Nanoencapsulation enhanced the performance of β-carotene for ameliorating inflammation in patient-derived organoids
Bibliographic record
Abstract
AIM: This study aims to develop a nanocarrier system for the oral delivery of β-Carotene (BC) (as a model therapeutic agent) and to test its efficacy in ameliorating inflammation in an ulcerative colitis (UC) patient-derived organoid. MATERIALS & METHODS: BC was encapsulated in a zein protein nano-cage surface-functionalized with pectin and polyethyleneglycol (PEG). The nanoencapsulated BC (nBC) was characterized for physicochemical properties (size, charge, surface chemistry) and functional properties (radical scavenging, mucoadhesion and penetration, release in simulated digestive fluids). Further, we evaluated the performance of nBC in ameliorating inflammation in Caco-2 and UC patient-derived organoid models. RESULTS: nBC achieved 75% encapsulation efficiency with improved stability and functional properties when compared to free BC. The nanocarrier was non-cytotoxic and improved mucoadhesion, mucopenetration, and the anti-inflammatory potential of BC. In UC organoids, nBC suppressed dextran sulfate sodium (DSS)-induced TNF-α and IL-8 production by approximately 70% and 31%, respectively, which was significantly higher than free BC at comparable concentrations. CONCLUSIONS: The protein-polymer nanoencapsulation strategy showed promise in protecting BC and overcoming intestinal mucus barriers for an improved anti-inflammatory effect in the organoid model. Further studies using animal models are warranted for establishing pharmacokinetics, tissue distribution, and therapeutic index of orally delivered nBC.
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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) | 0.001 | 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".