Speciation and bioaccessibility of arsenic in rice under different cooking methods and its implication in risk assessment.

Numerous studies have studied the health risk assessment of human exposure to As or bioaccessible As via rice intake; however, the bioaccessibility of different As species in rice is seldom reported. In the present study, 31 rice samples were collected from markets or individual growers to investigate the speciation and bioaccessibility of As. Five different species (AsIII, AsV, DMA, MMA, and AsB) were detected in rice samples from different regions, among which AsIII accounted for the largest proportion (62.95% in average), followed by DMA and AsV. In addition, the cooking method could facilitate the release of As from rice into gastric and intestinal juice, and subsequently increase the bioaccessibility of As. The bioaccessibility of inorganic As in cooked rice ranged from 71.83 to 100%, and that of organic As ranged from 31.69 to 61.04%. Non-carcinogenic and carcinogenic risk assessment of children and adults exposure to As via rice intake considering the bioaccessibility of cooked rice was carried out. The target hazard quotient (THQ) of iAs and total As for children ranged from 0.21 to 1.61 and 0.48 to 2.26, respectively, while those for adults ranged from 0.12 to 0.88 and 0.26 to 1.23, respectively. Incremental lifetime cancer risk (ILCR) for children and adults ranged from 9.57 [Formula: see text] 10-5 to 7.25 [Formula: see text] 10-4 and 5.21 [Formula: see text] 10-5 to 3.95 [Formula: see text] 10-4, respectively. The results of risk assessment indicated that children would face a higher health risk than adults when they took the same type of rice as their staple food.

Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data.

Single-cell sequencing technologies have emerged to address new and longstanding biological and biomedical questions. Previous studies focused on the analysis of bulk tissue samples composed of millions of cells. However, the genomes within the cells of an individual multicellular organism are not always the same. In this study, we aimed to identify the crucial and characteristically expressed genes that may play functional roles in tissue development and organogenesis, by analyzing a single-cell transcriptomic atlas of mice. We identified the most relevant gene features and decision rules classifying 18 cell categories, providing a list of genes that may perform important functions in the process of tissue development because of their tissue-specific expression patterns. These genes may serve as biomarkers to identify the origin of unknown cell subgroups so as to recognize specific cell stages/states during the dynamic process, and also be applied as potential therapy targets for developmental disorders.

Investigating the gene expression profiles of cells in seven embryonic stages with machine learning algorithms.

The development of embryonic cells involves several continuous stages, and some genes are related to embryogenesis. To date, few studies have systematically investigated changes in gene expression profiles during mammalian embryogenesis. In this study, a computational analysis using machine learning algorithms was performed on the gene expression profiles of mouse embryonic cells at seven stages. First, the profiles were analyzed through a powerful Monte Carlo feature selection method for the generation of a feature list. Second, increment feature selection was applied on the list by incorporating two classification algorithms: support vector machine (SVM) and repeated incremental pruning to produce error reduction (RIPPER). Through SVM, we extracted several latent gene biomarkers, indicating the stages of embryonic cells, and constructed an optimal SVM classifier that produced a nearly perfect classification of embryonic cells. Furthermore, some interesting rules were accessed by the RIPPER algorithm, suggesting different expression patterns for different stages.

Copy Number Variation Pattern for Discriminating MACROD2 States of Colorectal Cancer Subtypes.

Copy number variation (CNV) is a common structural variation pattern of DNA, and it features a higher mutation rate than single-nucleotide polymorphisms (SNPs) and affects a larger fragment of genomes. CNV is related with the genesis of complex diseases and can thus be used as a strategy to identify novel cancer-predisposing markers or mechanisms. In particular, the frequent deletions of mono-ADP-ribosylhydrolase 2 (MACROD2) locus in human colorectal cancer (CRC) alters DNA repair and the sensitivity to DNA damage and results in chromosomal instability. The relationship between CNV and cancer has not been explained. In this study, on the basis of the genome variation profiling by the SNP array from 651 CRC primary tumors, we computationally analyzed the CNV data to select crucial SNP sites with the most relevance to three different states of MACROD2 (heterozygous deletion, homozygous deletion, and normal state), suggesting that these CNVs may play functional roles in CRC tumorigenesis. Our study can shed new insights into the genesis of cancer based on CNV, providing reference for clinical diagnosis, and treatment prognosis of CRC.