Effects of Anti-PD-L1 or IL-15/IL-15Rα-Fc Complex Treatment on CD4-independent CD8+ T Cell-Mediated Skin Allograft Rejection 3401
Bibliographic record
Abstract
Abstract Description Purpose CD8+ T cells, without CD4+ T cell help, independently reject skin but not other allografts for unknown reasons. This study determined the effects of T cell exhaustion and anergy on graft-infiltrating CD8+ T cells in CD4-depleted skin transplant recipients. Methods C57BL/6J mice were transplanted with MHC-mismatched skin allografts, depleted of CD4+ T cells, and treated with anti-PD-L1 or IL-15/IL-15Rα-Fc complex. Skin allografts were harvested at the time of rejection and analyzed by flow cytometry. Results Alloprimed effector CD8+ T cells comprised up to 40% of graft-infiltrating cells in CD4-deficient conditions. Treatment with either anti-PD-L1 or IL-15/IL-15Rα-Fc complex enhanced the proportion of graft-infiltrating CD62LloCD44hi CD8+T cells (78% vs 51%; p < 0.03 and 68% vs 51%; p < 0.05, respectively). The majority of graft-infiltrating CD8+ T cells expressed TNFα or granzyme B and treatment with anti-PD-L1, but not IL-15/IL-15Rα-Fc complex, enhanced the proportion of TNFα+ graft-infiltrating CD8+ T cells (42% vs 32%; p < 0.05). IL-15/IL-15Rα-Fc complex and anti-PD-L1 treatment reduced the expression of exhaustion markers Tim-3 (18% vs 30%; p < 0.004) and LAG-3 (19% vs 34%; p < 0.02), respectively, but neither treatment accelerated skin allograft rejection. Conclusions Although the aforementioned treatments enhance the proportion of non-exhausted effector CD4-independent CD8+T cells in skin allografts, these treatments did not accelerate skin rejection kinetics. Funding Sources Acknowledgements: Steven Elzein, MD is a Burroughs Wellcome Fund Fellow supported by a Burroughs Wellcome Fund Physician Scientist Institutional Award to the Texas A&M University Academy of Physician Scientists. Topic Categories Transplantation Immunology (TRAN)
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".