Determination of Properties of Individual Liposomes by Capillary Electrophoresis with Postcolumn Laser-Induced Fluorescence Detection
Bibliographic record
Abstract
Individual liposome measurements by capillary electrophoresis with postcolumn laser-induced fluorescence detection facilitated the determination of liposome property distributions, two-dimensional plots, and an improved characterization of a liposomal preparation. This advancement in liposome analysis was feasible by using a high-sensitivity postcolumn laser-induced fluorescence detector wired for millisecond response. For each individual liposome containing fluorescein, peak height and migration time were determined. From these measurements the individual entrapped volumes and electrophoretic mobilities were determined. Distribution analysis of these properties facilitated comparison of various liposome dilutions and indicated that the method is reproducible and unaffected by the density of liposomes (10(7)-10(9) liposomes/mL) in the suspension. Furthermore, liposomes showed entrapped volumes that vary from 0.3 to 13 fL with apparent radius varying from 370 nm to 1.8 microns. Two-dimensional plots of reduced mobility versus kappa R (Debye parameter x liposome radius) revealed that the liposomes resuspended from a dried film of phospholipids are heterogeneous in regard to the surface charge density of individual liposomes. The described method has the potential of becoming a new tool for characterization of commercial liposomal preparations and theoretical studies.
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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 | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".