Metal Ion-Releasing Glass Particles to Enhance Antibiotic Efficacy Against Cystic Fibrosis Infection
Bibliographic record
Abstract
Abstract Cystic fibrosis is characterized by thickened airway mucus that impairs mucociliary clearance and promotes persistent bacterial infections. These chronic infections, primarily caused by Pseudomonas aeruginosa and Staphylococcus aureus , lead to progressive lung damage and respiratory failure, which are the leading causes of mortality in cystic fibrosis patients. Although antibiotics remain the cornerstone of treatment for such airway infections, incomplete bacterial eradication often contributes to the emergence of antibiotic resistance. This study explores whether the efficacy of conventional antibiotics can be enhanced through co-delivery with antibacterial metal ions released from borate-based bioactive glasses. To evaluate this combination therapy, we developed an in vitro airway infection model that replicates key features of the cystic fibrosis airway environment. The model incorporates a layer of bronchial epithelial cells, a mucus-like hydrogel, and bacteria deposited using an aqueous two-phase system. Multiple bioactive glass formulations were evaluated for their ability to augment antibiotic activity and enhance bacterial eradication. The results demonstrated additive or synergistic antibacterial effects against P. aeruginosa and S. aureus , while maintaining mammalian cell viability. These findings suggest that metal ion-antibiotic combination therapies delivered via bioactive glasses may improve treatment outcomes for cystic fibrosis-related infections and reduce reliance on high-dose or prolonged antibiotic regimens. Such approaches hold promise not only for cystic fibrosis patients but also for broader clinical applications where antibiotic resistance is a growing concern.
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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".