Intravenous (IV) anti‐D and IV immunoglobulin achieve acute platelet increases by different mechanisms: modulation of cytokine and platelet responses to IV anti‐D by Fc<i>γ</i>RIIa and Fc<i>γ</i>RIIIa polymorphisms
Bibliographic record
Abstract
Intravenous (IV) anti-D and IV immunoglobulin (IVIG) slow the Fcgamma receptor (FcgammaR)-mediated destruction of antibody-coated platelets in patients with immune thrombocytopenic purpura (ITP). This pilot study explored the mechanism of these immunoglobulin preparations by measuring interleukin-10 (IL-10), monocyte chemoattractant protein-1 (MCP-1), IL-6 and tumour necrosis factor alpha (TNFalpha), before and after infusion and by assessing the effect of FcgammaRIIa and FcgammaRIIIa polymorphisms on both cytokine and haematologic responses to anti-D. Following IVIG, only IL-10 was increased at 2 h and MCP-1 on day 7 (P < 0.05). In contrast, 2 h after anti-D infusion, plasma levels of all four cytokines were increased (P < 0.01); five of six patients with the highest MCP-1, IL-6 and TNFalpha levels had chills. Higher IL-10 levels correlated with platelet increases at 24 h and haemoglobin decreases at day 7 (P < 0.025). Patients with the FcgammaRIIa-131HH genotype had significantly higher MCP-1, IL-6 and TNFalpha levels. Patients with the FcgammaRIIIa-158VF genotype had higher platelet increments at day 7 (P < 0.05). Soluble CD16 (sCD16) was increased 2 h after IV anti-D; day 7 levels correlated with day 7 haemoglobin decreases (P < 0.01). In conclusion, the relationship of FcgammaRIIa and FcgammaRIIIa polymorphisms with both cytokine levels and platelet increments implicated these receptors in responses to anti-D and supported different mechanisms of FcgammaR interaction to those seen with IVIG.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".