Use of CD157 in FLAER-based assays for high-sensitivity PNH granulocyte and PNH monocyte detection
Bibliographic record
Abstract
BACKGROUND: Recent Flow Cytometric guidelines to detect Paroxysmal Nocturnal Hemoglobinuria (PNH) in white blood cells recommend using FLAER-based assays to detect granulocytes and monocytes lacking expression of GPI-linked structures. However national proficiency testing results continue to suggest a need for improved testing algorithms, including the need to optimize diagnostic analytes in PNH. METHODS: CD157 is another GPI-linked structure expressed on both granulocytes and monocytes and here we assess its ability to replace CD24 and CD14 in predicate 4-color granulocyte and monocyte assays respectively. We also assess a single tube, 5-color combination of FLAER, CD157, CD64, CD15, and CD45 to simultaneously detect PNH clones in granulocyte and monocyte lineages. RESULTS: Delineation of PNH from normal phenotypes with 4- or 5-color CD157-based assays compared favorably with 4-color predicate methods and PNH clone size data were similar and highly correlated (R(2) >0.99) with predicate values over a range (0.06%-99.8%) of samples. Both CD157-based assays exhibited similar high levels of sensitivity and low background levels in normal samples. CONCLUSIONS: While CD157-based 4- and 5-color assays generated closely similar results to the predicate assays on a range of PNH and normal samples, the 5-color assay has significant advantages. Only a single 5-color WBC reagent cocktail is required to detect both PNH granulocytes and monocytes. Additionally, sample preparation and analysis time is reduced yielding significant efficiencies in technical resources and reagent costs. All 4- and 5-color reagent sets stained stabilized whole blood PNH preparations, used in external quality assurance programs.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".