Monoclonal antibodies that mimic the action of anti-D in the amelioration of murine ITP act by a mechanism distinct from that of IVIg
Bibliographic record
Abstract
The mechanism of action of intravenous immunoglobulin (IVIg) and polyclonal anti-D-mediated reversal of immune thrombocytopenia (ITP) is still unclear. However, in a murine model of ITP, the therapeutic effect of IVIg appears to be wholly dependent upon the expression of the inhibitory Fc receptor, Fc gamma RIIB. We previously demonstrated that, similar to anti-D in humans, 2 erythrocyte-reactive monoclonal antibodies (TER119 and M1/69) ameliorated murine ITP and inhibited reticuloendothelial system (RES) function at doses that protected against thrombocytopenia. The current study evaluated the involvement of the inhibitory and activating Fc receptors, Fc gamma RIIB and Fc gamma RIIIA, respectively, in the TER119 and M1/69-mediated inhibition of thrombocytopenia. In contrast to IVIg, in Fc gamma RIIB-deficient mice, both monoclonal antibodies ameliorated ITP and both significantly down-regulated the level of expression of the activating Fc gamma RIIIA in splenic macrophages. These results indicate that anti-erythrocyte antibodies that ameliorate ITP act independently of Fc gamma RIIB expression but are dependent upon the activating Fc gamma RIIIA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".