Targeting Adhesion Molecules as a Potential Mechanism of Action for Intravenous Immunoglobulin
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIg) therapy has been shown to have therapeutic benefit in more than 50 inflammatory and immune-related diseases; however, the potential benefit of IVIg in cardiovascular disease is more limited, in part because our understanding of the mechanisms underlying the effects of IVIg in innate immunity is incomplete. METHODS AND RESULTS: In this study, a systematic assessment of the role of IVIg in leukocyte recruitment was completed with an in vitro flow-chamber system and in vivo intravital microscopy in a feline ischemia-reperfusion model system. IVIg treatment of blood resulted in a profound decrease in recruitment of either immobilized P-selectin or E-selectin due to direct effects of IVIg on the leukocyte (not substratum). Similar results were observed on endothelium treated with histamine, which induces P-selectin-dependent rolling and beta2-integrin-dependent adhesion. IVIg reduced P-selectin glycoprotein ligand-1 (PSGL-1) antibody binding to PSGL-1 on leukocytes. Use of a beta2-integrin-dependent static assay to bypass selectin-dependent recruitment revealed some inhibitory effectiveness (60%), which suggests that the majority of the effects of IVIg were due to selectin inhibition, with some inhibition of integrin function. In vivo intravital microscopy revealed a potent inhibitory effect of IVIg on P-selectin-dependent rolling and beta2-integrin-dependent adhesion that led to reduced leukocyte recruitment and vascular dysfunction in postischemic microvessels. CONCLUSIONS: Our data demonstrate that IVIg has direct inhibitory effects on leukocyte recruitment in vitro and in vivo through inhibition of selectin and integrin function.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".