Adult neural stem cells expressing IL-10 confer potent immunomodulation and remyelination in experimental autoimmune encephalitis
Post-publication record
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Bibliographic record
Abstract
Adult neural stem cells (aNSCs) derived from the subventricular zone of the brain show therapeutic effects in EAE, an animal model of the chronic inflammatory neurodegenerative disease MS; however, the beneficial effects are modest. One critical weakness of aNSC therapy may be an insufficient antiinflammatory effect. Here, we demonstrate that i.v. or i.c.v. injection of aNSCs engineered to secrete IL-10 (IL-10-aNSCs), a potent immunoregulatory cytokine, induced more profound functional and pathological recovery from ongoing EAE than that with control aNSCs. IL-10-aNSCs exhibited enhanced antiinflammatory effects in the periphery and inflammatory foci in the CNS compared with control aNSCs, more effectively reducing myelin damage, a hallmark of MS. When compared with mice treated with control aNSCs, those treated with IL-10-aNSCs demonstrated differentiation of transplanted cells into greater numbers of oligodendrocytes and neurons but fewer astrocytes, thus enhancing exogenous remyelination and neuron/axonal growth. Finally, IL-10-aNSCs converted a hostile environment to one supportive of neurons/oligodendrocytes, thereby promoting endogenous remyelination. Thus, aNSCs engineered to express IL-10 show enhanced ability to induce immune suppression, remyelination, and neuronal repair and may represent a novel approach that can substantially improve the efficacy of neural stem cell-based therapy in EAE/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".