Intestinal uptake of barley protein-based nanoparticles for β-carotene delivery
Bibliographic record
Abstract
Our previous study introduced a barley protein microparticle for encapsulation of hydrophobic drug/nutraceutical, which could release nanoparticles upon gastric digestion and deliver encapsulated compound to a simulated intestinal environment intact. This work focused on evaluating the potential of liberated nanoparticles to improve the absorption of encapsulated compounds (e.g., β-carotene) using in vitro Caco-2 cell and ex vivo small intestine models. Nanoparticles obtained from gastric digestion of barley protein microparticles had a spherical shape and an average size of 351 nm. Nanoparticles showed low cytotoxicity in Caco-2 cells and their cellular uptake was dependent on time, concentration and temperature. In a Caco-2 cell monolayer model, significantly greater uptake and transport of β-carotene were observed when it was delivered by nanoparticles (15%), compared to free β-carotene suspension (2.6%). In an ex vivo rat jejunum model, nanoparticles showed the capacity to retain in small intestinal tissue. Approximately 2.24 and 6.04 μg nanoparticle were able to permeate through each cm2 intestinal tissue and translocate to the serosal side after 60 and 90 min, respectively. Results from this study demonstrated the absorption improving effect of the barley protein nanoparticles and suggested their potential as vehicles for hydrophobic compounds.
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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".