Epitope specificity and isotype of monoclonal anti-D antibodies dictate their ability to inhibit phagocytosis of opsonized platelets
Bibliographic record
Abstract
Rh immune globulin (WinRho SDF; Cangene, Mississauga, ON, Canada) is an effective treatment for autoimmune thrombocytopenic purpura; however, maintaining a sustained supply for its use in autoimmune thrombocytopenic purpura and its primary indication, hemolytic disease of the newborn, makes the development of alternative reagents desirable. We compared Rh immune globulin and 6 human monoclonal anti-D antibodies (MoAnti-D) with differing isotypes and specificities for their ability to opsonize erythrocytes and inhibit platelet phagocytosis in an in vitro assay. Results demonstrated that opsonization of erythrocytes with Rh immune globulin significantly (P < .001) reduced phagocytosis of fluorescently labeled opsonized platelets in an Fc-dependent manner. Of the MoAnti-D that shared specificity but differed in isotype, only IgG3 antibodies could significantly (P < .001) inhibit platelet phagocytosis. In contrast, 2 MoAnti-D shared isotypes and differed in specificity; however, only one could significantly (P < .001) inhibit platelet phagocytosis. The results suggest that MoAnti-D epitope specificity and isotypes are critical requirements for optimal inhibition of opsonized platelet phagocytosis.
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 | 0.000 | 0.000 |
| 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".