Acute arsenic treatment alters cytochrome P450 expression and arachidonic acid metabolism in lung, liver and kidney of C57Bl/6 mice
Bibliographic record
Abstract
1. Arsenic (As(III)) toxicity has received increasing attention as human exposure to arsenic is associated with pulmonary, hepatic and renal toxicities. Therefore, in the present study, we investigated the effect of acute As(III) treatment on pulmonary, hepatic and renal cytochrome (CYP) P450-mediated arachidonic acid metabolism. 2. Our results demonstrated that acute As(III) treatment (12.5 mg/kg) altered CYP epoxygenases, CYP ω-hydroxylases and EPHX2 mRNA levels that were isozyme and tissue specific. 3. Furthermore, As(III) increased the formation of epoxyeicosatrienoic acids (EETs) in the kidney without affecting their levels in the lung or liver. In addition, acute As(III) treatment increased dihydroxyeicosatrienoic acid (DHETs) formation in the lung, while it did not affect liver DHETs formation and decreased kidney DHETs formation. 4. As(III) also increased total epoxygenases activity in the lung while it decreased its levels in the kidney and had no effect on the liver. Furthermore, As(III) increased 20-hydroxyeicosatetraenoic acid formation in the liver while it decreased its formation in the kidney. 5. Lastly, As(III) increased soluble epoxide hydrolase activity in the lung, while it decreased its levels in the kidney and had no effect on the liver. In conclusion, this is the first demonstration that As(III) alters arachidonic acid metabolism in a tissue specific manner.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".