7<sup>th</sup>International Immunoglobulin Conference: Mechanisms of Action
Bibliographic record
Abstract
The mechanism of action by which therapeutic administration of intravenous immunoglobulin (IVIg) is able to provide a beneficial effect in autoimmune and inflammatory diseases is not yet fully understood, but current research is providing some answers. Signalling via receptors that interact with immunoglobulin (Ig) is crucial, and genetic polymorphisms of the Fc receptors have clear links to disease and also appear to influence the outcome of IVIg treatment. Glycosylation of the IgG, Fc- or Fab-fragments has a role in enhancing or blocking the pro- and anti-inflammatory effector functions. In addition, and independently of Fc receptors and glycosylation, Fc fragment and the constant domain of the Fab fragment contain binding sites for activated complement fragments that mediate complement-scavenging based immunomodulation. Although IgG Fc sialylation may not be critical for IVIg activity, research in some diseases suggests that it is associated with improved clinical outcomes. Therefore, further investigation of how IgG and IgA receptor expression and regulation affects the outcome of IVIg treatment may further clarify the mechanisms behind IVIg, and provide valuable guidance for future treatment paradigms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.064 | 0.029 |
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".