Optimized automated data analysis for the cytokinesis‐block micronucleus assay using imaging flow cytometry for high throughput radiation biodosimetry
Bibliographic record
Abstract
The cytokinesis-block micronucleus (CBMN) assay is a well-established technique that can be employed in triage radiation biodosimetry to estimate whole body doses of radiation to potentially exposed individuals through quantitation of the frequency of micronuclei (MN) in binucleated lymphocyte cells (BNCs). The assay has been partially automated using traditional microscope-based methods and most recently has been modified for application on the ImageStream(X) (IS(X) ) imaging flow cytometer. This modification has allowed for a similar number of BNCs to be automatically scored as compared to traditional microscopy in a much shorter time period. However, the MN frequency measured was much lower than both manual and automated slide-based methods of performing the assay. This work describes the optimized analysis template which implements newly developed functions in the IDEAS(®) data analysis software for the IS(X) that enhances specificity for BNCs and increases the frequency of scored MN. A new dose response calibration curve is presented in which the average rate of MN per BNC is of similar magnitude to those presented in the literature using automated CBMN slide scoring methods. In addition, dose estimates were generated for nine irradiated, blinded samples and were found to be within ±0.5 Gy of the delivered dose. Results demonstrate that the improved identification accuracy for MN and BNCs in the IS(X) -based version of the CBMN assay will translate to increased accuracy when estimating unknown radiation doses received by exposed individuals following large-scale radiological or nuclear emergencies. © 2016 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".