A combination of taurine and caffeine maintains sperm quality in equine semen during chilled storage
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the effects of caffeine and taurine on the motility and viability of chilled equine semen. MATERIALS AND METHODS: A total of 12 ejaculates were collected from three mature stallions with proven fertility during the breeding season. The gel-free spermatic fraction of each ejaculate was divided into two aliquots and diluted with a semen extender (either INRA 96® or BotuSemen Gold®). The aliquots were then split and assigned to one of the six treatment groups: control (no supplement), caffeine (2 and 4 mM), taurine (25 and 50 mM), and a combination of caffeine (2 mM) plus taurine (25 mM). Samples were stored at 4°C and analyzed at different time points (0, 24, 48, 72, and 96 h) to evaluate total (TMOT) and progressive (PMOT) motility and viability by computer-assisted sperm analysis. RESULTS: Regardless of the extender, PMOT and TMOT decreased over time. However, compared with the control, the treatment with 4 mM caffeine significantly mitigated the decrease in PMOT at 72 h. Additionally, semen treated with a combination of caffeine plus taurine maintained a significantly higher PMOT at 96 h, with improved viability at all time points. CONCLUSIONS: The combination of caffeine plus taurine helps maintain chilled equine semen viability and progressive motility up to 96 h independently of the extender used.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".