Flow cytometric enumeration of micronucleated reticulocytes: High transferability among 14 laboratories
Bibliographic record
Abstract
This laboratory previously described a single-laser flow cytometric method, which effectively resolves micronucleated erythrocyte populations in rodent peripheral blood samples. Even so, the rarity and variable size of micronuclei make it difficult to configure instrument settings consistently and define analysis regions rationally to enumerate the cell populations of interest. Murine erythrocytes from animals infected with the malaria parasite Plasmodium berghei contain a high prevalence of erythrocytes with a uniform DNA content. This biological model for micronucleated erythrocytes offers a means by which the micronucleus analysis regions can be rationally defined, and a means for controlling interexperimental variation. The experiments described herein were performed to extend these studies by testing whether malaria-infected erythrocytes could also be used to enhance the transferability of the method, as well as control intra- and interlaboratory variation. For these studies, blood samples from mice infected with malaria, or treated with vehicle or the clastogen methyl methanesulfonate, were fixed and shipped to collaborating laboratories for analysis. After configuring instrumentation parameters and guiding the position of analysis regions with the malaria-infected blood samples, micronucleated reticulocyte frequencies were measured (20,000 reticulocytes per sample). To evaluate both intra- and interlaboratory variation, five replicates were analyzed per day, and these analyses were repeated on up to five separate days. The data of 14 laboratories presented herein indicate that transferability of this flow cytometric technique is high when instrumentation is guided by the biological standard Plasmodium berghei.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".