Effect of Staining Procedures on the Results of Micronucleus Assays with Exfoliated Oral Mucosa Cells
Bibliographic record
Abstract
Micronuclei in exfoliated epithelial cells are widely used as biomarkers of cancer risk in humans. To elucidate the effect of different staining procedures on the outcome of such investigation, we conducted a study in which the micronuclei frequencies in oral mucosa cells of heavy smokers (n = 20) and nonsmokers (n = 10) were evaluated with nonspecific (Giemsa, May-Grünwald-Giemsa) and DNA-specific (4',6-diamidino-2-phenylindole, Feulgen, acridine orange) stains, whereas with Giemsa-based stains, the frequencies of micronuclei in smokers were significantly (4- to 5-fold) higher in the smokers group, no significant increase was observed with any of the DNA-specific stains. Furthermore, the evaluation of cells of the two study groups with Feulgen stain showed that oral mucosa cells from smokers had significantly increased levels of nuclear anomalies other than micronuclei. These anomalies are consequences of cell injury found in epithelial cells and are paralleled by formation of keratin bodies in the cytoplasm that resemble micronuclei. Correlation analyses showed that micronuclei frequencies scored in Giemsa-stained slides correlated significantly with karyorrhexis, karyolysis, condensed chromatin, and binucleates, whereas no such correlations were found with DNA-specific stains. These findings indicate that nuclear anomalies (and possibly keratin bodies) may be misinterpreted as micronuclei with nonspecific DNA stains and lead to false-positive results in studies with cells of epithelial origin. Furthermore, our results show that exposure of oral mucosa cells to genotoxic carcinogens contained in tobacco smoke does not lead to induction of micronuclei in these cells.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".