Cytochrome P450 inactivation by serum from humans with a viral infection and serum from rabbits with a turpentine‐induced inflammation: the role of cytokines
Bibliographic record
Abstract
Serum from humans with an acute upper respiratory viral infection and from rabbits with turpentine-induced inflammation reduce the catalytic activity of hepatic cytochrome P450 (P450). The aim of this study was to identify the serum mediators responsible for the decrease in P450 activity. Rabbit and human sera were fractionated by size exclusion chromatography and the fractions tested for their ability to reduce the activity and amount of P450 after 4 h of incubation with hepatocytes from turpentine-treated rabbits (H(INF)). Rabbit and human sera decreased P450 activity by around 40% without any change in the amount of CYP1A1 and 1A2 apoproteins. In rabbit serum, the fraction containing proteins of M(r) 23-15 kDa decreased P450 content by 41%, but did not alter the amount of the apoproteins. Anti-IL-6 antibody added to the M(r) 23-15 kDa fraction restored P450 content to 97% of control values, while anti-IL-1beta, TNF-alpha and IFN-gamma antibodies had no effect. Supporting the role of IL-6, incubation of H(INF) in the presence of IL-6 for 4 h reduced P450 content by 40%. In human serum, the fraction containing proteins of M(r) >95 kDa lowered P450 content by 43% without modifying the amounts of CYP1A1/2. Neutralization experiments showed that IFN-gamma, IL-6, and IL-1beta contributed to the decrease in P450 content. In conclusion, the present results demonstrate that IL-6, and IFN-gamma, IL-6 and IL-1beta are the serum mediators released in vivo by a turpentine-induced inflammatory reaction in the rabbit and an upper respiratory viral infection in humans, respectively, inactivating hepatic P450.
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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 | 0.001 |
| 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) | 0.001 | 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".