Nuclear Receptors and the Control of Metabolism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The metabolic nuclear receptors act as metabolic and toxicological sensors, enabling the organism to quickly adapt to environmental changes by inducing the appropriate metabolic genes and pathways. Ligands for these metabolic receptors are compounds from dietary origin, intermediates in metabolic pathways, drugs, or other environmental factors that, unlike classical nuclear receptor ligands, are present in high concentrations. Metabolic receptors are master regulators integrating the homeostatic control of (a) energy and glucose metabolism through peroxisome proliferator-activated receptor gamma (PPARgamma); (b) fatty acid, triglyceride, and lipoprotein metabolism via PPARalpha, beta/delta, and gamma; (c) reverse cholesterol transport and cholesterol absorption through the liver X receptors (LXRs) and liver receptor homolog-1 (LRH-1); (d) bile acid metabolism through the farnesol X receptor (FXR), LXRs, LRH-1; and (e) the defense against xeno- and endobiotics by the pregnane X receptor/steroid and xenobiotic receptor (PXR/SXR). The transcriptional control of these metabolic circuits requires coordination between these metabolic receptors and other transcription factors and coregulators. Altered signaling by this subset of receptors, either through chronic ligand excess or genetic factors, may cause an imbalance in these homeostatic circuits and contribute to the pathogenesis of common metabolic diseases such as obesity, insulin resistance and type 2 diabetes, hyperlipidemia and atherosclerosis, and gallbladder disease. Further studies should exploit the fact that many of these nuclear receptors are designed to respond to small molecules and turn them into therapeutic targets for the treatment of these disorders.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it