Arsenic: Various species with different effects on cytochrome P450 regulation in humans
Bibliographic record
Abstract
) molecules, a multitude of more complex organic species are biologically produced through a process of metabolic transformation with biomethylation being the core of this process. Because of their differential toxicity, speciation of arsenic-based compounds is necessary for assessing health risks posed by exposure to individual species or co-exposure to several species. In this regard, exposure assessment is another pivotal factor that includes identification of the potential sources as well as routes of exposure. Identification of arsenic impact on different physiological organ systems, through understanding its behavior in the human body that leads to homeostatic derangements, is the key for developing strategies to mitigate its toxicity. Metabolic machinery is one of the sophisticated body systems targeted by arsenic. The prominent role of cytochrome P450 enzymes (CYPs) in the metabolism of both endobiotics and xenobiotics necessitates paying a great deal of attention to the possible effects of arsenic compounds on this superfamily of enzymes. Here we highlight the toxicologically relevant arsenic species with a detailed description of the different environmental sources as well as the possible routes of human exposure to these species. We also summarize the reported findings of experimental investigations evaluating the influence of various arsenicals on different members of CYP superfamily using human-based models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".