Mucoadhesive cellulose Nanocrystal-chitosan nanocomposite for the delivery of hydrophobic compounds
Bibliographic record
Abstract
Cellulose nanocrystals (CNC) have received much attention as a drug delivery agent, but their hydrophilic nature hinders hydrophobic drug loading. Ionotropic gelation using CNC and chitosan (CS) can enhance the loading/encapsulation efficiency of hydrophobic compounds, improve colloidal stability, and strengthen mucoadhesion due to the cationic surface of CS. The colloidal behavior of CNC/CS nanocomposites loaded with emamectin benzoate (EMB) were elucidated by measuring particle size, zeta potential, contact angle, and morphological structure using transmission electron microscopy. The mucoadhesive properties of the nanocomposites were evaluated by viscometric and titration method, followed by testing with zebrafish mucus. A facile and reproducible protocol to synthesize mucoadhesive CNC/CS nanocomposites that can encapsulate hydrophobic drugs is demonstrated. The optimal mass ratio for the synthesis was 1:10 (CS:CNC w/w), yielding the smallest average particle size (∼200 nm), highest zeta potentials (+40 mV), and highest drug encapsulation efficiency (68.8 ± 8.7%). The steric stabilization effect of polyvinylpyrrolidone (PVP) and amphiphilic CNC stabilized the colloidal system. Importantly, the CS-coating technique enhanced the colloidal stability due to electrostatic intramolecular repulsion of the positively charged CS. CNC/CS nanocomposites exhibited enhanced mucoadhesive interaction with porcine mucin protein and live zebrafish mucus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".