Selective retinoid X receptor agonism promotes functional recovery and myelin repair in experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
Evidence that myelin repair is crucial for functional recovery in multiple sclerosis (MS) led to the identification of bexarotene (BXT). This clinically promising remyelinating agent activates multiple nuclear hormone receptor subtypes implicated in myelin repair. However, BXT produces unacceptable hyperlipidemia. In contrast, IRX4204 selectively activates the retinoid X receptor (RXR). Given compelling links between RXR activation and increased myelin repair, we employed IRX4204 to investigate the impact of RXR agonism alone on functional recovery in mice subjected to experimental autoimmune encephalomyelitis (EAE). Since gait deficits are common in MS, we used machine learning to obtain highly sensitive and reliable measurements of sagittal hindleg joint movements for mice walking on a treadmill. IRX4204 not only blocked the progressive loss of knee and ankle movements but also reversed joint movement impairments in EAE mice. Our biochemical, transcriptional and histological measurements in spinal cord suggest these gait improvements reflect increased axon survival and remyelination and reduced inflammation. Using microglia, astrocytes and oligodendrocyte progenitor cells, we present additional data suggesting that IRX4204 may act on multiple glial subtypes to orchestrate myelin repair. These results inform the discovery of restorative neural therapeutics for MS by demonstrating that selective RXR agonism is sufficient for effective myelin repair. Moreover, our findings support the therapeutic potential of IRX4204 to promote functional recovery in MS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".