Distinctive Efficacy of IVIG in Ameliorating Thrombocytopenia Induced by Anti-Platelet GPIIbIIIa and GPIbα Antibodies.
Bibliographic record
Abstract
Abstract Idiopathic autoimmune thrombocytopenia (ITP) is an autoimmune disease characterized by autoantibody-mediated platelet destruction. The major platelet antigens that autoantibodies are directed against in ITP are GPIIbIIIa and GPIbα. Intravenous immunoglobulin G (IVIG) is a treatment used in ITP; however, some patients are refractory to this treatment and the mechanisms of action of IVIG in this disease are not fully understood. We tested the hypothesis that IVIG may not be equally effective in preventing ITP caused by GPIIbIIIa versus GPIbα anti-platelet antibodies. Thrombocytopenia was induced in Balb/c mice using three monoclonal antibodies against either mouse platelet GPIIbIIIa (JON1, 2, −3) or against GPIbα (p0p 3, 4, −5). Mouse platelet counts were performed daily from day one (pre-treatment) until day five using a flow cytometric assay. The efficacy of IVIG in inhibiting monoclonal antibody-induced platelet destruction in mice was assessed by intraperitoneal injection of IVIG (2 g/kg) 24-hours before anti-platelet antibody injections (7 mg/mouse, i.v.) on day two. We found that pre-treatment with IVIG resulted in a significantly higher platelet count on day three in all groups of anti-GPIIbIIIa-injected animals (n=3/antibody; for JON1, P=0.002; JON2, P=0.015; JON3, P=0.002) as compared to albumin-treated controls. In contrast, IVIG failed to prevent platelet clearance and thrombocytopenia on day three in two anti-GPIbα antibody-treated groups (p0p3 and p0p5, n=3/antibody), but did inhibit platelet destruction in mice that were injected with p0p4 antibody (P=0.0003). Our results indicate that the efficacy of IVIG treatment in preventing ITP induced by these anti-platelet antibodies is affected by their antigen specificities. These data also suggest that patients with ITP mediated by GPIbα may be less responsive to IVIG treatment than those with ITP mediated by GPIIbIIIa antibodies.
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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".