Sperm DNA damage is related to field fertility of semen from young Norwegian Red bulls
Bibliographic record
Abstract
Flow cytometry was utilised for the first time to independently measure five sperm parameters of individual spermatozoa of bull ejaculates to differentiate between outcome successes after artificial insemination (AI). These parameters included plasma membrane and acrosome integrity, mitochondrial functionality and DNA damage measured by sperm chromatin structure assay (SCSA) and terminal deoxynucleotide transferase-mediated dUTP nick end labelling (TUNEL) assays. For each parameter, results of 142 ejaculates (30 bulls) were ranked into three groups according to their flow cytometric measures: (1) ejaculates with the 25% lowest measures; (2) the 50% middle measures; and (3) the 25% highest measures. In total, 20 272 first-service inseminations (18 ;10(6) spermatozoa per AI dose) were performed, where fertility was defined as non-return within 60 days after first insemination. While plasma membrane and acrosome integrity, and mitochondrial functionality were not significantly related to fertility, data from SCSA and TUNEL assays were significantly associated with fertility. Ejaculates in SCSA group 1 had higher odds of AI success (1.07, 95% CI = 1.02-1.12), whereas those in group 3 had lower odds of AI success (0.94, 95% CI = 0.89-0.99), compared with the average odds of all three groups. Ejaculates in group 2 did not have significantly higher odds of AI success compared with the average odds. For TUNEL-positive spermatozoa, the odds of AI success was higher in group 1 compared with the average odds (1.10, 95% CI = 1.02-1.13), whereas odds of AI success in groups 2 and 3 were not significant compared with the average odds. In conclusion, despite the high number of spermatozoa per AI dose from high-quality bulls, both SCSA and TUNEL assays were valuable measures in this study for evaluating sperm quality in relation to fertility after AI.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".