Sperm swim-up techniques and DNA fragmentation
Bibliographic record
Abstract
BACKGROUND: Swim-up techniques for sperm separation may have detrimental effects on sperm DNA. We wished to determine whether the normal swim-up method with centrifugation used in our laboratory, which involves a centrifugation step, was harmful to sperm compared with swim-up without centrifugation. METHODS: Semen samples were obtained from patients undergoing IVF or andrology assessment. An aliquot was removed for fixation and subsequent DNA fragmentation determination. The remaining sample was divided into two equal parts, which were subjected to swim-up either with (normal swim-up) or without (direct-swim-up) centrifugation. Semen analysis was performed both before and after swim-up. DNA fragmentation, in spermatozoa previously fixed in 4% paraformaldehyde, was assessed by the terminal transferase-mediated DNA end-labelling procedure (TUNEL). The percentage of spermatozoa with DNA damage after each swim-up technique was compared with that in the original semen sample. RESULTS: DNA damage was <5% in most samples. No significant change in DNA fragmentation was observed between the two swim-up procedures, although the 'normal' swim-up sample had significantly less DNA fragmentation than the pre-swim-up sample. CONCLUSIONS: We conclude that our normal swim-up technique caused no more DNA damage to spermatozoa from normal semen samples than a direct swim-up technique that involved no centrifugation step.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".