Animal models of chronic lung infection with<i>Pseudomonas aeruginosa</i>: useful tools for cystic fibrosis studies
Bibliographic record
Abstract
Cystic fibrosis (CF) is caused by a defect in the transmembrane conductance regulator (CFTR) protein that functions as a chloride channel. Dysfunction of the CFTR protein results in salty sweat, pancreatic insufficiency, intestinal obstruction, male infertility and severe pulmonary disease. In most patients with CF life expectancy is limited due to a progressive loss of functional lung tissue. Early in life a persistent neutrophylic inflammation can be demonstrated in the airways. The cause of this inflammation, the role of CFTR and the cause of lung morbidity by different CF-specific bacteria, mostly Pseudomonas aeruginosa, are not well understood. The lack of an appropriate animal model with multi-organ pathology having the characteristics of the human form of CF has hampered our understanding of the pathobiology and chronic lung infections of the disease for many years. This review summarizes the main characteristics of CF and focuses on several available animal models that have been frequently used in CF research. A better understanding of the chronic lung infection caused particularly by P. aeruginosa, the pathophysiology of lung inflammation and the pathogenesis of lung disease necessitates animal models to understand CF, and to develop and improve treatment.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".