Sperm global <scp>DNA</scp> methylation level: association with semen parameters and genome integrity
Bibliographic record
Abstract
Sperm DNA methylation abnormalities have been detected in oligozoospermic men. However, the association between sperm DNA methylation defects, sperm parameters and sperm DNA, and chromatin integrity remains poorly understood. This study was designed to clarify this issue. We recruited a cohort of 92 men (62 normozoospermic and 30 oligoasthenozoospermic) presenting for infertility evaluation during a 1-year period. Sperm global DNA methylation was evaluated by an ELISA-like method, DNA fragmentation was evaluated by flow cytometry-based terminal transferase dUTP nick end-labeling (TUNEL) assay (reported as DNA fragmentation index or DFI), and sperm denaturation was evaluated by aniline blue staining (reported as sperm denaturation index or SDI, a marker of chromatin compaction). We found a significant positive association between sperm global DNA methylation level and conventional sperm parameters (sperm concentration and motility), supported by the results of methylation analysis on H19-DMR. We also identified significant inverse relationships between sperm global DNA methylation, and, both DFI and SDI. However, sperm global DNA methylation level was not related to sperm vitality or morphology. Our findings suggest that global sperm DNA methylation levels are related to conventional sperm parameters, as well as, sperm chromatin and DNA integrity.
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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 | 0.001 |
| 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) | 0.002 | 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".