Agonists, inverse agonists, and antagonists as therapeutic approaches to manipulate retinoic acid-related orphan receptors
Bibliographic record
Abstract
Retinoic acid-related orphan receptors (RORs) serve as transcription factors that play a pivotal role in a myriad of physiological processes within the body. Their involvement extends to critical biological processes that confer protective effects in the heart, immune system, and nervous system, as well as contributing to the mitigation of several aggressive cancer types. These protective functions are attributed to ROR's regulation of key proteins and the management of various cellular processes, including autophagy, mitophagy, inflammation, oxidative stress, and glucose metabolism, highlighting the emerging need for pharmacological approaches to modulate ROR expression. Thus, the modulation of RORs is a rapidly growing area of research aimed not only at comprehending these receptors, but also at manipulating them to attain the desired physiological response. Despite the presence of natural ROR ligands, the development of synthetic agonists with high selectivity for these receptors holds substantial therapeutic potential. The exploration and advancement of such compounds can effectively target diseases associated with ROR dysregulation, thereby providing avenues for therapeutic interventions. Herein, we provide a comprehensive examination of the multifaceted role of ROR in diverse physiological and pathophysiological conditions, accompanied by an in-depth exploration of a spectrum of ROR agonists, inverse agonists, and antagonists.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 0.002 |
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".