Prevalence and impact of <i>oprD</i> mutations in <i>Pseudomonas aeruginosa</i> strains in cystic fibrosis
Bibliographic record
Abstract
ABSTRACT Defective OprD porins contribute to carbapenem resistance and may be important in Pseudomonas aeruginosa adaptation to cystic fibrosis airways. It is unclear whether oprD mutations are fixed in populations of shared strains that are transmitted between patients or whether novel variants arise during infection. We investigated oprD sequences and antimicrobial resistance of two common Australian shared strains, constructed P. aeruginosa mutants with the most common oprD allelic variants and compared characteristics between patients with or without evidence of infection with strains harbouring these variants. Our data show that three independently acquired nonsense mutations arising from a 1-base pair substitution are fixed in strain sub-lineages. These nonsense mutations are likely to contribute to reduced carbapenem susceptibility in the sub-lineages without compromising in vitro fitness. Not only was lung function worse among patients infected with strains harbouring the nonsense mutations than those without, but they also had an increased hazard rate of lung transplantation/death. Our findings further highlight that understanding adaptive changes may help to distinguish patients with greater adverse outcomes despite infection with the same strain.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".