Peroxisome proliferator activated receptors, inflammation, the vasculature and the heart
Bibliographic record
Abstract
Over the last dozen years since the discovery of the family of transcription factor termed peroxisome proliferator activated receptors (PPAR) [], an impressive number of studies have investigated the characteristics, ligands and functional roles as well as molecular mechanisms of PPARs. Although PPARs were formerly believed to regulate genes involved only in lipid and glucose metabolism, a large number of more recent studies have explored the role of PPARs in cell growth, cell migration as well as in inflammation. The function of PPARs in inflammation was first demonstrated by Devchand et al. [] who showed that pro-inflammatory eicosanoid leukotriene B4 binds to PPARa and induces transcription of genes involved in wand (3-oxidation. In this chapter, we briefly summarize the role of PPARs in inflammation generally, and discuss in greater detail the role of PPARs in the heart. We will discuss molecular, biochemical, physiological and pharmacological roles of PPARa and PPARy in the regulation of cardiac hypertrophy, inflammation and cardiac function, and introduce novel concepts relating to PPARs as transcription factors in the regulation of the expression of inflammatory response genes as mechanisms that participate in the pathophysiology of cardiac disease.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".