Analysis of erythroid maturation in the nonlysed bone marrow with help of radar plots facilitates detection of flow cytometric aberrations in myelodysplastic syndromes
Bibliographic record
Abstract
BACKGROUND: Accumulating data support the role of flow cytometry (FCM) in diagnostic work-up of myelodysplastic syndromes (MDS). Changes in erythropoiesis are less documented than in granulopoiesis. However, most studies were performed on bone marrow samples (BMSs) after red blood cell lysis. We have established a FCM protocol for erythropoiesis, following a no-lysis approach and live gate acquisition of nucleated cells using DNA dye DRAQ5. METHODS: The ERY tube consisted of CD36, CD71, CD105, CD117, CD13, and CD45. Comparison with cytomorphological differential counts was carried out in a learning cohort of 80 BMS. To detect aberrations, we analyzed 208 BMS from 135 patients and five normal donors, divided into three cohorts: MDS (n = 68), nonclonal cytopenia (n = 43), and normal controls (n = 29). Radar plot (RP) was created for an overview of normal and aberrant patterns. RESULTS: The proportion of erythropoiesis in the ERY tube showed better agreement with the cytomorphology, compared to FCM panels on lysed BMS. We confirmed that aberrations in coefficient of variation (CV) of CD36 fluorescence intensity (p < .001), mean fluorescence intensity of CD36 (p = .012), and CV of CD105 (p < .001) can distinguish between MDS and nonclonal cytopenia. RP facilitated evaluation of erythropoietic maturation patterns and aberrant patterns were identified in 85% of MDS patients. CONCLUSION: This study provides evidence that a no-lysis approach and RP analysis allow a more reliable evaluation of erythropoiesis and erythroid dysplasia, supporting the integration of FCM erythroid panels in the standard work-up of MDS.
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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 | 0.002 | 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) | 0.002 | 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".