Daudi cell stroma: An alternative to dithiothreitol to resolve daratumumab interference in pretransfusion testing
Bibliographic record
Abstract
Background Treatment of red blood cells with dithiothreitol (DTT) or trypsin effectively denatures CD38; however, this treatment damages other antigens, some of which are of clinical importance. Thus, other avenues to deplete daratumumab (DARA) from plasma samples should be explored. Study Design and Methods The Daudi B‐cell line was found to express high levels of CD38 and was sonicated in a sonication buffer to achieve complete cell lysis. The resulting stroma preparation was centrifuged at 20 000 g for 20 minutes and then mixed with 250 μL of DARA–plasma and incubated for 10 minutes at 37°C. The stroma–DARA–plasma mixture was centrifuged again, and the supernatant was collected and subjected to four additional rounds of adsorption with fresh stroma. DARA‐depleted plasma was tested by gel indirect antiglobulin test (IAT). Results CD38 expression on Daudi cells was confirmed by flow cytometry. Gel IAT analysis showed that the incubation of plasma from DARA‐treated patients with Daudi cells stroma resulted in a significant depletion of DARA but allowing detection of other alloantibodies of interest such as anti‐K, anti‐Yt a , and anti‐Gy a . Conclusions Daudi cell stroma is inexpensive, easy to prepare in large batches, and can be used as an off‐the‐shelf reagent. Incubation of plasma from DARA‐treated patients with Daudi cell stroma can efficiently overcome DARA interference in serologic testing without affecting DTT‐ or trypsin‐sensitive antigens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".