Absence of cytokine modulation following therapeutic infusion of intravenous immunoglobulin or anti‐red blood cell antibodies in a mouse model of immune thrombocytopenic purpura
Bibliographic record
Abstract
Human intravenous immunoglobulin (IVIg) and anti-D immunoglobulin preparations are used in the treatment of immune thrombocytopenic purpura (ITP). One mechanism proposed to explain their therapeutic effects in ITP patients is the induction of expression of anti-inflammatory cytokines, such as interleukin (IL)-10 or IL-1ra, leading to a reduction of phagocytic activity of the reticuloendothelial system. However, increased expression of pro-inflammatory cytokines was also noted following treatment of ITP patients, raising doubt on the actual contribution of anti-inflammatory cytokines in the therapeutic effects of IVIg and anti-D immunoglobulins. The present study evaluated the in vivo modulation of expression of a large array of inflammatory cytokines using a mouse model of thrombocytopenia. IVIg was not found to modulate cytokine expression although it efficiently prevented thrombocytopenia. In contrast, protective (M1/69) and non-protective (TER-119) anti-mouse red blood cell (RBC) antibodies (mimicking anti-D treatment) both increased the expression of CXCL-1 and CXCL-5. Thus, there was no relationship between inflammatory cytokine expression and prevention of thrombocytopenia by IVIg or anti-mouse RBC in the ITP mouse model. These results suggest that the increase in cytokine expression observed in ITP patients following IVIg or anti-D infusion is not required for their therapeutic effects but may rather represent a side-effect of the treatment.
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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 | 0.001 | 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".