Mercury ions (Hg²⁺) are highly toxic environmental pollutants requiring rapid and cost-effective detection methods. This study presents an eco-friendly approach for the green synthesis of silver nanoparticles (AgNPs) using aqueous extracts of nutmeg (Myristica fragrans), pineapple (Ananas comosus), and jambulang (Syzygium cumini), which serve as both reducing and stabilizing agents. The formation of AgNPs was confirmed by UV-Vis spectroscopy, exhibiting characteristic surface plasmon resonance (SPR) peaks around 400 nm, and TEM analysis, which revealed spherical morphology with diameters of 4–6 nm. The synthesized AgNPs were evaluated as colorimetric sensors for Hg²⁺ detection, based on the color change from yellow/brown to colorless due to the Ag-Hg amalgamation process and the subsequent quenching of the SPR peak. Among the three extracts, S. cumini demonstrated superior performance with a limit of detection (LOD) of 0.47 ppm, significantly lower than A. comosus (1.16 ppm) and M. fragrans (2.51 ppm). To demonstrate practical utility, the AgNPs were immobilized onto cotton buds as a portable, “naked-eye” sensor, enabling on-site mercury detection without sophisticated instrumentation. These results suggest that green-synthesized AgNPs offer a sustainable and efficient platform for monitoring heavy metal contamination in aqueous environments.
Radiation-induced oral mucositis requires predictive biomarkers for personalized therapy. We evaluated salivary IL-6 for predicting severe mucositis and response to food-grade bee products. Secondary analysis of 51 head/neck cancer patients randomized to honey (n = 15), Taiwanese green propolis (TGP, n = 17), or usual-care (n = 19). Both interventions used standardized preparations (10 g in 20 mL water, 10 mL TID). Salivary cytokines were analyzed using ELISA at baseline and during early radiotherapy. Salivary IL-6 at week 3 of radiotherapy demonstrated superior predictive performance (AUC = 0.780, 95
This study proposes an automatic learning activity classification framework based on the Tri Pramana concept using immersive Virtual Reality (VR) video data. Tri Pramana comprising, Sabda, Pratyaksa, and Anumana, is operationalized in this research as a set of observable learning activity categories rather than as a comprehensive epistemological model. Video data were collected from a Fiber Optic Splicing practicum conducted in an immersive VR environment, with each video clip segmented into 5-second sequences. A total of 1,036 video clips were used, consisting of 824 training samples (80
This study aimed to investigate whether Mindful Learning (MiL), Meaningful Learning (MeL), and Joyful Learning (JL) are predictors of Deep Learning (DL) in AI-based language learning. The study employed a convergent mixed-methods design by combining phenomenology and ex-post facto approaches. This study recruited eight EFL students for the interviews and 276 EFL students for the quantitative part. The data were collected through semi-structured interviews, researcher field notes, and questionnaires. The data were analyzed using inductive thematic analysis, descriptive statistics, Pearson Product Moment, and SEM. The findings revealed that qualitatively, MiL, MeL, and JL are related to DL. However, the SEM outputs indicated that JL is the only non-predictor. The study offers four implications directed for the use of AI-based applications in facilitating DL in AI-based learning in EFL contexts.
Large vessel occlusion (LVO) requires prompt detection, and CT angiography (CTA) is frequently used due to its short acquisition time and visibility of vessels. Artificial intelligence (AI), including Viz-LVO, CINA-LVO, RAPID-CTA and JLK, may be available as emerging tools for supporting timely and accurate diagnoses. This study aimed to examine and summarise the evidence of AI diagnostic performance in detecting LVO. Scopus, PubMed and ScienceDirect were utilised to search relevant articles before February 2, 2025. Studies were included in the primary outcomes analysis if they reported an overall confusion diagnostic matrix and were included in the secondary outcomes if they reported AI's diagnostic performance by occlusion site. Of the 878 records, 11 articles were included, and 10.937 patients were identified. The pooled sensitivity and specificity were 0.87 (95% CI: 0.76-0.93) and 0.95 (95% CI: 0.91-0.97). The positive likelihood ratio (PLR) showed statistical significance (9.55 (95% CI: 5.79-13.30; p < 0.001; I 2: 99.9%)), whereas the negative likelihood ratio (NLR) was not significant with a pooled value of 0.14 (95% CI: 0.03-0.25; p < 0.624; I 2: 0%). The pooled AUC and DOR were substantial, with a pooled value of 0.87 (95% CI: 0.83-0.92; p < 0.001; I 2: 98.4%) and 4.69 (95% CI: 4.19-5.19; 0.001; I 2: 98.5%), respectively. Three covariates were identified (type of AI, AI software and region). However, significant heterogeneity remains in pooled PLR, AUC and DOR. The anterior circulation occlusion performed was generally acceptable, demonstrating good performance for M1 and ICA-type T occlusion and moderate performance for M2 occlusion. However, poor performance was observed in ICA Type I and posterior circulation occlusion. In conclusion, AI has demonstrated excellent performance in sensitivity, specificity, PLR, AUC and DOR while showing limitations in NLR, suggesting that negative cases detected by AI require careful reevaluation through imaging review and assessment of patients' clinical profiles to ensure better diagnostic accuracy.
