Multi‐parameter dose estimations in radiation biodosimetry using the automated cytokinesis‐block micronucleus assay with imaging flow cytometry
Bibliographic record
Abstract
The cytokinesis-block micronucleus (CBMN) assay is an established technique in radiation biological dosimetry for estimating the dose to an individual by measuring the frequency of micronuclei (MN) in binucleated lymphocyte cells (BNCs). The assay has been partially automated using slide-scoring algorithms, but an automated multiparameter method without the need of the slide-making procedure would be advantageous to further increase throughput for application in mass casualty events. The development of the ImageStreamX (ISX) imaging flow cytometer has made it possible to adapt the CBMN assay to an automated imaging flow cytometry (FCM) method. The protocol and analysis presented in this work tailor and expand the assay to a multiparameter biodosimetry tool. Ex vivo irradiated whole blood samples were cultured, processed, and analyzed on the ISX and BNCs, MN, and mononuclear cells were imaged, identified, and enumerated automatically and simultaneously. Details on development of the method, gating strategy, and dose response curves generated for the rate of MN per BNC, percentage of mononuclear cells as well as the replication index are presented. Results indicate that adapting the CBMN assay for use in imaging FCM has produced a rapid, robust, multiparameter analysis method with higher throughput than is currently available with standard microscopy. We conclude that the ISX-CBMN method may be an advantageous tool following a radiological event where triage biodosimetry must be performed on a large number of casualties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".