Diagnosing PNH with FLAER and multiparameter flow cytometry
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Bibliographic record
Abstract
BACKGROUND: PNH is an acquired hematopoietic stem cell disorder leading to a partial or absolute deficiency of all glycophosphatidyl-inositol (GPI)-linked proteins. The classical approach to diagnosis of PNH by cytometry involves the loss of at least two GPI-linked antigens on RBCs and neutrophils. While flow assays are more sensitive and specific than complement-mediated lysis or the Hams test, they suffer from several drawbacks. Bacterial aerolysin binds to the GPI moiety of cell surface GPI-linked molecules and causes lysis of normal but not GPI-deficient PNH cells. FLAER is an Alexa488-labeled inactive variant of aerolysin that does not cause lysis of cells. Our goals were to develop a FLAER-based assay to diagnose and monitor patients with PNH and to improve detection of minor populations of PNH clones in other hematologic disorders. METHODS: In a single tube assay, we combined FLAER with CD45, CD33, and CD14 allowing the simultaneous analysis of FLAER and the GPI-linked CD14 structure on neutrophil and monocyte lineages. RESULTS: Comparison to standard CD55 and CD59 analysis showed excellent agreement. Because of the higher signal to noise ratio, the method shows increased sensitivity in our hands over single (CD55 or CD59) parameter analysis. Using this assay, we were able to detect as few as 1% PNH monocytes and neutrophils in aplastic anemia, that were otherwise undetectable using CD55 and CD59 on RBC's. We also observed abnormal FLAER staining of blast populations in acute leukemia. In these cases, the neutrophils stained normally with FLAER, while the gated CD33bright cells failed to express normal levels of CD14 and additionally showed aberrant CD45 staining and bound lower levels of FLAER. CONCLUSION: FLAER combined with multiparameter flow cytometry offers an improved assay for diagnosis and monitoring of PNH clones and may have utility in detection of unsuspected myeloproliferative disorders.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it