Autoantibody-Induced Formation of Immune Complexes in Normal Human Serum
Bibliographic record
Abstract
Intravenous immunoglobulins (IVIg) are concentrated preparations of purified human plasma-derived IgG routinely used in the treatment of many autoimmune diseases. Their precise mechanisms of therapeutic action have remained unclear in most diseases and are attracting much interest due to the rapidly increasing use of this precious plasma-derived product. The presence in IVIg of IgG reactive with various human structures has been known for many years. In this review, we first briefly discuss the formation and role of natural autoreactive human IgG in healthy individuals. A role for IgG autoantibodies in the in vivo immunomodulatory effects of IVIg has been proposed several years ago but the underlying mechanism has remained unclear. Recent work has shown that the large IVIg doses infused in many patients could oversaturate the normal anti-idiotype-dependent inhibition of autoreactive IgG present in the plasma of all healthy individuals since the formation of autoimmune complexes could be observed in normal serum in presence of therapeutic amounts of IVIg. These autoimmune complexes could have potent in vivo immunomodulatory effects by interacting with various IgG and complement receptors. Furthermore the autoreactive IgG can be easily purified from IVIg by affinity chromatography, raising the interesting possibility of further fractionating IVIg into different sub-products for use in the treatment of different diseases. These results indicate that natural autoantibodies have important in vivo roles not only for the protection of the body against infectious agents but also for the efficiency of passive immunotherapy procedures used in the treatment of many diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".