Improvement of Post-Thaw Sperm Kinematics and DNA Integrity of Cross-Bred Bovine Sperm by Incorporating DGC as Selection Method Prior to Cryopreservation
Bibliographic record
Abstract
The aim of this study was to assess post-thaw sperm quality following initial sperm selection using density gradient centrifugation (DGC) prior to cryopreservation. Ejaculates from four mature Charolais cross Kedah-Kelantan bulls were collected using artificial vagina at IBVK Pahang, Malaysia. The ejaculates were aliquoted into 3 groups: non-cryopreserved group (NC); control group of cryopreserved sperm without DGC (ND) and treatment group of sperm undergoing DGC sperm selection before cryopreservation (CDGC). Prior to analysis, samples from both cryopreserved groups were thawed at 37 °C for 30 sec. All samples were analysed for kinematics parameters, viability and compromise in DNA integrity (evaluated as DNA Fragmentation Index, DFI). All kinematics parameters were analysed using computer aided sperm analysis (CASA). Results indicated significant (p < 0.05) kinematics parameter changes for all parameters of velocity (VCL, VSL, VAP) and progression (WOB, LIN, ALH and BCF). Unfortunately, changes in spermatozoa straightness were insignificant (STR) F(2, 68) = 1.004, p = 0.371. Spermatozoa viability had increased by 26.2% (p < 0.01) following the treatment. DFI revealed the treatment group recorded a significant reduction in DFI value (0.17% fragmented DNA). In conclusion, DGC sperm selection prior to cryopreservation reduced the effects of cryodamage and showed an improvement in post-thaw sperm quality, thus reducing the occurrence of asthenozoospermia in populations of frozen-thawed cross-bred bovine spermatozoa.
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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".