Environmental arsenic exposure: From genetic susceptibility to pathogenesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
More than 200 million people in 70 countries are exposed to arsenic through drinking water. Chronic exposure to this metalloid has been associated with the onset of many diseases, including cancer. Epidemiological evidence supports its carcinogenic potential, however, detailed molecular mechanisms remain to be elucidated. Despite the global magnitude of this problem, not all individuals face the same risk. Susceptibility to the toxic effects of arsenic is influenced by alterations in genes involved in arsenic metabolism, as well as biological factors, such as age, gender and nutrition. Moreover, chronic arsenic exposure results in several genotoxic and epigenetic alterations tightly associated with the arsenic biotransformation process, resulting in an increased cancer risk. In this review, we: 1) review the roles of inter-individual DNA-level variations influencing the susceptibility to arsenic-induced carcinogenesis; 2) discuss the contribution of arsenic biotransformation to cancer initiation; 3) provide insights into emerging research areas and the challenges in the field; and 4) compile a resource of publicly available arsenic-related DNA-level variations, transcriptome and methylation data. Understanding the molecular mechanisms of arsenic exposure and its subsequent health effects will support efforts to reduce the worldwide health burden and encourage the development of strategies for managing arsenic-related diseases in the era of personalized medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.019 |
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 it