High‐Sensitivity Detection of PNH Red Blood Cells, Red Cell Precursors, and White Blood Cells
Bibliographic record
Abstract
Flow cytometry is the method of choice to 'diagnose' paroxysmal nocturnal hemoglobinuria (PNH) and has led to improved patient management. Most laboratories have limited experience with PNH testing, and many different flow approaches are used. Careful selection and validation of antibody conjugates has allowed the development of reagent cocktails suitable for detection of PNH RBCs, CD71+ reticulocytes, and WBCs in clinical/sub-clinical PNH samples. A CD235a-FITC/CD59-PE assay was developed capable of detecting Type III PNH RBCs at 0.01% sensitivity. A protocol targeting immature CD71+ RBCs can detect PNH reticulocytes at similar sensitivity. Four-color FLAER-based neutrophil and monocyte assays were developed to detect PNH phenotypes at a level of 0.01% and 0.04% sensitivity, respectively. For instrumentation with five or more PMTs, a single-tube 5-color FLAER/CD157-based assay to simultaneously detect PNH neutrophils and monocytes is described. Using these standardized approaches, results have demonstrated good intra- and inter-laboratory performance characteristics even in laboratories with little prior experience performing PNH testing.
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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) | 0.000 | 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 | 0.000 | 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".