Bibliographic record
Abstract
Peroxisome proliferator-activated receptor, or PPAR, refers to a diverse and fascinating collection of receptor proteins in the human body that are involved in a myriad of complex functions pertaining to metabolism and inflammation.Interestingly, PPARs are highly expressed in the liver (among other organs), and as a class, they can affect gene expression by binding to particular sequences in the DNA.Because PPARs are involved with lipid and glucose processing and inflammatory pathways, they will be obvious drug targets in many hepatic conditions, including metabolic dysfunction-associated steatotic liver disease (MASLD), primary sclerosing cholangitis (PSC), and primary biliary cholangitis (PBC).Several PPAR agonists are in development, including alpha agonists that target lipid genesis, gamma agonists that influence glucose metabolism and insulin sensitivity, and delta agonists that regulate the fatty acid cycle and fat metabolism.During this past decade, and especially this year, exciting developments using PPAR agonists have been reported in the hepatology and general medicine literature, and at international meetings.PPAR agonists such as bezafibrate, fenofibrate, elafibranor, saroglitazar, and seladelpar, have all
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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.030 | 0.033 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".