THE EFFECT OF AN OPTIMIZED IMAGING FLOW CYTOMETRY ANALYSIS TEMPLATE ON SAMPLE THROUGHPUT IN THE REDUCED CULTURE CYTOKINESIS-BLOCK MICRONUCLEUS ASSAY
Bibliographic record
Abstract
In cases of overexposure to ionizing radiation, the cytokinesis-block micronucleus (CBMN) assay can be performed in order to estimate the dose of radiation to an exposed individual. However, in the event of a large-scale radiation accident with many potentially exposed casualties, the assay must be able to generate accurate dose estimates to within ±0.5 Gy as quickly as possible. The assay has been adapted to, validated and optimized on the ImageStreamX imaging flow cytometer. The ease of running this automated version of the CBMN assay allowed investigation into the accuracy of dose estimates after reducing the volume of whole blood cultured to 200 µl and reducing the culture time to 48 h. The data analysis template used to identify binucleated lymphocyte cells (BNCs) and micronuclei (MN) has since been optimized to improve the sensitivity and specificity of BNC and MN detection. This paper presents a re-analysis of existing data using this optimized analysis template to demonstrate that dose estimations from blinded samples can be obtained to the same level of accuracy in a shorter data collection time. Here, we show that dose estimates from blinded samples were obtained to within ±0.5 Gy of the delivered dose when data collection time was reduced by 30 min at standard culture conditions and by 15 min at reduced culture conditions. Reducing data collection time while retaining the same level of accuracy in our imaging flow cytometry-based version of the CBMN assay results in higher throughput and further increases the relevancy of the CBMN assay as a radiation biodosimeter.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".