A Role for IL-1Receptor Antagonist in the Acute Therapeutic Effects of IVIg?.
Bibliographic record
Abstract
Abstract It has been suggested that IVIg and anti-D may function through the regulation of various cytokines; in particular, significant increases in the anti-inflammatory compound interleukin-1 receptor antagonist (IL-1Ra) have been reported after exposure of patients to IVIg or anti-D. However, the possible role that IL-1Ra may play in the acute therapeutic effects of IVIg or anti-D is unknown. Using a murine model of ITP in which IVIg and anti-RBC-specific antibodies are therapeutically effective, we observed that mice injected with therapeutic doses of IVIg and one anti-RBC antibody (TER-119, which mimics the effects of anti-D) demonstrated increased serum levels of IL-1Ra, reaching peak levels at a time which coincided with an increase in platelet count. Surprisingly, another RBC-specific antibody which also reverses ITP, failed to increase IL-production above basal levels. Thrombocytopenic mice lacking the IL-1 receptor are known to be completely unresponsive to IL-1Ra. These mice responded to both IVIg and TER-119 as successfully as wild-type mice. Injection of mice with as much as 1 mg of recombinant IL-1Ra did not significantly ameliorate thrombocytopenia. To further study the possible indirect role of the IL-1Ra system in the amelioration of murine ITP, we next looked at nitric oxide (NO), an immunoregulatory compound that can be induced by IL-1, stimulate increases in IL-1Ra levels, and has been previously demonstrated to be modulated by IVIg. We report here that treatment of mice with aminoguanidine, an iNOS-selective inhibitor or with L-NAME, a multi-spectrum NOS inhibitor, did not significantly affect the ability of IVIg to ameliorate ITP. These data suggest that while IVIg and TER-119 may mediate the release of IL-1Ra, it is not required for IVIg or anti-RBC antibodies to exert their acute therapeutic effects.
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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) | 0.000 | 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) | 0.001 | 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".