Multiparameter imaging flow cytometry-based cytokinesis-block micronucleus assay: Reduction of culture time and blood volume for improved efficiency
Bibliographic record
Abstract
In the event of a large-scale incident involving radiological or nuclear exposures, there is a potential for large numbers of individuals to have received doses of radiation sufficient to cause adverse health effects. It is imperative to quickly identify these individuals in order to provide information to the medical community to assist in making decisions about their treatment. The cytokinesis-block micronucleus assay is a well-established method for performing biodosimetry. This assay has previously been adapted to imaging flow cytometry and has been validated as a high-throughput option for providing dose estimates in the range of 0-10 Gy. The goal of this study was to test the ability to further optimize the assay by reducing the time of culture to 48 h from 68 h as well as reducing the volume of blood required for the analysis to 200 μL from 2 mL. These modifications would provide efficiencies in time and ease of processing impacting the ability to manage large numbers of samples and provide dose estimates in a timely manner. Results demonstrated that either the blood volume or the culture time could be reduced while maintaining dose estimates with sufficient accuracy for triage analysis. Reducing both the blood volume and culture time, however, resulted in poor dose estimates. In conclusion, depending on the needs of the scenario, either culture time or the blood volume could be reduced to improve the efficiency of analysis for mass casualty scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".