Induction of PD-L1 on monocytes: A new mechanism by which IVIg inhibits mixed lymphocyte reactions
Bibliographic record
Abstract
Allograft rejection and graft-versus-host disease (GvHD) are frequent complications following solid organ or stem cell transplantation in which T cell activation plays a central role. Despite the development of new immunosuppressive drugs that improve the success rate of transplantation, allograft survival continues to be a challenge. Recently, intravenous immunoglobulin (IVIg) has been proposed as prophylaxis and post-transplant treatment to reduce acute rejection episodes. IVIg is a therapeutic agent that is known to down-modulate T cell functions in patients with autoimmune disorders. To test the hypothesis that this immunomodulatory effect could be beneficial in the context of transplantation, we used mixed lymphocyte reactions (MLR) as an in vitro model of allograft rejection and GvHD. Our results show that IVIg strongly inhibits the MLR as evaluated by IL-2 secretion, a well-known marker of T cell activation. IVIg also modulates the secretion of other pro-(IL-6, IFN-γ) and anti-inflammatory (IL-1RA) cytokines. More importantly, we show that IVIg induces monocytes with a CD80(low) PD-L1(high) phenotype and that blockade of PD-L1 partially abrogates the inhibitory effect of IVIg. We have thus identified a new mechanism by which IVIg inhibits T cell functions in the context of transplantation, supporting the potential usefulness of IVIg in the prevention or treatment of graft rejection and GvHD.
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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 | 0.001 | 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".