Validation of the Cytokinesis-block Micronucleus Assay Using Imaging Flow Cytometry for High Throughput Radiation Biodosimetry
Bibliographic record
Abstract
The cytokinesis-block micronucleus assay can be employed in triage radiation biodosimetry to determine the dose of radiation to an exposed individual by quantifying the frequency of micronuclei in binucleated lymphocyte cells. Partially automated analysis of the assay has been applied to traditional microscope-based methods, and most recently, the assay has been adapted to an automated imaging flow cytometry method. This method is able to automatically score a larger number of binucleated cells than are typically scored by microscopy. Whole blood samples were irradiated, divided into 2 mL and 200 μL aliquots, cultured for 48 h and 72 h, and processed to generate calibration curves from 0-4 Gy. To validate the method for use in radiation biodosimetry, nine separate whole blood samples were then irradiated to known doses, blinded, and processed. Results indicate that dose estimations can be determined to within ±0.5 Gy of the delivered dose after only 48 h of culture time with an initial blood volume of 200 μL. By performing the cytokinesis-block micronucleus assay using imaging flow cytometry, a significant reduction in the culture time and volume requirements is possible, which greatly increases the applicability of the assay in high throughput triage radiation biodosimetry.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".