Environmental arsenic (As) and its potential relationship with endemic disease in southwestern China

Quantification of arsenic in real samples using a spectrophotometric cloud point extraction of the formed ion pair with astrazon orange G

A novel cloud-point extraction (CPE) procedure for the determination of ultra-trace amounts of arsenic species in real samples, purchased from the local market by spectrophotometer was developed. Inorganic arsenic species analysis in water, beverages, and foods has become increasingly important in recent years, as arsenic species are considered carcinogenic and are assessed at significant levels in samples. The technique is established on a selective ternary complex of As(V) with astrazon orange G (AOG+) in the presence of tartaric acid and polyethylene glycol tertoctylphenyl ether (Triton X-114) at pH 4.0. The calibration curve developed within range 3.0-160 ng/mL with a correlation coefficient of 0.9988 for As(V) provided a preconcentration factor of 200 and a limit of detection (3S blank/m) of 0.88 ng/mL under optimum investigation conditions. The results of molar absorptivity and Sandell sensitivity are calculated and found to be 4.38 × 105 L/mol cm and 0.018 ng cm-2, respectively. The statistical treatment of data obtained from the proposed and GF-AAS procedures are compared in terms of Student's t-tests and variance ratio F-tests has revealed no significant differences. The methodology has been effectively confirmed by assessing real samples and comparing it to the GF-AAS method statistically.

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Multifaceted roles of silicon nano particles in heavy metals-stressed plants

Heavy metal (HM) contamination has emerged as one of the most damaging abiotic stress factors due to their prominent release into the environment through industrialization and urbanization worldwide. The increase in HMs concentration in soil and the environment has invited attention of researchers/environmentalists to minimize its' impact by practicing different techniques such as application of phytohormones, gaseous molecules, metalloids, and essential nutrients etc. Silicon (Si) although not considered as the essential nutrient, has received more attention in the last few decades due to its involvement in the amelioration of wide range of abiotic stress factors. Silicon is the second most abundant element after oxygen on earth, but is relatively lesser available for plants as it is taken up in the form of mono-silicic acid, Si(OH)4. The scattered information on the influence of Si on plant development and abiotic stress adaptation has been published. Moreover, the use of nanoparticles for maintenance of plant functions under limited environmental conditions has gained momentum. The current review, therefore, summarizes the updated information on Si nanoparticles (SiNPs) synthesis, characterization, uptake and transport mechanism, and their effect on plant growth and development, physiological and biochemical processes and molecular mechanisms. The regulatory connect between SiNPs and phytohormones signaling in counteracting the negative impacts of HMs stress has also been discussed.

Identification of hub genes and key pathways in arsenic-treated rice (Oryza sativa L.) based on 9 topological analysis methods of CytoHubba

BACKGROUND

Arsenic is a toxic metalloid that can cause acute and chronic adverse health problems. Unfortunately, rice, the primary staple food for more than half of the world's population, is generally regarded as a typical arsenic-accumulating crop plant. Evidence indicates that arsenic stress can influence the growth and development of the rice plant, and lead to high concentrations of arsenic in rice grain. But the underlying mechanisms remain unclear.

METHODS

In the present research, the possible molecules and pathways involved in rice roots in response to arsenic stress were explored using bioinformatics methods. Datasets that involving arsenic-treated rice root and the "study type" that was restricted to "Expression profiling by array" were selected and downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the arsenic-treated group and the control group were obtained using the online web tool GEO2R. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to investigate the functions of DEGs. The protein-protein interactions (PPI) network and the molecular complex detection algorithm (MCODE) of DEGs were analyzed using STRING and Cystoscope, respectively. Important nodes and hub genes in the PPI network were predicted and explored using the Cytoscape-cytoHubba plug-in.

RESULTS

Two datasets, GSE25206 and GSE71492, were downloaded from Gene Expression Omnibus (GEO) database. Eighty common DEGs from the two datasets, including sixty-three up-regulated and seventeen down-regulated genes, were then selected. After functional enrichment analysis, these common DEGs were enriched mainly in 10 GO items, including glutathione transferase activity, glutathione metabolic process, toxin catabolic process, and 7 KEGG pathways related to metabolism. After PPI network and MCODE analysis, 49 nodes from the DEGs PPI network were identified, filtering two significant modules. Next, the Cytoscape-cytoHubba plug-in was used to predict important nodes and hub genes. Finally, five genes [Os01g0644000, PRDX6 (Os07g0638400), PRX112 (Os07g0677300), ENO1(Os06g0136600), LOGL9 (Os09g0547500)] were verified and could serve as the best candidates associated with rice root in response to arsenic stress.

CONCLUSIONS

In summary, we elucidated the potential pathways and genes in rice root in response to arsenic stress through a comprehensive bioinformatics analysis.