Optimizing and quantifying fusion of liposomes to mammalian sperm using resonance energy transfer and flow cytometric methods
Bibliographic record
Abstract
BACKGROUND: Liposomes are used to carry pharmaceutical agents and to alter the lipid composition of cell membranes. This study compared resonance energy transfer (RET), fluorescence dequenching, and flow cytometry as monitors and quantifiers of fusion between liposomes and mammalian spermatozoa. METHODS: Preliminary experiments used RET to determine the optimum sperm concentration for fusion of DL-alpha-phosphatidylcholine dipalmitoyl (PC)/DL-alpha-phosphatidylethanolamine dipalmitoyl (PE) liposomes at 35 degrees C +/- 5 mM Ca2+. Microscopy confirmed the fusion of liposomes, not just adhesion (n = 3). Dequenching tested the time-dependent fusion of liposomes of two different lipid compositions to sperm, both, (n = 3) +/- 1 mM Ca2+ and (n = 3) without Ca2+ at two sperm concentrations. Finally, flow cytometry absolutely quantified the percentage of sperm fusing to liposomes at different liposome-to-sperm ratios (n = 4) and with sperm from different donors (n = 3). RESULTS: RET detected fusion of liposomes with sperm and microscopy confirmed the interaction to be true fusion. Dequenching detected more fusion of liposomes with sperm at 100 x 10(6) sperm per milliliter than at lower concentrations (P < 0.05). Fusion dynamics differed with lipid composition but Ca2+ had no effect. Flow cytometry reliably quantified the percentage of sperm fusing with liposomes, which varied from bull to bull (P < 0.05). CONCLUSION: Liposome fusion with mammalian sperm membranes can be quantified cytometrically and varies with lipid composition, sperm-to-liposome ratio, and individual animals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".