Nanowater enhances cryoprotective properties of glycerol-containing extenders used for ram semen freezing: A preliminary study spanning laboratory testing
Bibliographic record
Abstract
Abstract It has been suggested that nanowater (NW-water declustered in the cold plasma generator and characterized by a low freezing point and high diffusivity) could improve ram semen quality after freezing in glycerol-containing extenders. Eighteen ejaculates from six Olkuska rams were divided into six equal portions each, and then diluted (800×10 6 spermatozoa/ml) and frozen in the fructose-skimmed milk-egg yolk Kareta extenders containing 3% or 7% of glycerol. The extenders were prepared with deionized water (DW-3% and DW-7%) or NW declustered for 15 min (NW15’) or 30 min (NW30’). Post-thaw sperm motility, proportions of sperm defects and percentages of apoptotic, necrotic, and live spermatozoa were determined. The proportion of spermatozoa with midpiece defects was lower (P<0.05) in NW15’-3% compared with DW-3%. Sperm progressive motility was greater (P<0.05) for spermatozoa cryopreserved in both NW30’ (NW30’-3%/7%) extenders compared with their respective controls (DW30’-3%/7%). The proportion of necrotic spermatozoa 1 h after thawing was lower (P<0.05) in NW30’-7% compared with DW-7%, whereas the proportion of live cells detected immediately and 1 h after thawing was greater (P<0.05) in NW30’-7% than in DW-7%. In summary, NW enhanced cryoprotective effects of glycerol-containing extenders with an increase in sperm viability being greater with 7% than 3% of glycerol. Different declustering times appear to alter NW properties. These observations merit future studies of the utility of NW for semen cryopreservation in rams and other mammalian species. The specific mechanisms whereby NW ameliorates the quality of frozen-thawed ram spermatozoa remain to be elucidated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".