Interlaboratory validation of a CD71-based flow cytometric method (Microflow®) for the scoring of micronucleated reticulocytes in mouse peripheral blood
Bibliographic record
Abstract
An interlaboratory study was performed to validate an anti-CD71/flow cytometry-based technique for enumerating micronucleated reticulocytes (MN-RETs) in mouse peripheral blood. These experiments were designed to address International Workshop on Genotoxicity Test Procedures validation criteria by evaluating the degree of correspondence between MN-RET measurements generated by flow cytometry (FCM) with those obtained using traditional microscopy-based methods. In addition to these cross-methods data, flow cytometric MN-RET measurements for each blood sample were performed at two separate sites in order to evaluate the reproducibility of data between laboratories. In these studies, groups of male CD-1 mice were treated with vehicle (saline or vegetable oil), a negative control (saline or vegetable oil), or four dose levels of five known genotoxicants (clastogens: cyclophosphamide, benzo[a]pyrene, 5-fluorouracil, methotrexate; aneugen: vincristine sulfate). Exposure occurred on 3 consecutive days via intraperitoneal injection, and blood samples were obtained approximately 24 hr after the final treatment. MN-RET frequencies were determined for each sample based on the analysis of 2,000 (microscopy) and 20,000 (FCM) reticulocytes. Regardless of the method utilized, each genotoxic agent was observed to cause statistically significant increases in the frequency of MN-RETs, and each response occurred in a dose-dependent manner. Spearman's correlation coefficient (rs) for FCM versus microscopy-based MN-RET measurements (nine experiments, 252 paired measurements) was 0.740, indicating a high degree of correspondence between methods. The rs value for all flow cytometric MN-RET measurements performed at the two independent sites was 0.857 (n = 248), suggesting that the automated method is highly transferable between laboratories. Additionally, the flow cytometric system offered advantages relative to microscopy-based scoring, including a greater number of cells analyzed, much faster analysis times, and a greater degree of objectivity. Collectively, data presented in this report suggest that the overall performance of mouse peripheral blood micronucleus tests is enhanced by the use of the flow cytometric scoring procedure.
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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 | 0.031 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".