Dual Coating of Liposomes as Encapsulating Matrix of Antimicrobial Peptides: Development and Characterization
Bibliographic record
Abstract
Antimicrobial peptides have been proposed as a potential biopreservatives in pharmaceutical research and agribusiness. However, many limitations hinder their utilization, such as their vulnerability to proteolytic digestion and their potential interaction with other food ingredients in complex food systems. One approach to overcome such problems is developing formulations entrapping and thereby protecting the antimicrobial peptides. Liposome encapsulation is a strategy that could be implemented to combine protection of the antimicrobial activity of the peptides from proteolytic enzymes and the controlled release of the encapsulated active ingredients. The objective of this study was to develop dual-coated food grade liposome formulations for oral administration of bacteriocins. The formulations were developed from anionic and cationic phospholipids as models of negatively and positively charged liposomes, respectively. Liposomes were prepared by the hydration of lipid films. Subsequently, the liposomes were coated with two layers comprising a biopolymer network (pectin) and whey proteins (WPI) in order to further improve their stability and enable the gradual release of the developed liposomes. Liposomes were characterized for their size, charge, molecular structure, morphology, encapsulation efficiency, and release. The results of FTIR, zeta potential, size distribution, and transmission electron microscopy (TEM) confirmed that the liposomes were efficiently coated. Ionic interactions were involved in the stabilization of the positively charged liposome formulations. Negatively charge liposome formulations were stabilized through weak interactions. The release study proved the efficiency of dual coating on the protection of liposomes against gastrointestinal digestion. This work is the first to study the encapsulation of antimicrobial peptides in dual-coated liposomes. Furthermore, the work successfully encapsulated MccJ25 in both negative and positive liposome models.
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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".