Pseudomonas aeruginosa in Chronic Lung Infections: How to Adapt Within the Host?
Bibliographic record
Abstract
Bacteria that readily adapts to different natural environments, can also exploit this versatility upon infection of the host to persist. Pseudomonas aeruginosa, a ubiquitous Gram-negative bacterium, is harmless to healthy individuals, and yet a formidable opportunistic pathogen in compromised hosts. When pathogenic, P. aeruginosa causes invasive and highly lethal disease in certain compromised hosts. In others, such as individuals with the genetic disease cystic fibrosis, this pathogen causes chronic lung infections which persist for decades. During chronic lung infections, P. aeruginosa evolves and adapts to the host environment towards a state of reduced bacterial invasiveness that favours bacterial persistence without causing overwhelming host injury. Host responses to chronic P. aeruginosa infections are complex and dynamic, ranging from vigorous activation of innate immune responses that are ineffective at eradicating the infecting bacteria, to relative host tolerance and dampened activation of host immunity. This review will examine how P. aeruginosa subverts host defenses and modulates immune and inflammatory responses during chronic infection. This dynamic interplay between host and pathogen is a major determinant in the pathogenesis of chronic P. aeruginosa lung infections.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".