Insights into gold nanoparticles as a mucoadhesive system
Bibliographic record
Abstract
A large number of drugs are administered on different mucosal surfaces. However, due to the poor mucoadhesion of the current formulations, their bioavailability is often very low. The development of efficient mucoadhesive drug delivery systems is thus crucial for improving the performance of these drugs. The mucoadhesive properties of gold nanoparticles were investigated. First, two types of gold nanoparticles were synthesized: AuNP1 and AuNP2. AuNP1 only contain internal thiol groups on their metallic core, and AuNP2 contain both internal and peripheral thiol groups. Different protocols based on an adapted quantitative colorimetric method, UV-visible and fluorescence spectroscopies were then developed to gather information on their mucoadhesive properties. Moreover, a global correction factor for the inner filter effect in spectrofluorimetry was proposed, and the data obtained were compared to those commonly used in the literature. Mucins deeply interact with AuNP1, perturbing their core, whereas they remain at the periphery of AuNP2. The quantitative method suggests that a larger number of mucins interact with AuNP2. The establishment of this protocol could be applied to assess the mucoadhesive properties of other stable molecules. This mucoadhesive property of gold nanoparticles could be combined with their drug delivery ability in order to improve the medication administered on mucosa.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".