From genotype to phenotype: decoding mutations in blasts by holo-tomographic flow cytometry
Why this work is in the frame
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Bibliographic record
Abstract
Cup-like nuclear morphological alterations in acute myeloid leukemia (AML) blasts have been widely correlated with Nucleophosmin 1 (NPM1) mutations. NPM1-mutated AML has earned recognition as a distinct entity among myeloid tumors, but the absence of a thoroughly established tool for its morphological analysis remains a notable gap. Holographic tomography (HT) can offer a label-free solution for quantitatively assessing the 3D shape of the nucleus based on the volumetric variations of its refractive indices (RIs). However, traditional HT methods analyze adherent cells in a 2D layer, leading to non-isotropic reconstructions due to missing cone artifacts. Here we show for the first time that holo-tomographic flow cytometry (HTFC) achieves quantitative specificity and precise capture of the nucleus volumetric shape in AML cells in suspension. To retrieve nucleus specificity in label-free RI tomograms of flowing AML cells, we conceive and demonstrate in a real-world clinical case a novel strategy for segmenting 3D concave nuclei. This method implies that the correlation between the "phenotype" and "genotype" of nuclei is demonstrated through HTFC by creating a challenging link not yet explored between the aberrant morphological features of AML nuclei and NPM1 mutations. We conduct an ensemble-level statistical characterization of NPM1-wild type and NPM1-mutated blasts to discern their complex morphological and biophysical variances. Our findings suggest that characterizing cup-like nuclei in NPM1-related AML cells by HTFC may enhance the diagnostic approach for these tumors. Furthermore, we integrate virtual reality to provide an immersive fruition of morphological changes in AML cells within a true 3D environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it