<i>Pseudomonas aeruginosa</i>biofilm infections in cystic fibrosis: insights into pathogenic processes and treatment strategies
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: CF airway mucus can be infected by opportunistic microorganisms, notably Pseudomonas aeruginosa. Once organisms are established as biofilms, even the most potent antibiotics have little effect on their viability, especially during late-stage chronic infections. Better understanding of the mechanisms used by P. aeruginosa to circumvent host defenses and therapeutic intervention strategies is critical for advancing novel treatment strategies. AREAS COVERED IN THIS REVIEW: Inflammatory injury in CF lung, role of neutrophils in pathogenesis, P. aeruginosa biofilms, mucoidy and its relationship with poor airway oxygenation, mechanisms by which P. aeruginosa biofilms in the CF airway can be killed. WHAT THE READER WILL GAIN: An understanding of the processes that P. aeruginosa undergoes during CF airway disease and clues to better treat such infections in future. TAKE HOME MESSAGE: The course of CF airway disease is a process involving host and microbial factors that often dictate frequency of pulmonary exacerbations, thus affecting the overall course. In the past decade significant discoveries have been made regarding the pathogenic processes used by P. aeruginosa to bypass the immune system. Many new and exciting features of P. aeruginosa now illuminate weaknesses in the organism that may render it susceptible to inexpensive compounds that force its own destruction.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".