Therapeutic Effect of IVIG on Inflammatory Arthritis in Mice Is Dependent on the Fc Portion and Independent of Sialylation or Basophils
Bibliographic record
Abstract
High-dose i.v. Ig (IVIG) is used to treat various autoimmune and inflammatory diseases; however, the mechanism of action remains unclear. Based on the K/BxN serum transfer arthritis model in mice, IVIG suppression of inflammation has been attributed to a mechanism involving basophils and the binding of highly sialylated IgG Fc to DC-SIGN-expressing myeloid cells. The requirement for sialylation was examined in the collagen Ab-induced arthritis (CAbIA) and K/BxN serum transfer arthritis models in mice. High-dose IVIG (1-2 g/kg body weight) suppressed inflammatory arthritis when given prophylactically. The same doses were also effective in the CAbIA model when given subsequent to disease induction. In this therapeutic CAbIA model, the anti-inflammatory effect of IVIG was dependent on IgG Fc but not F(ab')2 fragments. Removal of sialic acid residues by neuraminidase had no impact on the anti-inflammatory activity of IVIG or Fc fragments. Treatment of mice with basophil-depleting mAbs did not abrogate the suppression of either CAbIA or K/BxN arthritis by IVIG. Our data confirm the therapeutic benefit of IVIG and IgG Fc in Ab-induced arthritis but fail to support the significance of sialylation and basophil involvement in the mechanism of action of IVIG therapy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".