The effect of centrifugation, osmotic pressure variations and pipetting on drone semen quality
Bibliographic record
Abstract
Drone semen undergoes various processes, including cryopreservation, genetic research and spermatological parameter analysis. Physical stress factors during these processes may negatively affect semen quality. This study evaluated the effects of osmotic pressure variations, repeated pipetting and centrifugation on drone semen, focusing on motility, plasma membrane integrity (PMI) and mitochondrial membrane potential (MMP). Exposure to different osmotic variations caused PMI loss at 200 mOsm/kg and motility losses at 200, 400 and 500 mOsm/kg. Repeated pipetting adversely impacted both motility and PMI. Centrifugation reduced motility at 800 × g and above, with PMI decreasing at 1000 × g and higher. MMP showed no statistically significant differences across treatments. Comparative analysis with other species revealed that semen from humans, canines and rats exhibited motility and PMI reductions at lower centrifugation forces. In another study conducted on drone semen, this effect was observed at forces of 1000 × g and above. Repeated pipetting decreased motility and PMI by up to 40% in rat and mouse semen and by 10% in ram, bull and boar semen. Osmotic pressure variations also adversely affected rat and ram semen, though drone semen displayed greater sensitivity. The most reliable parameters for drone semen were identified as centrifugation at 600 × g for 10 min, up to five pipetting repetitions and dilution within 250–300 mOsm/kg. These findings highlight the significant impact of stress factors on drone semen, emphasizing the need for optimized handling protocols to maintain semen integrity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".