<i>Aspergillus-Pseudomonas</i> interaction, relevant to competition in airways
Bibliographic record
Abstract
In airways of immunocompromised patients and individuals with cystic fibrosis, Pseudomonas aeruginosa and Aspergillus fumigatus are the most common opportunistic bacterial and fungal pathogens. Both pathogens form biofilms and cause acute and chronic illnesses. Previous studies revealed that P. aeruginosa is able to inhibit A. fumigatus biofilms in vitro. While numerous P. aeruginosa molecules have been shown to affect A. fumigatus, there never has been a systematic approach to define the principal causative agent. We studied 24 P. aeruginosa mutants, with deletions in genes important for virulence, iron acquisition, or quorum sensing, for their ability to interfere with A. fumigatus biofilms. Cells, planktonic or biofilm culture filtrates of four P. aeruginosa mutants, pvdD-pchE-, pvdD-, lasR-rhlR-, and lasR-, inhibited A. fumigatus biofilm metabolism or planktonic A. fumigatus growth significantly less than P. aeruginosa wild type. The common defect of these four mutants was a lack in the production of the P. aeruginosa siderophore pyoverdine. Pure pyoverdine affected A. fumigatus biofilm metabolism, and restored inhibition by the above mutants. In lungs from cystic fibrosis patients, pyoverdine production and antifungal activity correlated. The key inhibitory mechanism for pyoverdine was iron-chelation and denial of iron to A. fumigatus. Further experiments revealed a counteracting, self-protective mechanism by A. fumigatus, based on A. fumigatus siderophore production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".