Practical guidelines for the high‐sensitivity detection and monitoring of paroxysmal nocturnal hemoglobinuria clones by flow cytometry
Bibliographic record
Abstract
BACKGROUND: Paroxysmal nocturnal hemoglobinuria (PNH) is a life-threatening disorder caused by an inability to make glyco-phosphatidyl-inositol (GPI) anchors. While flow cytometry is the method of choice to detect the loss of GPI-linked proteins, the development and validation of sensitive, standardized, methodologies have been hampered by the rarity of this disease and by technical difficulties in the accurate identification of PNH cells. METHODS: Guidelines for the diagnosis and monitoring of PNH by flow cytometry were recently published by the International Clinical Cytometry Society (ICCS). However, specific reagent cocktails, and associated detailed analytic strategies were not directly addressed therein. In this supporting document based on the ICCS guidelines, we provide concise practical protocols for the high-sensitivity detection of PNH RBCs and WBCs (both granulocytes and monocytes). RESULTS: The CD235aFITC/CD59PE assay described was capable of detecting as few as 20 Type III PNH RBCs per million cells. Frequencies of Type III PNH cells in 10 normal samples were in the 0-6 per million RBCs. The high-resolution granulocyte/neutrophil assays described in this study could detect PNH phenotypes consistently at a level of 0.01% sensitivity. Frequencies of PNH phenotypes in normal individuals were in the 0-10 per million granulocytes/neutrophils range. CONCLUSIONS: The careful screening and selection of specific antibody conjugates has allowed the development of reagent cocktails suitable for high-sensitivity flow cytometric detection of PNH RBCs and PNH WBCs. The reagent cocktails described herein can be used on a variety of clinical flow cytometers equipped with four or more photo multiplier tubes.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.023 |
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".