Aneuploidy in human sperm: results of two- and three-color fluorescence in situ hybridization using centromeric probes for chromosomes 1, 12, 15, 18, X, and Y
Bibliographic record
Abstract
To understand the mechanisms that affect aneuploidy, fluorescence in situ hybridization (FISH), using chromosome-specific centromeric probes, was employed to screen a large population of human sperm for numerical errors. To determine the true rate of disomy for chromosomes 1, 12, 15, and 18, two-color FISH was performed, and for the gonosomes, three-color FISH. The use of multiple, differently colored probes allows one to distinguish a true disomic sperm from a diploid cell. A minimum of 10,000 sperm nuclei from each of five donors was scored per set of centromeric probes, giving a total of 165,330 sperm nuclei. The disomy frequencies for autosomes 1, 12, 15, and 18 were found to be similar, with a mean of 0.10% (range, 0.05%-0.16%) for chromosome 1, 0.16% (0.10%-0.25%) for chromosome 12, 0.11% (0.07%-0.20%) for chromosome 15, and 0.11% (0.08%-0.17%) for chromosome 18. For the sex chromosomes, the mean frequency of disomy was found to be 0.43% (range, 0.23%-0.71%), with XX disomy accounting for 0.07% (0.03%-0.10%), YY disomy 0.21% (0.10%-0.43%), and XY disomy 0.15% (0.08%-0.24%). The incidence of disomic sperm for the sex chromosomes was significantly increased, compared to the frequency of disomy for the autosomes (chi 2 = 218.61, P < 0.0001). Diploidy was observed in 0.05%-0.47% of the sperm nuclei counted. Interdonor heterogeneity for disomy frequencies was found to exist for the sex chromosomes and for chromosomes 1 and 15, suggesting significant variation among normal men.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".