IVIg-induced anti-inflammatory monocytes (CD14 low, HLA-DR high) with a low CD80 and high PDL1 expression inhibits T cell activation in allogeneic mixed lymphocytes reactions (P2194)
Bibliographic record
Abstract
Abstract Intravenous immunoglobulin (IVIg) is a therapeutic preparation of human IgG isolated from plasma donations and has been proposed as a therapy to improve the rate of graft survival in patients with a high risk for antibody-mediated-rejection. Blocking of anti-HLA antibodies by anti-idiotypic IgG present in IVIg was proposed to explain the rapid effect of IVIg. However, a long-term reduction of anti-HLA alloreactivity in IVIg-treated patients suggests that IVIg modulates the functions of immune cells. In the present study, we showed using the allogeneic mixed lymphocyte reaction (MLR) as an in vitro model of allograft rejection and GvHD that IVIg strongly inhibits IL-2 secretion (T cell activation) and modulates the level of other pro- and anti-inflammatory cytokines secretion. To determine the mechanisms underlying the inhibition of T cell activation in MLR, we studied the effect of IVIg on the phenotype of the cells involved in MLR. Our results revealed that MLR inhibition by IVIg correlates with the induction of anti-inflammatory monocytes (CD14 low, HLA-DR high) with a low CD80 and high PDL1 expression. To evaluate the importance of PDL1 on the MLR inhibition, anti-PDL1 was added during the MLR. Blocking of PDL1 restored the MLR, as evaluated by IL-2 secretion. We thus propose that PDL1 plays a central role in the inhibition of MLR. Our results help to better understand how IVIg induces long-term peripheral tolerance and improves graft survival in transplanted patients.
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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) | 0.001 | 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".