Microcolony formation: a novel biofilm model of Pseudomonas aeruginosa for the cystic fibrosis lung
Bibliographic record
Abstract
Pseudomonas aeruginosa colonizing the lung of cystic fibrosis patients is responsible for a decline in health and poor prognosis for these patients. Once established, growth of P. aeruginosa in microcolonies makes it very difficult to eradicate the organisms by antimicrobial treatment. An artificial sputum medium was developed to mimic growth of P. aeruginosa in the cystic fibrosis lung habitat and it was found that the organisms grew in tight microcolonies attached to sputum components. Several genes, such as algD, oprF and lasR but not fliC, were required for tight microcolony formation. Among the sputum components, amino acids, lecithin, DNA, salt and low iron were required for tight microcolony formation. Amino acids were also shown to be responsible for various other cystic-fibrosis-specific phenotypes of P. aeruginosa, such as diversification of colony morphology, alterations in LPS structure and hyperexpression of OprF. Since the amino acid content of sputum is elevated in severe lung disease, it is suggested that the tight microcolony biofilm is maintained in these conditions and that they contribute to the vicious cycle of disease severity and failure to eradicate the organism. Thus, growth of P. aeruginosa in artificial sputum medium is an appropriate model of chronic lung colonization and may be useful for evaluating therapeutic procedures and studying antibiotic-resistance mechanisms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".