Screening for Optimal Liposome Preparation Conditions by Using Dual Centrifugation and Time-Resolved Fluorescence Measurements
Bibliographic record
Abstract
Dual centrifugation (DC) is a novel in-vial homogenization technique for the preparation of liposomes in small batch sizes under gentle and sterile conditions which allows encapsulation efficiencies (EE) for water soluble compounds of >50%. Since liposome size, size distribution (PDI), and EE depend on the lipid concentration used in the DC process, a screening method to find optimal lipid concentrations for a defined lipid composition was developed. Four lipid mixtures consisting of cholesterol, hydrogenated or non-hydrogenated egg PC, and/or PEG-DSPE were screened and suitable concentration ranges could be identified for optimal DC homogenization. In addition to the very fast and parallel liposome preparation of up to 40 samples, the screening process was further accelerated by the finding that DC generates homogeneously mixed liposomes from a macroscopic lipid mixture without the need to initially prepare a molecularly mixed lipid film from an organic solution of all components. This much simpler procedure even works for cholesterol containing lipid blends, which could be explained by a nano-milling of the cholesterol crystals during DC homogenization. Furthermore, EE determination was performed by time-resolved fluorescence measurements of calcein-loaded liposomes without removing the non-entrapped calcein. The new strategy allows the rapid characterization of a certain lipid composition for the preparation of liposomes within a working day.
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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 | 0.000 | 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) | 0.000 | 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".