IVIg decrease antigen-dependent T cell activation by reducing the FcγR-dependent antigen presentation ability of APC (78.36)
Bibliographic record
Abstract
Abstract Intravenous immunoglobulins (IVIg) have been shown to have anti-inflammatory effects in a variety of autoimmune diseases, but their mechanisms of action remain unclear. In autoimmune diseases, activated self-reactive T cells invade specific tissues and activate local APC which in turn present autoantigens on their surface, leading to T cell restimulation in a positive feedback loop. Although it was shown that IVIg could reduce T cell activation and modify their cytokine secretion pattern, it is still not clear whether this effect occurs following a direct interaction of IVIg with T cells or through an interference of IVIg with the ability of APC to present autoantigens. To address this question, we used an in vitro antigen presentation system using ovalbumin as model antigen, to study the effects of IVIg on antigen-mediated T cell activation. The results obtained showed that IVIg inhibited T cell activation but that this effect was the indirect consequence of a reduction in the antigen presentation ability of APC. We further showed that this inhibitory effect was not due to modulation of expression of MHC II or CD80/86 costimulatory molecules on the surface of APC and was independent of inhibitory FcγRIIb. Finally, we showed that F(ab’)2 fragments of IVIg had no effect on antigen-mediated T cell activation, suggesting that IVIg must interact with activating FcγRs to mediate their inhibitory effect.
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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) | 0.000 | 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 | 0.000 |
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".