The role of para‐aminophenol in acetaminophen‐induced methemoglobinemia in dogs and cats
Bibliographic record
Abstract
Acetaminophen (APAP) overdose in most species is associated with hepatotoxicity because of the metabolite N-acetyl-p-benzoquinoneimine (NAPQI). In dogs and cats, APAP overdose primarily causes methemoglobinemia and hemolysis. Although NAPQI has been proposed as the responsible intermediate in dogs and cats, it lacks chemical or pharmacokinetic characteristics that favor methemoglobin formation. We hypothesized that para-aminophenol (PAP) rather than NAPQI induces methemoglobinemia and that deficient arylamine N-acetyltransferase (NAT) activity in dogs and cats contributes to this species-dependent methemoglobinemia. Erythrocytes from dogs, cats, mice, and rats were exposed in vitro to APAP, NAPQI, and PAP. Only PAP induced methemoglobin and it induced more methemoglobin formation in dog and cat than rat and mouse erythrocytes. PAP also induced more methemoglobin in erythrocytes from Nat1/Nat2 knockout mice than wildtype (WT) mouse erythrocytes (P < 0.05), but less than in dog and cat erythrocytes (P < 0.01). APAP and PAP toxicity were compared in vivo in WT and Nat1/Nat2 knockout mice. APAP caused no hematotoxicity while PAP induced more methemoglobin in NAT1/NAT2 knockout mice than in WT mice (P < 0.05). These results support the hypothesis that PAP is the metabolite responsible for APAP-induced methemoglobinemia and that deficient NAT activity in dogs and cats contributes to this species-dependent toxicity.
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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 | 0.001 | 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) | 0.001 | 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".