Antibiotic perturbation of mixed-strain Pseudomonas aeruginosa infection in patients with cystic fibrosis
Bibliographic record
Abstract
Pulmonary exacerbations in cystic fibrosis (CF) remain poorly understood and treatment is usually targeted at Pseudomonas aeruginosa . Within Australia a predominant shared P. aeruginosa strain (AUST-02) is associated with greater treatment needs. This single centre study assessed temporal shared strain population dynamics during and after antibiotic treatment of exacerbations. Sputum was collected from 12 adult patients with a history of chronic AUST-02 infection at four time-points during and after treatment of an exacerbation. Forty-eight P. aeruginosa isolates within each sample underwent AUST-02 allele-specific PCR and SNP-based strain genotyping. Various commonly shared Australian strains (AUST-01, 0.1%; AUST-02, 54.3%; AUST-06, 36.6%; AUST-07, 4.6%; AUST-11, 4.3%) and two unique strains (0.1%) were identified from 45 sputum samples (2160 isolates). Based on within-patient relative abundance of strains, a “single-strain infection” ( n = 7) or “mixed-strain infection” ( n = 5) was assigned to each patient. A significant temporal variation in the P. aeruginosa population composition was found for those with mixed-strain infection ( P < 0.001). Patients with mixed-strain infections had more long-term treatment requirements than those with single-strain infection. Moreover, despite both groups having similar lung function at study entry, patients with single-strain infection had greater improvement in FEV 1 % predicted following their exacerbation treatment ( P = 0.02). Pulmonary exacerbations may reveal multiple, unrelated P. aeruginosa strains whose relative abundance with one another may change rapidly, in a sustained and unpredictable manner.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".