The Effects of Glycerophospholipid Nanomicelles on the Cryotolerance of Frozen–Thawed Rooster Sperm
Bibliographic record
Abstract
Semen banking is an efficient method of artificial insemination for commercial breeders. However, the cryopreservation process induces severe damages to plasma membranes, which leads to reduced fertility potential of thawed sperm. The replacement of membrane lipids with oxidized membrane lipids repairs the cell membrane and improves its stability. The aim of this study was to investigate the effects of glycerophospholipid (GPL) nanomicelles on the cryosurvival of thawed rooster semen. Semen samples were collected from six 29-week Ross broiler breeder roosters, then mixed and divided into five equal parts. The samples were diluted with the Beltsville extender containing different concentrations of GPL according to the following groups: 0 (GPL-0), 0.1% (GPL-0.1), 0.5% (GPL-0.5), 1% (GPL-1), and 1.5% (GPL-1.5), then diluted semen was gradually cooled to 4°C during 3 hours and stored in liquid nitrogen. The optimum concentration of GPL was determined based on the quality parameters of thawed sperm. Our results showed sperm exposed to GPL-1 had significantly increased motion parameters and mitochondrial activity. The percentages of viability and membrane integrity were significantly higher in the GPL-1, and GPL-1.5 groups compared with the other groups (p < 0.05). Moreover, the lowest rate of apoptosis and lipid peroxidation were observed in the GPL-1 and GPL-1.5 groups in comparison with the frozen control group. Our findings indicated that membrane lipid replacement with GPL nanomicelles (1% and 1.5%) could substitute for damaged lipids in membranes and protect sperm cells against cryoinjury.
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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".