Modulation of Cardiac and Hepatic Cytochrome P450 Enzymes During Heart Failure
Bibliographic record
Abstract
Heart failure is a very serious cardiovascular disease that affects more than five million people in North America. The role of cytochrome P450 (CYP) in cardiovascular health and disease is well established. Many CYP enzymes have been identified in the heart and their levels have been reported to be altered during cardiac hypertrophy and heart failure. There is a great deal of discrepancy between various reports on CYP alterations during heart failure, likely due to differences in disease severity, species in question and other underlying conditions. In general, however, cardiac CYP1B and CYP2A, CYP2B, CYP2E, CYP2J, CYP4A and CYP11 mRNA levels and related enzyme activities are usually increased. Moreover, there is a strong correlation between CYP-mediated endogenous metabolites and the pathogenesis of cardiac hypertrophy and heart failure. Some of these metabolites confer cardioprotective effect such as estradiol, dehydroepiandrosterone, epoxyeicosatrienoic acids, and prostaglandin I(2); whereas, other metabolites may be harmful to the heart such as androgens, aldosterone, hydroxyeicosatetraenoic acids, and thromboxane A(2). On the other hand, heart failure plays an important role in the down-regulation of hepatic CYP involved in drug metabolism through several mechanisms which include hepatocellular damage, hypoxia, elevated levels of pro-inflammatory cytokines, and increased production of heme oxygenase-1. Therefore, more research is needed to elucidate the mechanisms by which CYP affect the development and/or progression of heart failure and also the mechanism by which heart failure alters cardiac and hepatic CYP enzymes.
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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".