Mechanism of Selenium-Induced Inhibition of Arsenic-Enhanced UVR Carcinogenesis in Mice
Bibliographic record
Abstract
BACKGROUND: Hairless mice that ingested arsenite in drinking water exhibited more than a 5-fold enhancement of ultraviolet radiation (UVR) carcinogenesis, whereas arsenite alone was carcinogenically inactive. Dietary organoselenium blocked the cancer enhancement effect of arsenic but not cancer induction by UVR. OBJECTIVE: In this study we sought to explain selenium blockage of As enhancement by establishing the extent that As and Se tissue distributions are coincident or divergent. METHODS: We used the X-ray fluorescence microprobe at the Advanced Photon Source (Argonne National Laboratory) to probe sections of skin and liver from hairless mice exposed to a) UVR, b) UVR + As, c) UVR + organoselenium, or d) UVR + As + organoselenium. RESULTS: We found elevated levels of As in the skin epithelium (hair follicles and epidermis) and diffusely in the liver of mice exposed to UVR + As. Arsenic was entirely absent in skin in mice exposed to UVR + As + organoselenium, but a diffuse low level was seen in the liver. As and Se locations were consistently divergent in skin; As was more diffusely distributed, whereas Se was strongly associated with membranes. X-ray absorption near-edge spectra are consistent with the presence of the seleno-bis(S-glutathionyl) arsinium ion in the liver. CONCLUSIONS: Supplemental Se was uncommonly effective at preventing even a trace of As in skin at 14 or 196 days of continuous exposure to As in drinking water. Traces of the seleno-bis(S-glutathionyl) arsinium ion in the liver suggested that formation of this compound was more likely to be responsible for the As-blocking effect of Se than was a mechanism based on antioxidation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".