GM-CSF and IL-4 are not involved in IVIG-mediated amelioration of ITP in mice: a role for IL-11 cannot be ruled out
Bibliographic record
Abstract
Summary Previously, we have reported that interleukin (IL)-4, granulocyte–macrophage colony-stimulating factor (GM-CSF), and IL-11, but not IL-33, are up-regulated in two strains of mice with immune thrombocytopenia (ITP) that are responsive to intravenous immunoglobulin (IVIg) treatment. Previously, IL-4 was ruled out in the mechanism of IVIg; however, other publications have suggested this cytokine as a major player in the mechanism of IVIg action. Thus, we sought to further investigate a role for IL-4 and, in addition, GM-CSF and IL-11 in the mechanism of action of IVIg using a murine model of ITP. A passive platelet antibody model was used to generate ITP in IL-4 receptor knock-out (IL-4R–/–), IL-11 receptor knock-out (IL-11Rα–/–) and GM-CSF knock-out (Csf2–/–) mice. We also used a neutralizing antibody to IL-11 and recombinant human IL-11 (rhIL-11) in addition to depleting basophils in vivo to study the effect of IVIg to ameliorate ITP. Our results showed that basophils, IL-4 and GM-CSF were unimportant in both ITP induction and its amelioration by IVIg. The role of IL-11 in these processes was less clear. Even though IL-11Rα–/– mice with ITP responded to IVIg similarly to wild-type (WT) mice, treatment of ITP WT mice with rhIL-11 instead of IVIg showed an increase in platelet numbers and WT mice administered anti-IL-11 showed a significant reduction in the ability of IVIg to ameliorate the ITP. Our findings indicate that neither IL-4, basophils or GM-CSF have roles in IVIg amelioration of ITP; however, a role for IL-11 requires further study.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".