Microfluidic assessment of swimming media for motility-based sperm selection
Bibliographic record
Abstract
Selection medium is important in sperm isolation for assisted reproductive technologies. Contrary to the naturally occurring human cervical mucus which has a high viscosity, most current practices for motility based sperm selection use a low viscosity medium. In this study, we used a microfluidic device to assess the effects of high viscosity media made with hyaluronic acid (HA) and methyl cellulose (MC) on bovine and human sperm motility and viability (sperm transferred directly from cryoprotectant). The microfluidic penetration test, viability, and motility were compared for sperm swimming in both HA and MC media with about 20cp viscosity (measured at 20 °C). Our resulted indicate that MC medium resulted in a significantly higher number of viable bovine sperm penetrating the medium as compared to HA. Furthermore, MC resulted in the selection of a sperm subpopulation with a 274% increase in sperm viability in comparison to the raw semen, while HA increased viability by only 133%. In addition to viability, bovine sperm motility parameters were significantly higher in the MC medium as compared with HA. Experiments with human sperm swimming in MC indicate that sperm swim slower and straighter at higher viscosities. In conclusion, the results indicate that in a micro-confined environment representative of the in vivo environment, MC is a preferred high viscosity medium to ensure the highest concentration of motile and viable 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 | 0.001 |
| 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) | 0.000 | 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".