria; NTM) from M. tuberculosis H37Ra (MTB‑H37Ra; a nonvirulent strain) based on genomic DNA detection, as well as distinguishing M. abscessus subspecies abscessus (MAB) from M. abscessus subspecies massiliense (MMAS). OnSpec‑Prime SERS chips and a portable Raman spectrometer device were used. Machine learning approaches, including linear discriminant analysis (LDA), random forest, extreme gradient boosting (XGB), and logistic regression (LR), as well as the receiver operating characteristic (ROC) curve and area under the curve (AUC) were analyzed. A competence model to distinguish NTM from MTB‑H37Ra was established; MAB and MMAS were potentially differentiated. At 15 and 50 ng/µl of genomic DNA, respectively, LR demonstrated 99.74 and 99.73% accuracy in differentiating NTM from MTB‑H37Ra; XGB displayed 96.25 and 92.97% accuracy in differentiating between MAB and MMAS. LDA revealed clear clustering in each group. The ROC curves showed strong performance of the XGB model across various DNA concentrations. All models achieved an excellent to perfect AUC of 0.96 to 1.00. The present study established a competence model using SERS which may represent a rapid and high‑accuracy detection approach, especially in M. abscessus subspecies‑level discrimination in clinical specimens.