Intravenous immunoglobulins (IVIg) inhibit antigen presentation in vitro via an FcgammaR‐independent mechanism.
Bibliographic record
Abstract
Intravenous immunoglobulins (IVIg) are used to treat a variety of autoimmune diseases, but their mechanism of therapeutic action remains unclear. It has been observed that IVIg could reduce autoantibody titers in treated patients, suggesting that IVIg may inhibit the ongoing and future production of pathogenic autoantibodies. Several studies also suggested that the anti‐inflammatory effects of IVIg were mediated by either inhibitory or activating Fc gamma receptors (FcγRs). In the present work, we have studied the possibility that IVIg interfere with the process of antigen presentation by APC to T cells which is essential for antibody production in a sustained immune response, and whether this effect could be FcγRs‐dependent. Using an in vitro antigen presentation assay with different types of murine APC, we observed that IVIg inhibited OVA presentation by more than 50%. Surprisingly, IVIg still significantly inhibited OVA presentation by dendritic cells isolated from mice deficient in either inhibitory FcγRIIb or activating FcγRIII (gamma chain deficient), suggesting that IVIg inhibit antigen presentation by an FcγR‐independent mechanism. In addition, we showed that this inhibition may not be dependent on a modulation of expression of co‐stimulatory molecules such as CD80, CD86 or MHC‐II expression. We are currently investigating other mechanisms by which IVIg could interfere with antigen presentation.
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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) | 0.001 | 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".