Biologically Relevant Murine Models of Chronic Pseudomonas aeruginosa Respiratory Infection
Bibliographic record
Abstract
Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen and the leading cause of infection in patients with cystic fibrosis (CF). The ability of P. aeruginosa to evade host responses and develop into chronic infection causes significant morbidity and mortality. Several mouse models have been developed to study chronic respiratory infections induced by P. aeruginosa, with the bead agar model being the most widely used. However, this model has several limitations, including the requirement for surgical procedures and high mortality rates. Herein, we describe novel and adapted biologically relevant models of chronic lung infection caused by P. aeruginosa. Three methods are described: a clinical isolate infection model, utilising isolates obtained from patients with CF; an incomplete antibiotic clearance model, leading to bacterial bounce-back; and the establishment of chronic infection; and an adapted water bottle chronic infection model. These models circumvent the requirement for a surgical procedure and, importantly, can be induced with clinical isolates of P. aeruginosa and in wild-type mice. We also demonstrate successful induction of chronic infection in the transgenic βENaC murine model of CF. We envisage that the models described will facilitate the investigations of host and microbial factors, and the efficacy of novel antimicrobials, during chronic P. aeruginosa respiratory infections.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".