Molecular Mechanism of Arsenic-Induced Neurotoxicity including Neuronal Dysfunctions
Bibliographic record
Abstract
Arsenic is a key environmental toxicant having significant impacts on human health. Millions of people in developing countries such as Bangladesh, Mexico, Taiwan, and India are affected by arsenic contamination through groundwater. Environmental contamination of arsenic leads to leads to various types of cancers, coronary and neurological ailments in human. There are several sources of arsenic exposure such as drinking water, diet, wood preservatives, smoking, air and cosmetics, while, drinking water is the most explored route. Inorganic arsenic exhibits higher levels of toxicity compared its organic forms. Exposure to inorganic arsenic is known to cause major neurological effects such as cytotoxicity, chromosomal aberration, damage to cellular DNA and genotoxicity. On the other hand, long-term exposure to arsenic may cause neurobehavioral effects in the juvenile stage, which may have detrimental effects in the later stages of life. Thus, it is important to understand the toxicology and underlying molecular mechanism of arsenic which will help to mitigate its detrimental effects. The present review focuses on the epidemiology, and the toxic mechanisms responsible for arsenic induced neurobehavioral diseases, including strategies for its management from water, community and household premises. The review also provides a critical analysis of epigenetic and transgenerational modifications, mitochondrial oxidative stress, molecular mechanisms of arsenic-induced oxidative stress, and neuronal dysfunction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".