Bibliographic record
Abstract
PURPOSE OF REVIEW: Therapeutic strategies to alleviate the growing epidemic of insulin-resistant syndromes (obesity and type 2 diabetes) as well as the conferred cardiovascular disease risk remain sparse. The peroxisome proliferator-activated receptor δ (PPARδ) has emerged as a versatile regulator of lipid homeostasis and inflammatory signaling, making it an attractive therapeutic target for the treatment and prevention of type 2 diabetes and atherosclerosis. RECENT FINDINGS: PPARδ activation regulates lipid homeostasis and inflammatory signaling in a variety of cell types, conferring protection from metabolic disease and atherosclerosis. Specifically, PPARδ activation in the liver stimulates glucose utilization and inhibits gluconeogenesis, which improves insulin resistance and hyperglycemia. In macrophages, PPARδ-specific activation with synthetic agonists inhibits VLDL-induced triglyceride accumulation and inflammation. In mice, PPARδ agonists halt the progression of atherosclerosis and stabilize existing lesions by promoting an anti-inflammatory milieu within the diseased macrovasculature. In humans, PPARδ activation improves insulin sensitivity and reduces atherogenic dyslipidemia via a mechanism complementary to statin monotherapy. SUMMARY: Recent advances in the understanding of PPARδ reveal that activation of this receptor represents a multifaceted therapeutic strategy for the prevention and treatment of insulin-resistant syndromes and atherosclerosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".