Targeting a different mechanism for treatment of demyelinating disease using vitamin B12 in combination therapy
Bibliographic record
Abstract
The process of demyelination involves complex, multifactorial mechanisms. Our ND4 transgenic mouse model, an over-expressor of the proteolipid protein (PLP) alternatively-pliced variant DM20 which exhibits spontaneous demyelination, was used for this study. Paclitaxel was recently demonstrated to be an effective agent in combating demyelination within this mouse model. IFN-β treatment, a current widely employed therapeutic agent, was used for comparison. In this study, Vitamin B12 was used in combination with paclitaxel as a novel treatment to combat demyelination, resulting in more effective and sustained attenuation of clinical signs. Changes to HPLC profiles of myelin basic protein (MBP) were observed after different therapies, indicating modifications to MBP. Combination treatments with Vitamin B 12 resulted in changes of MBP methylation, which were more similar to patterns exhibited by the normal mice. This indicates a potential role of Vitamin B12 in promoting remyelination or protective of further degradation by enhancing myelin stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".