MiR-219a-5p Enriched Extracellular Vesicles Induce OPC Differentiation and EAE Improvement More Efficiently Than Liposomes and Polymeric Nanoparticles
Bibliographic record
Abstract
Remyelination is a key aspect in multiple sclerosis pathology and a special effort is being made to promote it. However, there is still no available treatment to regenerate myelin and several strategies are being scrutinized. Myelination is naturally performed by oligodendrocytes and microRNAs have been postulated as a promising tool to induce oligodendrocyte precursor cell differentiation and therefore remyelination. Herein, DSPC liposomes and PLGA nanoparticles were studied for miR-219a-5p encapsulation, release and remyelination promotion. In parallel, they were compared with biologically engineered extracellular vesicles overexpressing miR-219a-5p. Interestingly, extracellular vesicles showed the highest oligodendrocyte precursor cell differentiation levels and were more effective than liposomes and polymeric nanoparticles crossing the blood-brain barrier. Finally, extracellular vesicles were able to improve EAE animal model clinical evolution. Our results indicate that the use of extracellular vesicles as miR-219a-5p delivery system can be a feasible and promising strategy to induce remyelination in multiple sclerosis patients.
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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 | 0.000 |
| 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".