VALIDATING HIGH-THROUGHPUT MICRONUCLEUS ANALYSIS OF PERIPHERAL RETICULOCYTES FOR RADIATION BIODOSIMETRY: BENCHMARK AGAINST DICENTRIC AND CBMN ASSAYS IN A MOUSE MODEL
Bibliographic record
Abstract
Automation of radiation biodosimetry is one of the top priority tasks considered by the Office of Science and Technology Policy and the Homeland Security Council in preparation for the nation's readiness for a possible radionuclear terrorist attack. The Center for Biophysical Assessment and Risk Management Following Irradiation, a consortium of researchers and institutions centered at the University of Rochester, has been investigating automated scoring of radiation-induced micronucleus formation in reticulocytes for high-throughput radiation biodosimetry. The collaborative project is based on a commercially-available product by Litron Laboratories in Rochester, New York. The study was designed to validate the flow-cytometry based analysis of micronucleated reticulocyte expression for radiation biodosimetry by benchmarking against the standard lymphocyte-based biodosimetry methods in a mouse model. C57B1/6 mice and C3H mice were exposed to Cs total-body radiation from 0-3 Gy. Blood samples were subsequently analyzed for CD71+ micronucleated reticulocyte and reticulocyte frequencies by flow cytometry. Results showed a linear dose-response of MN-RET up to 1 Gy for C57B1/6 and 2 Gy for C3H mice. On the other hand, robust and good dose-response curves were obtained with lymphocyte-based dicentric assay and cytokinesis-block micronucleus assay up to 3 Gy. High-throughput, automated analyses of micronucleated reticulocytes is a sensitive and reproducible method for detecting recent radiation exposure. In mice, the dose range of detection is useful up to 1 Gy (C57Bl/6) and 2 Gy (C3H) but not reliable beyond these dose limits. The utilization of this automated analysis for human radiation biodosimetry is currently under investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".