Analysis of Aneuploidy in Spermatozoa From Testicular Biopsies From Men With Nonobstructive Azoospermia
Bibliographic record
Abstract
Testicular sperm biopsy combined with intracytoplasmic sperm injection (ICSI) allows men with azoospermia the possibility of fathering a child. However, little information exists on the risk of chromosome abnormalities in their sperm. Multicolor fluorescence in situ hybridization (FISH) analysis was used to determine the frequency of sperm diploidy and disomy for the sex chromosomes in six men with normal karyotypes and non-obstructive azoospermia. A new method using microwave decondensation and codenaturation of sperm nuclei yielded a much larger number of sperm nuclei for FISH analysis than our previous study of men with azoospermia. A total of 59916 sperm were analyzed; more than 9000 sperm were scored for each man. The men with nonobstructive azoospermia had an increased frequency of sperm chromosomal disomy for YY, XY, total sex chromosomal disomy, and diploidy compared with 18 normal controls, but only YY disomy reached statistical significance. One infertile man had a frequency of 3.8% XY disomy and 4.3% diploidy, which was 13-fold and 7-fold higher than control donors, respectively. Our results suggest that some men with nonobstructive azoospermia have a significantly increased frequency of sex chromosomal abnormalities than normal men, but that the overall frequency of abnormalities is similar to that found in infertile men with abnormal semen parameters.
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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 | 0.000 | 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".