Personalized Inhaled Bacteriophage Therapy Decreases Multidrug-Resistant <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Abstract Bacteriophage therapy, which uses lytic viruses as antimicrobials, has received renewed interest to address the emerging antimicrobial resistance (AMR) crisis. Cystic fibrosis (CF), a disease complicated by recurrent P. aeruginosa pulmonary infections that cause lung function decline, is an example where AMR is already a clinical problem. While bacteria evolve bacteriophage resistance, we developed a strategy to select bacteriophages that target bacterial cell surface receptors that contribute to antibiotic resistance or virulence. Thus, in addition to killing bacteria, these phages steer surviving, evolved bacteria to antibiotic re-sensitivity or attenuated virulence. Here, we present outcomes from nine CF adults treated with nebulized bacteriophage therapy for AMR P. aeruginosa using this personalized approach. Results showed that phage therapy: 1) reduced sputum P. aeruginosa , 2) showed evidence for predicted trade-offs in most subjects, and 3) improved lung function, which may reflect the combined effects of decreased bacterial sputum density and phage-driven evolved trade-offs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".