Monitoring recovery after CNS demyelination, a novel tool to de-risk pro-remyelinating strategies
Bibliographic record
Abstract
In multiple sclerosis, while remarkable progress has been accomplished to control the inflammatory component of the disease, repair of demyelinated lesions is still an unmet need. Despite encouraging results generated in experimental models, several candidates favouring or promoting remyelination have not reached the expected outcomes in clinical trials. One possible reason for these failures is that, in most cases, during preclinical testing, efficacy was evaluated on histology only, while functional recovery had not been assessed. We have generated a Xenopus laevis transgenic model Tg(mbp:GFP-NTR) of conditional demyelination in which spontaneous remyelination can be accelerated using candidate molecules. Xenopus laevis is a classic model for in vivo studies of myelination because tadpoles are translucent. We reasoned that demyelination should translate into loss of sensorimotor functions followed by behavioural recovery upon remyelination. To this end, we measured the swimming speed and distance travelled before and after demyelination and during the ongoing spontaneous remyelination and have developed a functional assay based on the visual avoidance of a virtual collision. Here we show that alteration of these functional and clinical performances correlated well with the level of demyelination and that histological remyelination, assayed by counting in vivo the number of myelinating oligodendrocytes in the optic nerve, translated in clinical-functional recovery. This method was further validated in tadpoles treated with pro-remyelinating agents (clemastine, siponimod) showing that increased remyelination in the optic nerve was associated with functional improvement. Our data illustrate the potential interest of correlating histopathological parameters and functional-clinical parameters to screen molecules promoting remyelination in a simple in vivo model of conditional demyelination.
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How this classification was reachedexpand
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".