Toward Modern Inhalational Bacteriophage Therapy: Nebulization of Bacteriophages of <i>Burkholderia cepacia</i> Complex
Bibliographic record
Abstract
Antibiotic-resistant bacterial infections have renewed interest in finding substitute methods of treatment. The purpose of the present in vitro study was to investigate the possibility of respiratory delivery of a Burkholderia cepacia complex (BCC) bacteriophage by nebulized aerosol administration. Bacteriophages in isotonic saline were aerosolized with Pari LC star and eFlow nebulizers, at titers with mean value (standard deviation) of 2.15 x 10(8) (1.63 x 10(8)) plaque-forming unit (PFU)/mL in 2.5-mL nebulizer fills. The breathing pattern of an adult was simulated using a pulmonary waveform generator. During breath simulation, the size distributions of the nebulized aerosol were measured using phase doppler anemometry (PDA). Efficiency of nebulizer delivery was subsequently determined by collection of aerosol on low resistance filters and measurement of bacteriophage titers. These filter titers were used as input data to a mathematical lung deposition model to predict regional deposition of bacteriophages in the lung and initial bacteriophage titers in the liquid surface layer of each conducting airway generation. The results suggest that BCC bacteriophages can be nebulized successfully within a reasonable delivery time and predicted titers in the lung indicate that this method may hold potential for treatment of bacterial lung infections common among cystic fibrosis patients.
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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 | 0.001 | 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".