Extraction and purification of phosphatidylcholine and its potential in nanoliposomal delivery of <scp><i>Eucalyptus citriodora</i></scp> oil
Bibliographic record
Abstract
Abstract Phosphatidylcholine (PC) possesses amphiphilic characteristics to form vesicles or liposome nanoparticles and can be utilized to deliver essential nutrients such as proteins, peptide antigens, and essential fatty acids. In this study, an attempt has been made to obtain purified PC and evaluate its potential in nanoliposome synthesis and its corresponding drug release profile. In this regard, four physical separation techniques comprising extraction, precipitation, static, and dynamic adsorption were assessed and applied to purify PC from soybean lecithin. Different solvents and the ratio of lecithin to solvent were used to achieve the highest PC percentage. The results of an HPLC test showed that the maximum rate of PC extracted was 69%, through combining all four stages. In the next step, the synthesis of nanoliposomes from purified PC was carried out by the sonication method. Eucalyptus citriodora oil was used to evaluate the potential of the resultant nanoliposomes to encapsulate hydrophobic antibacterial drugs. The morphology and size distribution of nanoliposomes were investigated by dynamic light scattering (DLS) and atomic force microscopy (AFM) analysis. The long‐term stability of antibacterial‐loaded nanoliposomes was confirmed by their release behaviour patterns. The data from DLS and AFM resulted in narrow size distribution, with an average diameter of 201.23 and 197.22 nm, respectively. Compared to non‐loaded nanoparticles, the results showed a slight increase in the size of oil‐loaded nanoliposomes. The release profile of the encapsulated drug resulted in sustained release behaviour during eight days of storage in phosphate saline buffer.
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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) | 0.000 | 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 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".