Rhesus D Antigenic Determinants on Residual Red Blood Cells in Apheresis and Buffy Coat Platelet Concentrates
Bibliographic record
Abstract
BACKGROUND: The level of residual red blood cells (RBCs) in platelet concentrates (PCs) is of interest because of clinical concerns related to alloimmunization to RBC antigens in transfused patients. This work aims at characterizing and quantifying the levels of intact and fragmented RBCs in apheresis (AP-PCs) and buffy coat PCs (BC-PCs) to assess their potential risk for RhD antigen alloimmunization. METHODS: After staining with anti-CD41 (platelets) and anti-CD235a (RBCs) antibodies, the size and density of RhD antigen on intact and fragmented RBCs were analyzed by flow cytometry. RESULTS: /unit in BC-PCs, which correspond to about 3 and 11 µL of RBCs by product, respectively. RhD expression was about 4 times higher on RBC particles in AP-PCs, and these particles contribute to 66 and 75% of the total antigenic load in BC-PCs and AP-PCs, respectively. CONCLUSIONS: Processing methods influence the quantity and nature of contaminating residual RBCs and RBC-derived particles in PCs. The estimation of residual RBCs in these blood products is generally based on measurements of intact RBCs, which might underestimate the risk for alloim-munization in transfused patients. The question of whether these RBC-derived particles can produce an immune response and, thus, should then be taken into consideration for Rh immune prophylactic treatments, remains to be clarified.
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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 | 0.001 |
| 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".