Carcinogenic and non-carcinogenic health hazards of potentially toxic elements in commonly consumed rice cultivars in Dhaka city, Bangladesh.

The study aimed to assess the level of potentially toxic elements (As, Cd, Pb, Zn, Cu, Cr, Mn, and Ni) and associated health implications through commonly consumed rice cultivars of Bangladesh available in Capital city, Dhaka. The range of As, Cd, Pb, Zn, Cu, Cr, Mn, and Ni in rice grains were 0.04-0.35, 0.01-0.15, 0.01-1.18, 10.74-34.35, 1.98-13.42, 0.18-1.43, 2.51-22.08, and 0.21-5.96 mg/kg fresh weight (FW), respectively. The principal component analysis (PCA) identified substantial anthropogenic activities to be responsible for these elements in rice grains. The estimated daily intake (EDI) of the elements was below the maximum tolerable daily intake (MTDI) level. The hazard index (HI) was above the threshold level, stating non-carcinogenic health hazards from consuming these rice cultivars. The mean target cancer risk (TCR) of As and Pb exceeded the USEPA acceptable level (10-6), revealing carcinogenic health risks from the rice grains.

Comparative Seed Proteome Profile Reveals No Alternation of Major Allergens in High-Yielding Mung Bean Cultivars.

Mung bean contains up to 32.6% protein and is one of the great sources of plant-based protein. Because many allergens also function as defense-related proteins, it is important to determine their abundance levels in the high-yielding, disease-resistant cultivars. In this study, for the first time, we compared the seed proteome of high-yielding mung bean cultivars developed by a conventional breeding approach. Using a label-free quantitative proteomic platform, we successfully identified and quantified a total of 1373 proteins. Comparative analysis between the high-yielding disease-resistant cultivar (MC5) and the other three cultivars showed that a total of 69 common proteins were significantly altered in their abundances across all cultivars. Bioinformatic analysis of these altered proteins demonstrated that PDF1 (a defensin-like protein) exhibited high sequence similarity and epitope matching with the established peanut allergens, indicating a potential mung bean allergen that showed a cultivar-specific response. Conversely, known mung bean allergen proteins such as PR-2/PR-10 (Vig r 1), Vig r 2, Vig r 4, LTP1, ß-conglycinin, and glycinin G4 showed no alternation in the MC5 compared to other cultivars. Taken together, our findings suggest that the known allergen profiles may not be impacted by the conventional plant breeding method to develop improved mung bean cultivars.

Deep Clustering of Electronic Health Records Tabular Data for Clinical Interpretation.

Machine learning applications are widespread due to straightforward supervised learning of known data labels. Many data samples in real-world scenarios, including medicine, are unlabeled because data annotation can be time-consuming and error-prone. The application and evaluation of unsupervised clustering methods are not trivial and are limited to traditional methods (e.g., k-means) when clinicians demand deeper insights into patient data beyond classification accuracy. The contribution of this paper is three-fold: 1) to introduce a patient stratification strategy based on a clinical variable instead of a diagnostic label, 2) to evaluate clustering performance using within-cluster homogeneity and between-cluster statistical difference, and 3) to compare widely used traditional clustering algorithms (e.g., k-means) with a state-of-the-art deep learning solution for clustering tabular data. The deep clustering method achieves superior within-cluster homogeneity and between-cluster separation compared to k-means and identifies three statistically distinct and clinically interpretable high blood pressure patient clusters. The proposed clustering strategy and evaluation metrics will facilitate the stratification of large patient cohorts in health science research without requiring explicit diagnostic labels.