TNF-alpha but not IL-1alpha are Correlated with PGE<sub>1</sub>-Dependent Protection Against Acute D-Galactosamine-Induced Liver Injury
Bibliographic record
Abstract
BACKGROUND: Prostaglandin E1 (PGE1) treatment of humans and rodents during acute hepatic failure ameliorates different parameters of hepatic dysfunction. PURPOSE: To investigate whether prevention of acute liver injury induced by D-galactosamine (D-GalN) with preadministration of PGE1 is correlated with a change in the concentration of two proinflammatory cytokines, as tumour necrosis factor-alpha (TNF-alpha) and interleukin (IL)-1alpha, and/or nitrite+nitrate (NOx), as nitric oxide-related end products in serum. RESULTS: D-GalN significantly increased alanine aminotransferase (ALT) and TNF-alpha concentration in serum 5 and 10 mins, respectively, after treatment compared with the control group (P< or =0.05). D-GalN did not change the IL-1alpha concentration at any time during the study. Preadministration of PGE1 to D-GalN-treated rats significantly reduced the ALT content and increased significantly the TNF-alpha concentration in serum 1, 2.5, 5 and 10 mins after D-GalN treatment compared with the D-GalN group (P< or =0.05). Nitric oxide was not involved in either the toxic effect due to D-GalN or the protection observed with PGE1 against D-GalN toxicity. CONCLUSIONS: Acute liver injury induced by D-GalN is correlated with an increased TNF-alpha release. Preadministration of PGE1 to D-GalN-treated rats exerted a priming effect on inflammatory cells to release enhanced levels of TNF-alpha but not IL-1alpha. These findings indicate that stimulation of TNF-alpha release may be involved in the acute D-GalN-induced liver injury and also in PGE1 protection from hepatotoxicity in clinical and experimental studies.
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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 | 0.001 |
| 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".