IL-10-dependent Tr1 cells attenuate astrocyte activation and ameliorate chronic central nervous system inflammation
Bibliographic record
Abstract
See Winger and Zamvil (doi: 10.1093/brain/aww121 ) for a scientific commentary on this article. The innate immune system plays a central role in the chronic central nervous system inflammation that drives neurological disability in progressive forms of multiple sclerosis, for which there are no effective treatments. The mucosal immune system is a unique tolerogenic organ that provides a physiological approach for the induction of regulatory T cells. Here we report that nasal administration of CD3-specific antibody ameliorates disease in a progressive animal model of multiple sclerosis. This effect is IL-10-dependent and is mediated by the induction of regulatory T cells that share a similar transcriptional profile to Tr1 regulatory cells and that suppress the astrocyte inflammatory transcriptional program. Treatment results in an attenuated inflammatory milieu in the central nervous system, decreased microglia activation, reduced recruitment of peripheral monocytes, stabilization of the blood–brain barrier and less neurodegeneration. These findings suggest a new therapeutic approach for the treatment of progressive forms of multiple sclerosis and potentially other types of chronic central nervous system inflammation. See Winger and Zamvil (doi: 10.1093/brain/aww121 ) for a scientific commentary on this article. Current therapies have limited effect on the chronic CNS inflammation observed in progressive multiple sclerosis (MS). Mayo et al. show that CD3-specific antibody ameliorates disease in a mouse model of progressive MS. The effect is dependent on induction of regulatory T-cells, which attenuate astrocyte and microglia activation via secretion of interleukin-10.
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) | 0.001 | 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 | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".