Development of Gelled-Oil Nanoparticles for the Encapsulation and Release of Berberine
Bibliographic record
Abstract
In this study, a new drug carrier based on gelled-oil nanoparticles (GNPs) was designed and synthesized for the encapsulation and release of the model hydrophobic drug, berberine chloride (BCl). Two compositions with different oil phases were examined, sesame oil (SO) and cinnamaldehyde (Cin), which were emulsified with water, stabilized with Tween 80 (Tw80), and gelled using an N-alkylated primary oxalamide low-molecular-weight gelator (LMWG) to give stable dispersions of GNPs between 100 and 200 nm in size. The GNP formulation with Cin was significantly favored over SO due to (1) lower gel melting temperatures, (2) higher gel mechanical strength, and (3) significantly higher solubility, encapsulation efficiency, and loading of BCl. Also, the solubility and loading of BCl in Cin were significantly increased (at least 7-fold) with the addition of cinnamic acid. In vitro release studies showed that the release of BCl from the GNPs was independent of gelator concentration and lower than that for BCl solution and the corresponding nanoemulsion (no LWMG). Also, cell internalization studies suggested that the N-alkylated primary oxalamide LMWG did not interfere with the internalization efficiency of BCl into mouse mast cells. Altogether, this work demonstrates the potential use of these new GNP formulations for biomedical studies involving the encapsulation of drugs and nutraceuticals and their controlled release.
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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".