Prediction of Red Blood Cell Antibody Significance Using the Monocyte-Macrophage Assay
Bibliographic record
Abstract
Derived from monocytes in the bone marrow, macrophages are large, innate immune cells that play a major role in clearing dead cells, debris, tumor cells, and foreign pathogens. The phagocytic capacity of monocytes versus macrophages is a concept that is not well understood. Here, we aim to examine a difference in the phagocytosis of monocytes versus macrophages, specifically M1/M2 macrophages, against various opsonized red cells using a modified and updated version of the established monocyte monolayer assay (MMA). Peripheral blood mononuclear cells (PBMCs) were isolated from donor buffy coats. Using purified monocytes, inflammatory M1 and anti-inflammatory M2 macrophages were produced by in vitro culture and polarization. M1/M2 cells were harvested and used in an MMA-like assay, which we refer to as the M-MA, to decipher clinically significant phagocytosis of various red cell antibodies. A phagocytic index (PI) > 5 was deemed clinically significant phagocytosis with the use of monocytes. A phagocytic index (PI) > 12 was deemed clinically significant phagocytosis with the use of M1/M2 macrophages. M2 macrophages demonstrate an increased ability to phagocytose opsonized RBCs compared to monocytes and M1s. The same weak antibody (anti-S) yields significant phagocytosis with only M2 macrophages (PI=43) but not M1s (PI=2) or monocytes (PI=0), and this was demonstrated repeatedly using various antibodies. The use of M2 macrophages instead of monocytes may allow for more accurate results as these cells are more phagocytic, offering further clinical relevance to the assay. Further studies with different antibodies to red blood cells, including validation of the monocyte-macrophage assay (M-MA) with antibodies having known clinical significance, may show the M-MA more useful to help predict clinically significant red cell alloantibodies and transfusion reactions. This method will advance the field of transfusion medicine and immunology.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".