Isolate<sup>®</sup> and Optiprep<sup>®</sup> minigradients as alternatives for sperm selection in bovine in vitro embryo production
Bibliographic record
Abstract
Vianna, L. L., Pradieé, J., Santos, E. C. S., Gonçalves, A. O., Pfeifer, L. F. M., Rheingantz, M. G. T., Dode, M. A. N., Vieira, A. D., Lima, V. F. H., Correa, M. N. and Pegoraro, L. M. C. 2014. Isolate® and Optiprep® minigradients as alternatives for sperm selection in bovine in vitro embryo production. Can. J. Anim. Sci. 94: 35–42. The objective of this study was to evaluate alternatives in small volumes to conventional gradient of Percoll® on semen quality, in vitro embryo production, sex ratio and embryo survival after vitrification. Thawed semen was randomly allocated to one of four density gradient selection methods: (1) conventional Percoll® (P), (2) MiniPercoll (MP), (3) MiniIsolate (MI), and (4) MiniOptiprep (MO). Sperm kinetics and quality were evaluated. Use of P, MP and MI gradients did not affect sperm motility (P>0.05). However, there was a decrease in total and progressive sperm motility in MO (70.8 and 51.3% vs. 87.3 and 69.5% for P; 87.3 and 73% for MP; 92.3 and 78.8% for MI; P<0.05). The MO had lower membrane integrity compared with P, MP and MI (39.7 vs. 70.5, 72.3, 63.8%, respectively, P<0.05). The percentage of blastocysts produced was higher in MI than in MP and MO (21.1 vs. 16.1 and 16.9%, P<0.05) and similar to P (18.4%; P>0.05). Sex ratio and embryo survival after vitrification were similar among groups (P>0.05). Semen selected by Isolate and Optiprep gradient, at the concentrations and small volumes used, demonstrated similar characteristics and in vitro embryo production to conventional Percoll® gradient.
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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 | 0.000 |
| 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 | 0.001 | 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".