Cytochrome P450 1B1 is critical in the development of TNF-α, IL-6, and LPS-induced cellular hypertrophy
Bibliographic record
Abstract
Heart failure (HF) is preceded by cellular hypertrophy (CeH) which alters expression of cytochrome P450 enzymes (CYPs) and arachidonic acid (AA) metabolism. Inflammation is involved in CeH pathophysiology, but mechanisms remain elusive. This study investigates the impacts of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and lipopolysaccharides (LPS) on the development of CeH and the role of CYP1B1. AC16 cells were treated with TNF-α, IL-6, and LPS in the presence and absence of CYP1B1-siRNA or resveratrol. mRNA and protein expression levels of CYP1B1 and hypertrophic markers were determined using PCR and Western blot analysis, respectively. CYP1B1 enzyme activity was determined, and AA metabolites were analyzed using liquid chromatography-tandem mass spectrometry. Our results show that TNF-α, IL-6, and LPS induce expression of hypertrophic markers, induce CYP1B1 expression, and enantioselectively modulate CYP1B1-mediated AA metabolism in favor of mid-chain HETEs. CYP1B1-siRNA or resveratrol ameliorated these effects. In conclusion, our results demonstrate the crucial role of CYP1B1 in TNF-α, IL-6, and LPS-induced CeH.
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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) | 0.002 | 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".