THE CORRELATION BETWEEN DNA FRAGMENTATION INDEX (DFI), SPERM PARAMETERS, EUPLOIDY AND PREGNANCY OUTCOMES AFTER ICSI
Bibliographic record
Abstract
Introduction: DNA fragmentation index (DFI) is a diagnostic assessment that measures the percentage of denatured sperm (DNA damage) in a semen sample. A low DFI score (<15%) is thought to be associated with better success in natural conception and assisted reproductive technology. In contrast, a borderline DFI score (> 15% to < 30%) indicates good to fair fertility potential, and a high DFI score (≥ 30%) has been associated with poor fertility. Objective: This study investigated the relationship between sperm parameters including DFI scores, and their impact on embryo development, euploidy status and pregnancy rates. Design: A retrospective cohort study at a university-affiliated private clinic. Materials and Methods: Patients (n=98) were stratified into one of three groups based on DFI score: DFI <15% (n=57); DFI > 15% to < 30% (n=31); and DFI ≥ 30% (n=10) for comparison. Sperm was processed for ICSI using a conventional swim-up method. Descriptive statistics were calculated as mean ± standard deviation. A one-way ANOVA comparing concentration, motility and morphology between each cohort was calculated. To investigate embryo euploidy and pregnancy rates, borderline and high DFI scores were combined and compared to low DFI scores using a chi-square analysis. A p-value < 0.05 was considered statistically significant. Results: Age of female partner, dose of FSH and E2 levels were similar amongst the three groups. Low DFI samples had significantly higher motility (p=0.037), concentration (p<0.001), morphology (p=0.007) and post swim-up motility (p=0.026) when compared to samples with a high DFI. No difference was seen in overall pregnancy rates. Subsequent analysis between embryo euploidy rates between low DFI (30.8%, n=9) and borderline to high DFI (34.0 %, n = 8) showed no difference (p = 0.830). Conclusions: Sperm parameters associated with a low DFI score had no impact on embryo euploidy and pregnancy rates. We propose that the swim-up preparation and selection of sperm by the embryologist for ICSI mitigates the effects of high DFI. Further investigation and a sufficiently powered sample size is required to confirm results. Support: In-kind contribution from ONE Fertility.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".