Analysis of peptide and lipopeptide content in liposomes.
Bibliographic record
Abstract
PURPOSE: To evaluate several methods for extraction of peptides from liposomal formulations as a first step in their quantification, and to determine the encapsulation efficiency for a panel of 8 peptides. METHODS: Eight peptides were chosen due to their importance in the field of vaccine development. Three different extraction media were examined: 25% ethanol, 98% ethanol, and 100% methanol. After extraction from liposomes, peptide content was measured using reverse phase HPLC. RESULTS: The effectiveness of the extraction media for peptide recovery varied considerably for the different peptides studied. In general, more hydrophilic peptides were recovered to a greater extent using 25% ethanol while more hydrophobic peptides were more thoroughly recovered using 98% ethanol. Encapsulation efficiencies (EE) ranged from 1% to 99% for the different peptides. No strong correlation was found between the average hydrophobicity values for the peptides and their EE. CONCLUSIONS: The most effective solvent for the extraction of a peptide from liposomes depends on the physicochemical properties of the peptide. Although the peptide sequence characteristics may provide guidance on the choice of the extraction media, only peptide recovery experiments will be able identify the optimal medium for extraction.
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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 | 0.001 |
| 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 | 0.000 | 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".