The nuclear receptor PPARγ as a therapeutic target for cerebrovascular and brain dysfunction in Alzheimer's disease
Bibliographic record
Abstract
Peroxisome proliferator-activated receptors (PPARs) are ligand-activated nuclear transcription factors that regulate peripheral lipid and glucose metabolism. Three subtypes make up the PPAR family (alpha, gamma, beta/delta), and synthetic ligands for PPARalpha (fibrates) and PPARgamma (Thiazolidinediones, TZDs) are currently prescribed for the respective management of dyslipidemia and type 2 diabetes. In contrast to the well characterized action of PPARs in the periphery, little was known about the presence or function of these receptors in the brain and cerebral vasculature until fairly recently. Indeed, research in the last decade has uncovered these receptors in most brain cell types, and has shown that their activation, particularly that of PPARgamma, is implicated in normal brain and cerebrovascular physiology, and confers protection under pathological conditions. Notably, accumulating evidence has highlighted the therapeutic potential of PPARgamma ligands in the treatment of brain disorders such as Alzheimer's disease (AD), leading to the testing of the TZDs pioglitazone and rosiglitazone in AD clinical trials. This review will focus on the benefits of PPARgamma agonists for vascular, neuronal and glial networks, and assess the value of these compounds as future AD therapeutics in light of evidence from transgenic mouse models and recent clinical trials.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".