An Organ System-Based Synopsis of<i>Pseudomonas aeruginosa</i>Virulence
Bibliographic record
Abstract
Driven in part by its metabolic versatility, high intrinsic antibiotic resistance, and a large repertoire of virulence factors, Pseudomonas aeruginosa is expertly adapted to thrive in a wide variety of environments, and in the process, making it a notorious opportunistic pathogen.Apart from the extensively studied chronic infection in the lungs of people with cystic fibrosis (CF), P. aeruginosa also causes multiple serious infections encompassing essentially all organs of the human body, among others, lung infection in patients with chronic obstructive pulmonary disease, primary ciliary dyskinesia and ventilator-associated pneumonia; bacteremia and sepsis; soft tissue infection in burns, open wounds and postsurgery patients; urinary tract infection; diabetic foot ulcers; chronic suppurative otitis media and otitis externa; and keratitis associated with extended contact lens use.Although well characterized in the context of CF, pathogenic processes mediated by various P. aeruginosa virulence factors in other organ systems remain poorly understood.In this review, we use an organ system-based approach to provide a synopsis of disease mechanisms exerted by P. aeruginosa virulence determinants that contribute to its success as a versatile pathogen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".