Synergy between paclitaxel plus an exogenous methyl donor in the suppression of murine demyelinating diseases
Bibliographic record
Abstract
Progressive demyelination in multiple sclerosis (MS) reflects the negative balance between myelin damage and repair due to physical and molecular barriers, such as astrocytic glial scars, between oligodendrocytes and target neurons. In this paper, we show that combination therapy with paclitaxel (Taxol) plus the universal methyl-donor, vitamin B12CN (B12CN), dramatically limits progressive demyelination, and enhances remyelination in several independent, immune and nonimmune, in vivo and in vitro model systems. Combination therapy significantly reduced clinical signs of EAE in SJL mice, as well as the spontaneously demyelinating ND4 transgenic mouse. Astrocytosis was normalised in parallel to ultrastructural and biochemical evidence of remyelination. The combination therapy suppressed T cell expansion, reduced IFN-gamma, while enhancing IFN-beta and STAT-1 expression, STAT-1 phosphorylation and methylation of STAT-1 and MBP in the brain. Paclitaxel/B12CN has nearly identical effects to the previously described combination of IFN-beta/ B12CN, whose clinical usefulness is transient because of IFN-neutralising antibodies, not observed (or expected) with the present drug combination. This report provides a mechanistic foundation for the development of a new therapeutic strategy in humans with MS.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".