CD71 improves delineation of PNH type III, PNH type II, and normal immature RBCS in patients with paroxysmal nocturnal hemoglobinuria
Bibliographic record
Abstract
BACKGROUND: The diagnosis of paroxysmal nocturnal hemoglobinuria (PNH) relies on flow cytometric demonstration of loss of glycosyl-phosphatidyl inositol (GPI)-anchored proteins from red blood cells (RBC) and white blood cells (WBC). High-sensitivity multiparameter assays have been developed to detect loss of GPI-linked structures on PNH neutrophils and monocytes. High-sensitivity assays to detect PNH phenotypes in RBCs have also been developed that rely on the loss of GPI-linked CD59 on CD235a-gated mature RBCs. The latter is used to delineate PNH Type III (total loss of CD59) and PNH Type II RBCs (partial loss of CD59) from normal (Type I) RBCs. However, it is often very difficult to delineate these subsets, especially in patients with large PNH clones who continue to receive RBC transfusions, even while on eculizumab therapy. METHODS: We have added allophycocyanin (APC)-conjugated CD71 to the existing CD235aFITC/CD59PE RBC assay allowing simultaneous delineation and quantification of PNH Type III and Type II immature RBCs (iRBCs). RESULTS: We analyzed 24 medium to large-clone PNH samples (>10% PNH WBC clone size) for PNH Neutrophil, PNH Monocyte, Type III and Type II PNH iRBCs, and where possible, Type III and Type II PNH RBCs. The ability to delineate PNH Type III, Type II, and Type I iRBCs was more objective compared to that in mature RBCs. Additionally, total PNH iRBC clone sizes were very similar to PNH WBC clone sizes. CONCLUSIONS: Addition of CD71 significantly improves the ability to analyze PNH clone sizes in the RBC lineage, regardless of patient hemolytic and/or transfusion status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".