Elaboration and characterization of barley protein nanoparticles as an oral delivery system for lipophilic bioactive compounds
Bibliographic record
Abstract
This is the first report in which barley protein nanoparticles were prepared with the aim of developing a delivery system for lipophilic bioactive compounds at ambient temperature using high pressure homogenization. No organic solvents or crosslinking reagents were involved in the nanoparticle preparation. Effects of processing conditions and formulae on particle size and size distribution were investigated. Optimal nanoparticles with regular spherical shape, small size (90-150 nm) and narrow size distribution (PDI < 0.3) could be achieved at a protein weight concentration of up to 5% when the oil/protein ratio was maintained within a range of 1 to 1.5. These nanoparticles exhibited high zeta-potential (about -35 mV), high payload (51.4-54.5%) and good stability without the use of surfactants. As shown by the release test, though the bulk protein matrices of nanoparticles were degraded in the simulated gastric tract, even smaller nanoparticles were released and bioactive compounds were protected by a layer of barley protein. Then, complete release occurred in the simulated intestinal environments due to pancreatin degradation. In vitro studies showed that barley protein nanoparticles are relatively safe and could be internalized by Caco-2 cells and accumulated in the cytoplasm.
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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".