Interspecies biofilms of<i>Pseudomonas aeruginosa</i>and<i>Burkholderia cepacia</i>
Bibliographic record
Abstract
The leading cause of morbidity and mortality in cystic fibrosis (CF) continues to be lung infections with Pseudomonas aeruginosa biofilms. Co-colonization of the lungs with P aeruginosa and Burkholderia cepacia can result in more severe pulmonary disease than P. aeruginosa alone. The interactions between P. aeruginosa biofilms and B. cepacia are not yet understood; one possible association being that mixed species biofilm formation may be part of the interspecies relationship. Using the Calgary Biofilm Device (CBD), members of all genomovars of the B. cepacia complex were shown to form biofilms, including those isolated from CF lungs. Mixed species biofilm formation between CF isolates of P. aeruginosa and B. cepacia was readily achieved using the CBD. Oxidation-fermentation lactose agar was adapted as a differential agar to monitor mixed biofilm composition. Scanning electron micrographs of the biofilms demonstrated that both species readily integrated in close association in the biofilm structure. Pseudomonas aeruginosa laboratory strain PAO1, however, inhibited mixed biofilm formation of both CF isolates and environmental strains of the B. cepacia complex. Characterization of the soluble inhibitor suggested pyocyanin as the active compound.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".