Characterization of an ester-based core-multishell (CMS) nanocarrier for the topical application at the oral mucosa
Bibliographic record
Abstract
OBJECTIVES: Topical drug administration is commonly applied to control oral inflammation. However, it requires sufficient drug adherence and a high degree of bioavailability. Here, we tested the hypothesis whether an ester-based core-multishell (CMS) nanocarrier is a suitable nontoxic drug-delivery system that penetrates efficiently to oral mucosal tissues, and thereby, increase the bioavailability of topically applied drugs. MATERIAL AND METHODS: To evaluate adhesion and penetration, the fluorescence-labeled CMS 10-E-15-350 nanocarrier was applied to ex vivo porcine masticatory and lining mucosa in a Franz cell diffusion assay and to an in vitro 3D model. In gingival epithelial cells, potential cytotoxicity and proliferative effects of the nanocarrier were determined by MTT and sulphorhodamine B assays, respectively. Transepithelial electrical resistance (TEER) was measured in presence and absence of CMS 10-E-15-350 using an Endohm-12 chamber and a volt-ohm-meter. Cellular nanocarrier uptake was analyzed by laser scanning microscopy. Inflammatory responses were determined by monitoring pro-inflammatory cytokines using real-time PCR and ELISA. RESULTS: CMS nanocarrier adhered to mucosal tissues within 5 min in an in vitro model and in ex vivo porcine tissues. The CMS nanocarrier exhibited no cytotoxic effects and induced no inflammatory responses. Furthermore, the physical barrier expressed by the TEER remained unaffected by the nanocarrier. CONCLUSIONS: CMS 10-E-15-350 adhered to the oral mucosa and adhesion increased over time which is a prerequisite for an efficient drug release. Since TEER is unaffected, CMS nanocarrier may enter the oral mucosa transcellularly. CLINICAL RELEVANCE: Nanocarrier technology is a novel and innovative approach for efficient topical drug delivery at the oral 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 | 0.000 |
| 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 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".