Can IVIg Preparations with Specificity for Soluble Antigens Mimic the Therapeutic Effects of IVIg in the Treatment of Immune Thrombocytopenia?.
Bibliographic record
Abstract
Abstract Intravenous immunoglobulin (IVIg) mediates protection from the effects of immune thrombocytopenic purpura (ITP). In addition, an IVIg specific for a cell-associated antigen (anti-D) is also able to increase platelet counts in D positive subjects with ITP. Whether an IVIg directed to a soluble antigen can likewise be beneficial in ITP is, however, unknown. A murine model of ITP was used to determine the effectiveness of IgG specific to soluble antigens in treating immune thrombocytopenia. Ovalbumin (OVA) was selected as the primary target antigen because it can be used in its soluble form or can be coupled to syngeneic red blood cells (OVA-RBC), and the same anti-OVA antibody could be used with both OVA and OVA-RBC. Mice were injected with either preformed soluble OVA + anti-OVA, OVA-RBC’s presensitised with anti-OVA, or appropriate control preparations, followed by an anti-platelet antibody to induce thrombocytopenia. Both experimental regimes, but none of the control preparations, protected mice from ITP. Similar to IVIg, soluble OVA + anti-OVA did not have any effect on thrombocytopenia in mice lacking the inhibitory receptor FcγRIIB (FcγRIIB−/−). In contrast, injection of anti-OVA sensitised OVA-RBC’s did ameliorate thrombocytopenia in FcγRIIB−/− mice. Finally, mice injected with IgG specific for the endogenous soluble antigens, albumin and transferrin, also inhibited ITP in an FcγRIIB-dependent manner. We conclude that IgG antibodies directed to soluble antigens can inhibit or reverse immune thrombocytopenia in an FcγRIIB-dependent manner.
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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".