Nuclear Receptors as Therapeutic Targets for Neurodegenerative Diseases: Lost in Translation
Bibliographic record
Abstract
Neurodegenerative diseases are characterized by a progressive loss of neurons that leads to a broad range of disabilities, including severe cognitive decline and motor impairment, for which there are no effective therapies. Several lines of evidence support a putative therapeutic role of nuclear receptors (NRs) in these types of disorders. NRs are ligand-activated transcription factors that regulate the expression of a wide range of genes linked to metabolism and inflammation. Although the activation of NRs in animal models of neurodegenerative disease exhibits promising results, the translation of this strategy to clinical practice has been unsuccessful. In this review we discuss the role of NRs in neurodegenerative diseases in light of preclinical and clinical studies, as well as new findings derived from the analysis of transcriptomic databases from humans and animal models. We discuss the failure in the translation of NR-based therapeutic approaches and consider alternative and novel research avenues in the development of effective therapies for neurodegenerative diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".