Risk beyond neutropenia: insights into neutrophil migration from newly diagnosed AML until late after allogeneic stem cell transplantation
Bibliographic record
Abstract
Quantification of neutrophil counts is the most relevant assessment of cellular immunity in clinical practice. Patients with neutropenia are considered at risk and are categorized according to its severity. The incidence of febrile neutropenia varies, but patients with acute myeloid leukemia are traditionally considered at high risk, especially following myelotoxic treatments. To provide additional functional parameters, we investigated the ex vivo migration properties and morphology of neutrophils in 10 patients with acute myeloid leukemia using single-cell video-microscopy and discovered, in addition to neutropenia, highly pathological neutrophil migration patterns and polarization defects in patients with untreated acute myeloid leukemia. Neutrophil speed was the most sensitive parameter and significantly lower at leukemia diagnosis (9.067 vs 15.810 µm/min, P = 0.0025) compared to healthy controls (n = 46). Hematological remission was associated with improved neutrophil migration profiles, but these ultimately normalized only after hematopoietic cell transplantation. Five patients were followed up for long-term effects of hematopoietic cell transplantation for up to 24 mo. This is the first longitudinal ex vivo neutrophil migration study in patients with acute myeloid leukemia, followed by allogeneic hematopoietic cell transplantation. It identified functional neutrophil impairments beyond routine quantitative assessments, adding to the well-known quantitative impairment of neutropenia. HCT can reestablish functional neutrophils with healthy migration profiles in these patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".