The Application of Imaging Flow Cytometry to High-Throughput Biodosimetry
Bibliographic record
Abstract
Biodosimetry methods, including the dicentric chromosome assay, the cytokinesis-block micronucleus assay and the γH2AX marker of DNA damage are used to determine the dose of ionizing radiation. These techniques are particularly useful when physical dosimetry is absent or questioned. While these assays can be very sensitive and specific, the standard methods need to be adapted to increase sample throughput in the case of a large-scale radiological/nuclear event. Recent modifications to the microscope-based assays have resulted in some increased throughput, and a number of biodosimetry networks have been, and continue to be, established and strengthened. As the imaging flow cytometer (IFC) is a technology that can automatically image and analyze processed blood samples for markers of radiation damage, the microscope-based biodosimetry techniques can be modified for the IFC for high-throughput biological dosimetry. Furthermore, the analysis templates can be easily shared between networked biodosimetry laboratories for increased capacity and improved standardization. This review describes recent advances in IFC methodology and their application to biodosimetry.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".