Alginate/Chitosan Particle-Based Drug Delivery Systems for Pulmonary Applications
Bibliographic record
Abstract
Cystic fibrosis (CF) is a complex, potentially life-threatening disease that is most effectively treated through the administration of antibiotics (e.g., colistimethate sodium). Chronic infection with Pseudomonas aeruginosa is one of the most significant events in the pathogenesis of cystic fibrosis, and tobramycin is the treatment of choice for those patients with chronic P. aeruginosa infection who are deteriorating despite regular administration of colistimethate sodium. Effective treatment can be challenging due to the accumulation of thickened mucus in the pulmonary environment, and here we describe the results of our investigation into the development of alginate/chitosan particles prepared via precipitation for such environments. Tobramycin loading and release from the alginate/chitosan particles was investigated, with evidence of both uptake and release of sufficient tobramycin to inhibit P. aeruginosa in vitro. Functionalisation of the alginate/chitosan particles with secretory leukocyte protease inhibitor (SLPI) was shown to help inhibit the inflammatory response associated with lung infections (via inhibition of neutrophil elastase activity) and enhance their interaction with cystic fibrosis mucus (assayed via reduction of the depth of particle penetration into the mucus) in vitro, which have prospects to enhance their efficacy in vivo.
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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 | 0.001 | 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".