Anti‐CD52 blocks EAE independent of PD‐1 signals and promotes repopulation dominated by double‐negative T cells and newly generated T and B cells
Bibliographic record
Abstract
Lymphocyte depletion using anti-CD52 antibody effectively reduces relapses of multiple sclerosis (MS). To begin to understand what mechanisms might control this outcome, we examined the effect of a murine-CD52-specific mAb on the depletion and repopulation of immune cells in mice with experimental autoimmune encephalomyelitis (EAE), a model of MS. We tested whether the tolerance-promoting receptor programmed cell death protein-1 (PD-1) is required for disease remission post anti-CD52, and found that PD-1-deficient mice with a more severe EAE were nevertheless effectively treated with anti-CD52. Anti-CD52 increased the proportions of newly generated T cells and double-negative (DN) T cells while reducing newly generated B cells; the latter effect being associated with a higher expression of CD52 by these cells. In the longer term, anti-CD52 caused substantial increases in the proportion of newly generated lymphocytes and DN T cells in mice with EAE. Thus, the rapid repopulation of lymphocytes from central lymphoid organs post anti-CD52 may limit further disease. Furthermore, these data identify DN T cells, a subset with immunoregulatory potential, as a significant hyperrepopulating subset following CD52-mediated depletion.
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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) | 0.001 | 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".