Intravenous immunoglobulin G selectively inhibits IL-1α-induced neutrophil–endothelial cell adhesion
Bibliographic record
Abstract
OBJECTIVES: Intravenous immunoglobulin (IVIG) G at high doses has therapeutic benefits in a variety of autoimmune and inflammatory disorders. The mechanism by which IVIG modulates inflammation is incompletely understood. We tested the hypothesis that IVIG modulates inflammation by inhibiting interactions between neutrophils and vascular endothelium, required for leukocyte recruitment to inflamed tissues. METHODS: The adhesion of human blood neutrophils to resting or cytokine-activated human umbilical vein endothelial cells (HUVECs) was measured, and the effect of IVIG or normal donor sera added at various stages was determined. RESULTS: IVIG completely inhibited neutrophil adhesion to endothelium stimulated with interleukin-1 (IL-1α), when it was present during the endothelial stimulation phase. IVIG had no effect on adhesion when IL-1β or TNF-α was the activating cytokine. The plasma of some (one of five) healthy donors also selectively blocked the IL-1α activation of the endothelium for supporting adhesion, and this was due to the presence of neutralizing IgG at high levels in the blood of the donor. CONCLUSIONS: Thus, IgG antibodies to IL-1α are present in IVIG at a biologically significant level, which can prevent endothelial activation. However, IVIG does not directly affect activation of endothelium or neutrophil adhesion mechanisms. The anti-inflammatory properties of IVIG may in part be related to blocking IL-1α-dependent leukocyte recruitment. Potentially, such antibodies may also have immunoregulatory effects by binding and neutralizing membrane-bound IL-1α during cell-cell interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".