Spontaneous frequencies of aneuploid and diploid sperm in 10 normal Chinese men: assessed by multicolor fluorescence in situ hybridization
Bibliographic record
Abstract
Many studies have been published establishing the background frequencies of disomic and diploid sperm in normal men by fluorescence in situ hybridization (FISH) analysis, with highly significant variance among the reports. Besides interdonor heterogeneity and differences in the experimental protocols used, the question of inherent differences in chromosome malsegregation and meiotic arrest among different geographic and ethnic groups of donors has been raised. In this study, multicolor FISH analysis was carried out on semen samples from 10 nonsmoking, nondrinking Chinese men from the People's Republic of China. The results were compared to FISH data on 10 nonsmoking, nondrinking Canadians under the same experimental conditions, in the same laboratory. A total of 200,497 sperm was scored in the Chinese donors and compared to 202,320 sperm from Canadian donors. Approximately 10,000 sperm per chromosome probe per donor were analyzed. The mean hybridization efficiency was 99.99%. The frequencies of X-bearing and Y-bearing sperm were not significantly different from the expected 50% for each individual and for the combined data from all donors (49.73% vs. 49.46%, P = 0.3946). The mean disomy frequencies (range) were 0.07% (0.02%-0.12%) for chromosome 13, 0.18% (0.09%-0.19%) for chromosome 21, 0.05% (0. 01%-0.09%) for 24,XX, 0.02% (0.01%-0.06%) for 24,YY, and 0.29% (0. 13%-0.49%) for 24,XY. The mean diploidy frequency (range) was 0.38% (0.22%-0.73%) for 13-21 hybridizations and 0.32% (0.07%-0.70%) for XY hybridizations. Highly significant interdonor heterogeneity was found for diploidy (P = 0.0000) and for XY disomy (P = 0.0011), but no age effect was observed in any category of disomic or diploid sperm. The data reported here show no marked differences in disomy and diploidy frequencies between the mainland Chinese and Canadian groups, if donor heterogeneity is taken into account.
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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 | 0.000 |
| 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".