Sperm aneuploidy and meiotic sex chromosome configurations in an infertile XYY male
Bibliographic record
Abstract
BACKGROUND: There is little information regarding the behaviour of the extra Y chromosome during meiosis I in men with 47,XYY karyotypes and the segregation of the sex chromosomes in sperm. We applied immunofluorescent and FISH techniques to study the relationship between the sex chromosome configuration in meiotic germ cells and the segregation pattern in sperm, both isolated from semen samples of a 47,XYY infertile man. METHODS: The sex chromosome configuration of pachytene germ cells was determined by immunostaining pachytene nuclei for synaptonemal complex protein 3 (SCP3) and SCP1. FISH was subsequently performed to identify the sex chromosomes and chromosome 18 in pachytene cells. Dual- and triple-color FISH was performed on sperm to analyse aneuploidy for chromosomes 13, 18, 21, X, and Y. RESULTS: 46,XY/47,XYY mosaic pachytene cells were observed (22.2% vs. 77.8%, respectively). The XYY trivalent, and X+YY configurations were most common. While the majority of sperm were of normal chromosomal constitution, an increase in sex and autosome disomy was observed. CONCLUSIONS: The level of germ cell moscaicism and their meiotic sex chromosome configurations may determine sperm aneuploidy rate and fertility status in 47,XYY men. Our approach of immunostaining meiotic cells in the ejaculate is a novel method for investigating spermatogenesis in infertile men.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".