Co-existence of Citrobacter freundii exacerbated Pseudomonas aeruginosa infection in vivo
Bibliographic record
Abstract
The presence of bacterial species other than the pathogen at infection site can affect the progression of a bacterial infection. Based on the fact that Citrobacter freundii can coexist during Pseudomonas aeruginosa infection, this study aims to investigate the impact of the co-existing C. freundii on the pathogenesis of P. aeruginosa infection. A murine peritonitis model was used to compare the mortality rates and histopathology of P. aeruginosaPAO1 infection in the presence and absence of a C. freundii clinical isolate C9. We also investigated the intercellular interaction between PAO1 and C9 by examining pyocyanin production and comparing gene expression levels. The results demonstrate that co-infection with C9 significantly increased the mortality rate and tissue damages in PAO1 infected mice. At an inoculum of 106 CFU, no mortality was observed in the C9 infected group at three days post-infection, whereas the mortality rate in the PAO1-C9 co-infection group was 64%, compared with 24% in the PAO1 infected group. Pyocyanin production in P. aeruginosa PAO1 increased 8 folds approximately in the presence of C. freundii C9, and operons associated with phenazine synthesis, phzA1 and phzA2, were also upregulated. Disruption of the phzA1 and phzA2 eliminated the exacerbated pathogenicity in the co-infection group, indicating that the elevated pyocyanin production was the main contributing factor. The results suggest that co-existing C. freundii during P. aeruginosa infection can exacerbate the pathogenicity, which may have significant implications in patients infected with these bacteria.
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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) | 0.001 | 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 | 0.001 | 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".