Curcumin–Induced Stabilization of Protein–Based Nano-Delivery Vehicles Reduces Disruption of Zwitterionic Giant Unilamellar Vesicles
Bibliographic record
Abstract
Curcumin-loaded native and succinylated pea protein nanoparticles, as well as zwitterionic giant unilamellar vesicles were used in this study as model bioactive compound loaded-nanoparticles and biomembranes, respectively, to assess bio-nano interactions. Curcumin-loaded native protein-chitosan and succinylated protein-chitosan complexes, as well as native protein-chitosan and succinylated protein-chitosan hollow, induced leakage of the calcein encapsulated in the giant unilamellar vesicles. The leakage was more pronounced with hollow protein-chitosan complexes. However, curcumin-loaded native protein and curcumin-loaded succinylated protein nanoparticles induced calcein fluorescence quenching. Dynamic light scattering measurements showed that the interaction of curcumin-loaded native protein, curcumin-loaded succinylated protein, native protein-chitosan, and succinylated protein-chitosan complexes with the giant unilamellar vesicles caused a major reduction in the size of the lipid vesicles. Confocal and widefield fluorescence microscopy showed rupturing of the unilamellar vesicles after treatment with native pea protein-chitosan and succinylated pea protein-chitosan complexes. The nature of interaction between the curcumin-loaded protein nanoparticles and the biomembranes, at the bio-nano interface, is influenced by the encapsulated curcumin. Findings from this study showed that, as the protein plays a crucial role in stabilizing the bioactive compound from chemical and photodegradation, the encapsulated nutraceutical stabilizes the protein nanoparticle to reduce its interaction with biomembranes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".