Stimulating PD-1???negative signals concurrent with blocking CD154 co-stimulation induces long-term islet allograft survival1
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it