Pseudomonas aeruginosa las and rhl quorum-sensing systems are important for infection and inflammation in a rat prostatitis model
Bibliographic record
Abstract
Pseudomonas aeruginosa frequently acts as an opportunistic pathogen of mucosal surfaces; yet, despite causing aggressive prostatitis in some men, its role as a pathogen in the prostate has not been investigated. Consequently, we developed a Ps. aeruginosa infection model in the rat prostate by instilling wild-type (WT) Ps. aeruginosa strain PAO1 into the rat prostate. It was found that Ps. aeruginosa produced acute and chronic infections in this mucosal tissue as determined by bacterial colonization, gross morphology, tissue damage and inflammatory markers. WT strain PAO1 and its isogenic mutant PAO-JP2, in which both the lasI and rhlI quorum-sensing signal systems have been silenced, were compared during both acute and chronic prostate infections. In acute infections, bacterial numbers and inflammatory markers were comparable between WT PA01 and PAO-JP2; however, considerably less tissue damage occurred in infections with PAO-JP2. Chronic infections with PAO-JP2 resulted in reduced bacterial colonization, tissue damage and inflammation as compared to WT PAO1 infections. Therefore, the quorum-sensing lasI and rhlI genes in Ps. aeruginosa affect acute prostate infections, but play a considerably more important role in maintaining chronic infections. We have thus developed a highly reproducible model for the study of Ps. aeruginosa virulence in the prostate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".