Application of an imaging flow cytometry γ-H2AX assay for biodosimetry using supervised machine learning
Bibliographic record
Abstract
PURPOSE: Phosphorylation of the histone H2AX (γ-H2AX) is a rapid response to radiation-induced DNA double strand breaks (DSBs) and is a good biomarker for exposure to ionizing radiation. The signal has traditionally been detected by microscopy (spot counting) or by flow cytometry (fluorescent intensity). An imaging flow cytometry (IFC) method has been developed, which combines the high resolution of microscopy with the statistical power of flow cytometry methods to measure γ-H2AX in human lymphocytes. MATERIALS AND METHODS: The assay was optimized and validated for both sample acquisition and data analysis, in the dose range of 0-10 Gy. For data analysis, mean fluorescence intensity (MFI), spot count (foci per cell), and average area of the spots were used with the supervised machine learning (SML) K-Nearest Neighbors (K-NN) algorithm to estimate doses. These dose estimates were compared to the traditional flow cytometry method of estimating doses from an MFI-based dose response curve. RESULTS: A statistical analysis of both methodologies showed that SML K-NN method was able to determine the dose delivered to blind, irradiated samples more accurately than when using a linear fit of the MFI response alone, especially in the 7-10 Gy dose range. CONCLUSIONS: The efficiency of the γ-H2AX-IFC assay, 1 hour post-exposure, has been improved and validated using the SML K-NN methodology for dose estimation. This study could help establish the γ-H2AX assay as a triage tool for the rapid screening of a large number of samples.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".