Direct addition of EDTA to LABScreen single antigen bead suspension: A simple way to prevent complement mediated interference and streamline HLA antibody testing
Bibliographic record
Abstract
Compromised detection of HLA specific antibodies due to complement mediated interference (CMI) is a well-recognized limitation of the single antigen bead (SAB) assay. Serum treatment with EDTA prior to SAB assay testing is a common strategy used to prevent CMI, however, treatment of individual sera, especially in large clinical runs, can extend assay turnaround time and increase the risk of a sample mix-up. In this study, we describe a simplified EDTA treatment strategy that can be applied simultaneously to all sera in a testing run. This strategy effectively prevents CMI without added pipetting steps and the increased turnaround time associated with other strategies. In the novel bead treatment (BT) method, EDTA solution and SAB suspension are combined and added into the wells of a testing tray together, patient sera are then added to the SAB/EDTA mixture. This eliminates the separate EDTA pre-treatment step used in the standard procedure (ST). Parallel testing using the BT, ST, and no EDTA treatment strategies followed by antibody identification using the Rapid Optimized SAB (ROB) protocol was performed for 19 well characterized sera with known CMI at two different laboratories. Both BT and ST methods were equally effective in preventing CMI. In addition, excellent MFI correlation was observed for specificities not affected by CMI. Both negative and pooled positive control sera performed as expected using the BT method and the pooled positive control serum, specifically designed to exhibit CMI, served well as an EDTA treatment control for all sera. The modified BT protocol can be easily implemented for clinical testing and eliminates extra pipetting steps, reduces the likelihood of pipetting error, and decreases turnaround time, while effectively preventing CMI and ensuring accurate detection of HLA antibodies. This protocol was recently implemented in the Halifax HLA Laboratory and has been very positively received by the laboratory team.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 0.002 |
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".