The Effect of Density Gradient Centrifugation and Magnetic-Activated Cell Sorting with the Combination of Both on Bovine Spermatozoa Kinematics, Viability and Acrosome Status
Bibliographic record
Abstract
Conventional and novel spermatozoa separation techniques have their own strength and weaknesses. Both are widely used in assisted reproductive technologies (ARTs). In this study, combinations of two separation methods against single separation methods were evaluated. Freshly ejaculated semen was collected and divided into five groups; control, density gradient centrifugation (DGC), magnetic activated cell sorting (MACS), DGC-MACS and MACS-DGC; based on the sequence of separation. Evaluation of spermatozoa kinematics, viability and acrosome status for each group were done by computer assisted semen analysis (CASA), hypo-osmotic swelling test (HOST) and chlorotetracycline (CTC) staining respectively. Results show that DGC yield the fastest spermatozoa velocity and highest percentage of viable spermatozoa fraction compared to other groups. However, they also produced a high percentage of acrosome-reacted spermatozoa. Spermatozoa isolated through DGC-MACS shows the highest percentage of non-capacitated spermatozoa. However, spermatozoa isolated through DGC-MACS yields slower spermatozoa velocity population and lower percentage of viable sperm compared to spermatozoa isolated through DGC. Isolation of spermatozoa through MACS-DGC yields slower population of spermatozoa velocity with a lower percentage of non-capacitated spermatozoa compared to spermatozoa isolated through DGC-MACS. DGC separation and its combination with MACS had yield different spermatozoa quality. This distinct characteristic is likely suitable for in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) procedure respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".