Cystic fibrosis lung environment and Pseudomonas aeruginosa infection
Bibliographic record
Abstract
BACKGROUND: The airways of patients with cystic fibrosis (CF) are highly complex, subject to various environmental conditions as well as a distinct microbiota. Pseudomonas aeruginosa is recognized as one of the most important pulmonary pathogens and the predominant cause of morbidity and mortality in CF. A multifarious interplay between the host, pathogens, microbiota, and the environment shapes the course of the disease. There have been several excellent reviews detailing CF pathology, Pseudomonas and the role of environment in CF but only a few reviews connect these entities with regards to influence on the overall course of the disease. A holistic understanding of contributing factors is pertinent to inform new research and therapeutics. DISCUSSION: In this article, we discuss the deterministic alterations in lung physiology as a result of CF. We also revisit the impact of those changes on the microbiota, with special emphasis on P. aeruginosa and the influence of other non-genetic factors on CF. Substantial past and current research on various genetic and non-genetic aspects of cystic fibrosis has been reviewed to assess the effect of different factors on CF pulmonary infection. A thorough review of contributing factors in CF and the alterations in lung physiology indicate that CF lung infection is multi-factorial with no isolated cause that should be solely targeted to control disease progression. A combinatorial approach may be required to ensure better disease outcomes. CONCLUSION: CF lung infection is a complex disease and requires a broad multidisciplinary approach to improve CF disease outcomes. A holistic understanding of the underlying mechanisms and non-genetic contributing factors in CF is central to development of new and targeted therapeutic strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".