Spray-dried microparticles of encapsulated gefitinib for slow-release localized treatment of periodontal disease
Bibliographic record
Abstract
Periodontal disease (PD) can be prevented by local or systemic application of epidermal growth factor receptor inhibitors (EGFRIs) that stabilize αvβ6 integrin levels in the periodontal tissue, leading to an increase in the expression of anti-inflammatory cytokines, such as transforming growth factor-β1. Systemic EGFRIs have side effects and, therefore, local treatment of PD applied into the periodontal pockets would be preferrable. Thus, we have developed slow-release three-layered microparticles of gefitinib, a commercially available EGFRI. A combination of different polymers [cellulose acetate butyrate (CAB), Poly (D, L-lactide-co-glycolide) (PLGA) and ethyl cellulose (EC)] and sugars [D-mannose, D-mannitol and D-(+)-trehalose dihydrate] were used for the encapsulation. The optimal formulation was composed of CAB, EC, PLGA, mannose and gefitinib (0.59, 0.24, 0.09, 1, and 0.005 mg/ml, respectively; labeled CEP-gef), and created microparticles of 5.7 ± 2.3 µm in diameter, encapsulation efficiency of 99.98%, and a release rate of more than 300 h. A suspension of this microparticle formulation blocked EGFR phosphorylation and restored αvβ6 integrin levels in oral epithelial cells, while the respective control microparticles showed no effect.
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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".