Contribution of PPARγ in modulation of acrolein-induced inflammatory signaling in gp91<sup><i>phox</i></sup> knock-out mice
Bibliographic record
Abstract
Oxidative stress and inflammation are major contributors to acrolein toxicity. Peroxisome proliferator activated receptor gamma (PPARγ) has antioxidant and anti-inflammatory effects. We investigated the contribution of PPARγ ligand GW1929 to the attenuation of oxidative stress in acrolein-induced insult. Male gp91phox knock-out (KO) mice were treated with acrolein (0.5 mg·(kg body mass)–1 by intraperitoneal injection for 7 days) with or without GW1929 (GW; 0.5 mg·(kg body mass)–1·day–1, orally, for 10 days). The livers were processed for further analyses. Acrolein significantly increased 8-isoprostane and reduced PPARγ activity (P < 0.05) in the wild type (WT) and KO mice. GW1929 reduced 8-isoprostane (by 32% and 40% in WT and KO mice, respectively) and increased PPARγ activity (by 81% and 92% in WT and KO, respectively). Chemokine activity was increased (by 63%) in acrolein-treated WT mice, and was reduced by GW1929 (by 65%). KO mice exhibited higher xanthine oxidase (XO). Acrolein increased XO and COX in WT mice and XO in KO mice. GW1929 significantly reduced COX in WT and KO mice and reduced XO in KO mice. Acrolein significantly reduced the total antioxidant status in WT and KO mice (P < 0.05), which was improved by GW1929 (by 75% and 74%). The levels of NF-κB were higher in acrolein-treated WT mice. GW1929 reduced NF-κB levels (by 51%) in KO mice. Acrolein increased CD36 in KO mice (by 43%), which was blunted with GW1929. Data confirms that the generation of free radicals by acrolein is mainly through NAD(P)H, but other oxygenates play a role too. GW1929 may alleviate the toxicity of acrolein by attenuating NF-κB, COX, and CD36.
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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 | 0.001 |
| 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) | 0.003 | 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".