Stimulating PD-1???negative signals concurrent with blocking CD154 co-stimulation induces long-term islet allograft survival1
Bibliographic record
Abstract
BACKGROUND: A balanced network of positive and negative T-cell co-stimulatory signals is important in regulating T-cell activation. Blocking CD28, CD154 (CD40L), or both co-stimulatory molecules has been efficacious in preventing acute allograft rejection in certain but not all transplantation models. In the present study, the authors tested the hypothesis that stimulating programmed death 1 (PD-1)-triggered negative signals concurrent with blocking CD154 co-stimulatory signals would facilitate islet allograft tolerance. METHODS: The authors used a dimeric PD-L1 immunoglobulin (Ig) fusion protein to stimulate the inhibitory receptor PD-1, and a monoclonal antibody to block CD154. The effects of PD-1 engagement and CD154 blockade on lymphocyte activation were determined by cell proliferation, flow cytometry, and a model of islet transplantation. RESULTS: PD-L1Ig inhibited the proliferation of both CD4+ and CD8+ T cells stimulated by anti-CD3. The inhibitory effect of PD-L1Ig was enhanced by concurrent blockade of CD154 co-stimulatory signals, as demonstrated by T-cell proliferation and expression of cell surface activation markers. PD-L1Ig and anti-CD154 also synergistically blocked the activation and maturation of antigen-presenting cells. In an islet transplantation model, treatment of recipient C57BL/6 (H-2b) mice with PD-L1Ig and anti-CD154 induced long-term survival of DBA/2 (H-2d) islet allografts, whereas treatment with each reagent alone failed to prevent islet allograft rejection. CONCLUSIONS: These results suggest that engaging the negative receptor PD-1 exhibits critical immunoregulatory effects in the allograft response, and blocking positive co-stimulatory molecules with active delivery of inhibitory signals may represent a novel therapeutic strategy in transplantation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".