Frequency of Semen Collection Affects Ram Sperm Cryoresistance
Bibliographic record
Abstract
The improvement of frozen-thawed sperm quality has been mostly approached from the view of cryopreservation protocol optimization in terms of cryoprotectant solutions, freezing-thawing rates and antioxidant supplementation, while the impact of sperm collection frequency remains unknown in rams. In this work, a multiparametric study was carried out in cooled and frozen-thawed semen to evaluate sperm quality after different semen collection frequencies during a month: zero sperm collection (0 CW), four sperm collections per week (4 CW), and ten sperm collections per week (10 CW). Traditional analyses have been applied, in combination with novel technologies related to redox balance. Frozen-thawed semen quality showed a significant decrease (p < 0.05) in 0 CW and 10 CW in comparison to 4 CW, concerning motility and kinetics parameters. However, apoptosis showed a significant increase (p < 0.05) in 10 CW in comparison to 0 CW and 4 CW. The employment methods related to redox balance provided us with the definitive probe to ensure the influence of collection frequency on balance redox after thawing. Specifically, glutathione peroxidase (GPX) and superoxide dismutase (SOD) activity showed a significant decrease (p < 0.05) in 10 CW compared to 0 CW and 4 CW. The characterization of alternative strategies to sperm cryopreservation based on consideration of male sexual regimes, could improve the quality of frozen-thawed sperm.
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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 | 0.000 | 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) | 0.003 | 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".