Health Effects Associated With Pre- and Perinatal Exposure to Arsenic
Bibliographic record
Abstract
Inorganic arsenic is a well-established human carcinogen, able to induce genetic and epigenetic alterations. More than 200 million people worldwide are exposed to arsenic concentrations in drinking water exceeding the recommended WHO threshold (10μg/l). Additionally, chronic exposure to levels below this threshold is known to result in long-term health effects in humans. The arsenic-related health effects in humans are associated with its biotransformation process, whereby the resulting metabolites can induce molecular damage that accumulates over time. The effects derived from these alterations include genomic instability associated with oxidative damage, alteration of gene expression (including coding and non-coding RNAs), global and localized epigenetic reprogramming, and histone posttranslational modifications. These alterations directly affect molecular pathways involved in the onset and progression of many conditions that can arise even decades after the exposure occurs. Importantly, arsenic metabolites generated during its biotransformation can also pass through the placental barrier, resulting in fetal exposure to this carcinogen at similar levels to those of the mother. As such, more immediate effects of the arsenic-induced molecular damage can be observed as detrimental effects on fetal development, pregnancy, and birth outcomes. In this review, we focus on the genetic and epigenetic damage associated with exposure to low levels of arsenic, particularly those affecting early developmental stages. We also present how these alterations occurring during early life can impact the development of certain diseases in adult life.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".