Intravenous Transfusion of BCR-Activated B Cells Protects NOD Mice from Type 1 Diabetes in an IL-10-Dependent Manner
Bibliographic record
Abstract
Although B cells play a pathogenic role in the initiation of type 1 diabetes (T1D) in NOD mice, it is not known whether activated B cells can maintain tolerance and transfer protection from T1D. In this study, we demonstrate that i.v. transfusion of BCR-stimulated NOD spleen B cells into NOD mice starting at 5-6 wk of age both delays onset and reduces the incidence of T1D, whereas treatment initiated at 9 wk of age only delays onset of T1D. This BCR-activated B cell-induced protection from T1D requires IL-10 production by B cells, as transfusion of activated B cells from NOD.IL-10(-/-) mice does not confer protection from T1D. Consistent with this result, severe insulitis was observed in the islets of NOD recipients of transfused NOD.IL-10(-/-) BCR-stimulated B cells but not in the islets of NOD recipients of transfused BCR-stimulated NOD B cells. The therapeutic effect of transfused activated NOD B cells correlates closely with the observed decreased islet inflammation, reduced IFN-gamma production and increased production of IL-4 and IL-10 by splenocytes and CD4(+) T cells from NOD recipients of BCR-stimulated NOD B cells relative to splenocytes and CD4(+) T cells from PBS-treated control NOD mice. Our data demonstrate that transfused BCR-stimulated B cells can maintain long-term tolerance and protect NOD mice from T1D by an IL-10-dependent mechanism, and raise the possibility that i.v. transfusion of autologous IL-10-producing BCR-activated B cells may be used therapeutically to protect human subjects at risk for T1D.
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 | 0.000 | 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".