pH-Sensitive Chitosan Nanoparticles for Salivary Protein Delivery
Bibliographic record
Abstract
Salivary proteins such as histatins (HTNs) have demonstrated critical biological functions directly related to tooth homeostasis and prevention of dental caries. However, HTNs are susceptible to the high proteolytic activities in the oral environment. Therefore, pH-sensitive chitosan nanoparticles (CNs) have been proposed as potential carriers to protect proteins from enzymatic degradation at physiological salivary pH. Four different types of chitosan polymers were investigated and the optimal formulation had good batch to batch reproducibility, with an average hydrodynamic diameter of 144 ± 6 nm, a polydispersity index of 0.15 ± 0.04, and a zeta potential of 18 ± 4 mV at a final pH of 6.3. HTN3 encapsulation and release profiles were characterized by cationic polyacrylamide gel electrophoresis. The CNs successfully encapsulated HTN3 and selectively swelled at acidic pH to facilitate HTN3 release. Protection of HTN3 against enzymatic degradation was investigated in diluted whole saliva. HTN3 encapsulated in the CNs had a prolonged survival time compared to the free HTN3. CNs with and without HTN3 also successfully reduced biofilm weight and bacterial viability. The results of this study have demonstrated the suitability of CNs as potential protein carriers for oral applications, especially for complications occurring at acidic conditions.
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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".