Optimized Chitosan-Based Nanoemulsion Improves Luteolin Release
Bibliographic record
Abstract
Luteolin (LUT) is a flavonoid found in several edible and medicinal plants. It is recognized for its biological activities such as antioxidant, anti-inflammatory, neuroprotective, and antitumor effects. However, the limited water solubility of LUT leads to poor absorption after oral administration. Nanoencapsulation may improve the solubility of LUT. Nanoemulsions (NE) were selected for the encapsulation of LUT due to their biodegradability, stability, and ability to control drug release. In this work, chitosan (Ch)-based NE was developed to encapsulate luteolin (NECh-LUT). A 23 factorial design was built to obtain a formulation with optimized amounts of oil, water, and surfactants. NECh-LUT showed a mean diameter of 67.5 nm, polydispersity index 0.174, zeta potential of +12.8 mV, and encapsulation efficiency of 85.49%. Transmission electron microscopy revealed spherical shape and rheological analysis verified the Newtonian behavior of NECh-LUT. SAXS technique confirmed the bimodal characteristic of NECh-LUT, while stability analysis confirmed NECh-LUT stability when stored at room temperature for up to 30 days. Finally, in vitro release studies showed LUT controlled release up to 72 h, indicating the promising potential of NECh-LUT to be used as novel therapeutic option to treat several disorders.
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".