Potential adverse effect of sperm DNA damage on embryo quality after ICSI
Bibliographic record
Abstract
BACKGROUND: Sperm DNA damage is prevalent amongst infertile men and has been shown to strongly impact adversely natural reproduction, intrauterine insemination-assisted reproduction and to a lesser degree IVF/ICSI fertilization. The objective of this study was to examine further the relationship between sperm DNA denaturation (DD) and reproductive outcomes after ICSI. METHODS: We evaluated infertile couples (n = 60) undergoing IVF/ICSI at a single centre. Sperm DD was assessed by flow cytometry analysis of Acridine Orange-treated sperm and expressed as the percentage of sperm with DD. Couples were sub-grouped according to sperm DD results: group 1: 0-15%; group 2: >15-30%; group 3: >30%. RESULTS: There were no differences between the three groups with regard to maternal age, sperm parameters, oocyte maturation, fertilization or pregnancy rates. Group 3 had a significantly higher rate of multinucleation among the embryo cohorts compared to either groups 1 or 2 (20% versus 10% and 8% respectively, P = 0.04). There was a statistically insignificant trend toward an increased spontaneous pregnancy loss rate in group 3 (P =0.50). CONCLUSION: Although we did not observe significant relationships between sperm DNA damage and either fertilization or pregnancy rates, the potential adverse effect of sperm DNA damage on embryo quality and spontaneous pregnancy loss is concerning.
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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 | 0.003 |
| 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".