Natural Emulgel from Dialdehyde Cellulose for Lipophilic Drug Delivery
Bibliographic record
Abstract
In this study, we develop a cellulosic emulgel based on dialdehyde cellulose (DAC) cross-linked with chitosan using an embedded emulsion for the delivery of lipophilic compounds. First, a surfactant-free oil-in-water emulsion was prepared by ultrasonication, using DAC as an interfacial stabilizer. Then, by adding chitosan to the emulsion, the aldehyde groups of DAC chemically reacted with the amine groups of chitosan via a Schiff base reaction to form a cross-linked emulgel, which was confirmed by nuclear magnetic resonance and Fourier transform infrared spectroscopy. The developed emulgel with a larger surface area (∼81 m 2 g –1 ) swells more slowly compared to its hydrogel homologue (∼5 m 2 g –1 ). Scanning electron microscopy imaging along with Barrett, Joyner and Halenda method results indicated the formation of a homogenized structural matrix of micro- and nanosized pores. β-Carotene was loaded in the oil phase, and its release was measured using a static orogastrointestinal method. It was shown that after safely passing the oral processing, only ∼20% of β-carotene is released in the stomach, while a gradual increase in β-carotene release, up to 50%, is observed during a 4-h stay in the intestines. This emulgel, rationally processed via safe and green strategies, is a promising candidate for the delivery of lipophilic compounds, specifically for the purpose of oral administration.
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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".