7<sup>th</sup>International Immunoglobulin Conference: Mechanisms of Action
Bibliographic record
Abstract
Although intravenous immunoglobulin (IVIg) is widely used for replacement therapy in immunodeficiencies and to treat autoimmune and inflammatory diseases, its mechanisms of action are not fully understood. Examination of immunoglobulin (Ig) receptors, including the Fc-gamma receptors (FCγRs) and the neonatal Fc receptor, have revealed genetic variations that are linked to autoimmune diseases and to the efficacy of IVIg treatment. However, the beneficial effect of IVIg encompasses multiple mechanisms of action. One of these is scavenging of activated complement fragments, such as C3a, C5a, C3b and C4b, by infused Ig molecules. This interaction prevents binding of complement fragments to their receptors on target cells, thus attenuating the immune damage. Additionally, anti-inflammatory effects may be facilitated by IgA via specific receptors and/or complement scavenging. Glycosylation of both the Fc- and Fab-fragments has also been implicated in the anti-inflammatory action of IVIg. Although there is evidence to support a role for sialylated IgG glycovariants in mediating the effect of IVIg, evidence from animal models of inflammatory disease suggest that sialylation may not be a critical factor. However, an increase in IgG glycosylation has been observed following IVIg treatment in Guillain-Barré syndrome patients, and this has been associated with improved clinical outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.032 |
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".