The effects of inhaled aztreonam on the cystic fibrosis lung microbiome
Bibliographic record
Abstract
BACKGROUND: Aztreonam lysine for inhalation (AZLI) is an inhaled antibiotic used to treat chronic Pseudomonas aeruginosa infection in CF. AZLI improves lung function and quality of life, and reduces exacerbations-improvements attributed to its antipseudomonal activity. Given the extremely high aztreonam concentrations achieved in the lower airways by nebulization, we speculate this may extend its spectrum of activity to other organisms. As such, we sought to determine if AZLI affects the CF lung microbiome and whether community constituents can be used to predict treatment responsiveness. METHODS: Patients were included if they had chronic P. aeruginosa infection and repeated sputum samples collected before and after AZLI. Sputum DNA was extracted, and the V3-hypervariable region of the 16S ribosomal RNA (rRNA) gene amplified and sequenced. RESULTS: improvement following initiation. No significant changes in alpha diversity were noted following AZLI. Furthermore, beta diversity demonstrated clustering with respect to patients, but had no association with AZLI use. However, we did observe a decline in the relative abundance of several individual operational taxonomic units (OTUs) following AZLI initiation suggesting that specific sub-populations of organisms may be impacted. Patients with higher abundance of Staphylococcus and anaerobic organisms including Prevotella and Fusobacterium were less likely to respond to therapy. CONCLUSIONS: Results from our study suggest potential alternate/additional mechanisms by which AZLI functions. Moreover, our study suggests that the CF microbiota may be used as a biomarker to predict patient responsiveness to therapy suggesting the microbiome may be harnessed for the personalization of therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".