Evaluation of the ability of<i>C. albicans</i>to form biofilm in the presence of phage-resistant phenotypes of<i>P. aeruginosa</i>
Bibliographic record
Abstract
Pseudomonas aeruginosa and Candida albicans are disparate microbial species, but both are known to be opportunistic pathogens frequently associated with nosocomial infections. The aim of this study was to provide a better understanding of the interactions between these microorganisms in dual-species biofilms. Several bacteriophage-resistant P. aeruginosa phenotypes have been isolated and were used in dual-species mixed-biofilm studies. Twenty-four and 48 h mixed-biofilms were formed using the isolated phenotypes of phage-resistant P. aeruginosa and these were compared with similar experiments using other P. aeruginosa strains with a defined lipopolysaccharide (LPS) deficiency based on chromosomal knockout of specific LPS biosynthetic genes. Overall, the results showed that the variants of phage-resistant P. aeruginosa and LPS mutants were both less effective in inhibiting the growth of C. albicans in mixed-biofilms compared to the wild-type strains of P. aeruginosa. Conversely, the proliferation of P. aeruginosa was not influenced by the presence of C. albicans. In conclusion, the ability of strains of P. aeruginosa to inhibit the formation of a biofilm of C. albicans appears to be correlated with the LPS chain lengths of phenotypes of P. aeruginosa, suggesting that LPS has a suppressive effect on the growth of C. albicans.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".