Core-shell gelatin-chitosan nanoparticles with lysozyme responsiveness formed via pH-drive and transglutaminase cross-linking
Bibliographic record
Abstract
Lysozyme-responsive nanoparticles were fabricated using a hydrophilic protein (gelatin type A) as the core and a hydrophobic polysaccharide (chitosan) as the shell. In this study, curcumin was used as a model molecule for encapsulation and promoted the aggregation of gelatin nanoparticles. Transglutaminase catalyzed both intra-molecular cross-linking within gelatin and inter-molecular cross-linking between gelatin and chitosan. The formation mechanism of gelatin nanoparticles was investigated by molecular docking simulations, circular dichroism spectroscopy, UV-vis spectroscopy, turbidity analysis, and dynamic light scattering. Results indicated that pH-driven processes can induce molecular conformational changes of gelatin. However, these alone are insufficient to induce nanoparticle formation. Hydrogen bonding, Pi-alkyl interactions, Pi-Pi interactions, and van der Waals forces between gelatin and curcumin are crucial for the core formation. The coating mechanism of chitosan involved covalent bonds catalyzed by transglutaminase and electrostatic interactions, verified by dynamic light scattering and Fourier transform infrared spectroscopy. Physicochemical properties characterization revealed that the core-shell nanoparticles exhibited a maximum encapsulation efficiency of 97.2 ± 0.3 % and an average particle size of 120 ± 21 nm. The core-shell nanoparticles exhibited high thermal and pH stability, with curcumin retention rates exceeding 80 % under acidic, neutral, and weakly alkaline conditions, and detained thermal degradation up to 90 °C. Additionally, lysozyme responsiveness was evaluated by controlled curcumin release with varying lysozyme concentrations, through which enzymatic hydrolysis of chitosan by lysozyme triggered an increased release rate. In summary, core-shell nanoparticles synthesized from gelatin and chitosan may be effective target delivery systems for curcumin.
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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 | 0.000 | 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 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".