Analysis of kinetic parameters of sexed Holstein-Friesian bull spermatozoa using Percoll density gradient centrifugation with computer-assisted sperm analysis
Bibliographic record
Abstract
Background and Aim: Artificial insemination (AI) is a key biotechnology for improving dairy cattle populations, offering genetic enhancement and increased milk production. The advent of sexed semen allows for the preferential selection of female offspring which is beneficial for dairy operations. This study aimed to evaluate and optimize a spermatozoa sexing method using Percoll density gradient centrifugation (PDGC) and analyze kinetic parameters of the separated spermatozoa using computer-assisted sperm analysis. Materials and Methods: The study was conducted on two Holstein-Friesian bulls at the Singosari Artificial Insemination Center and Universitas Brawijaya, Indonesia. Semen samples underwent PDGC sexing at two density gradients, (T1) 20%-65% and (T2) 20%-60%. Kinetic parameters, including motility, velocity, and movement patterns, were assessed pre- and post-sexing. Statistical analyses were performed using a one-way analysis of variance and Duncan's test to determine significant differences. Results: Fresh semen (control) exhibited significantly higher motility (88.45%) compared to T1 (70.94%) and T2 (72.22%), with p < 0.01. Velocity parameters, including curvilinear velocity, were also significantly reduced post-sexing. However, motility levels in sexed samples still exceeded the 40% AI threshold. The 20%-65% gradient demonstrated better performance in maintaining sperm quality compared to the 20%-60% gradient. Conclusion: Although sexing reduced motility and kinetic parameters, both gradients yielded semen suitable for AI applications. The 20%-65% gradient showed superior results, indicating its potential for optimizing the sexing process. Further research is recommended to refine the technique and improve the viability of sexed 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 | 0.000 | 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 | 0.000 | 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".