Self-assembled poloxamer-legumin/vicilin nanoparticles for the nanoencapsulation and controlled release of folic acid
Bibliographic record
Abstract
Plant-based food proteins are a promising choice for the preparation of nanoparticles (NPs) due to their high digestibility, low cost, and ability to interact with various compounds and nutrients. Moreover, nanoencapsulation offers a potential solution for protecting nutrients during processing and enhancing their bioavailability. This study aimed to develop and evaluate nanoparticles (NPs) based on legumin/vicilin (LV) proteins extracted from fava beans, with the goal of encapsulating and delivering a model nutraceutical compound, folic acid (FA). Specifically, NPs were self-assembled from LV proteins extracted from commercially available frozen fava beans using a pH-coacervation method with poloxamer 188 (P188) and chemically cross-linked with glutaraldehyde. Microscopy and spectroscopy studies were carried out on the empty and FA-loaded NPs in order to evaluate the particle morphology, size, size distribution, composition, mechanism of formation, impact of FA loading and release behavior. In vitro studies with Caco-2 cells also confirmed that the empty and FA-loaded nanoparticles were non-toxic. Thus, the LV-NPs are good candidates as food additives for the delivery and stabilization of nutrients as well as in drug delivery for the controlled release of therapeutics.
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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".