Phenotypic Heterogeneity of Pseudomonas aeruginosa Populations in a Cystic Fibrosis Patient
Bibliographic record
Abstract
The opportunistic pathogen Pseudomonas aeruginosa chronically infects the lower airways of patients with cystic fibrosis. Throughout the course of infection this organism undergoes adaptations that contribute to its long-term persistence in the airways. While P. aeruginosa diversity has been documented, it is less clear to what extent within-patient diversity contributes to the overall population structure as most studies have been limited to the analysis of only a few isolates per patient per time point. To examine P. aeruginosa population structure in more detail we collected multiple isolates from individual sputum samples of a patient chronically colonized with P. aeruginosa. This strain collection, comprised of 169 clonal isolates and representing three pulmonary exacerbations as well as clinically stable periods, was assayed for a wide selection of phenotypes. These phenotypes included colony morphology, motility, quorum sensing, protease activity, auxotrophy, siderophore levels, antibiotic resistance, and growth profiles. Each phenotype displayed significant variation even within isolates of the same colony morphotype from the same sample. Isolates demonstrated a large degree of individuality across phenotypes, despite being part of a single clonal lineage, suggesting that the P. aeruginosa population in the cystic fibrosis airways is being significantly under-sampled.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".