Fusion of Boar Sperm with Nanoliposomes Prepared from Synthetic Phospholipids
Bibliographic record
Abstract
Liposomes are artificial membrane vesicles that can be used to test and model the functions and interactions of various biological membranes, or as a carrier system to deliver biologically active substances into the cells, or to incorporate lipids into the plasma membrane of target cells to modify membrane structure-function relationships. Sperm plasma membrane undergoes lipid modification during maturation in epididymis and during capacitation in the female reproductive tract to facilitate fertilization. Natural variation in the amounts and composition of lipids in the sperm plasma membrane may also contribute to the species-specific sperm sensitivities to handling and storage conditions. Boar sperm are notoriously susceptible to membrane damage and are resistant to compositional alteration by artificial liposomes. This study used flow cytometry to demonstrate stable incorporation of nanoliposomes prepared from a complex mixture of various phospholipids (phosphatidylcholine : phosphatidylethanolamine : sphingomyelin : phosphatidylserine : phosphatidylinositol) with high fusion efficiency. Over 90% of sperm rapidly took up fluorescently labelled liposomes and retained the lipids for at least 60 min, in a significant time- and concentration-dependent manner. This unique fusion efficacy could be used to alter sperm plasma membrane composition and hence membrane-based functional responses.
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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".