Quorum sensing mechanisms in Pseudomonas aeruginosa biofilm formation on medical devices
Bibliographic record
Abstract
What enables Pseudomonas aeruginosa to colonize medical devices so effectively, and can disrupting its communication network prevent biofilm-related infections? This research examined quorum sensing (QS) pathways—specifically the las and rhl signaling circuits—that regulate biofilm development on catheter surfaces, prosthetic joint material, and silicone tubing. Bacterial isolates were recovered from device-associated infections at the Boreal University of Ecology Medical Microbiology Laboratory in Thunder Bay, Ontario, between March 2023 and January 2024. Crystal violet staining quantified biofilm biomass at six time points (6, 12, 24, 48, 72, and 96 hours), while acyl-homoserine lactone (AHL) concentrations were measured by liquid chromatography-mass spectrometry. Results showed that silicone tubing supported the highest biofilm biomass (OD570 = 1.93±0.11 at 96 h), followed by catheter (1.71±0.09) and prosthetic joint surfaces (1.52±0.13). AHL levels rose sharply between 12 and 48 hours, paralleling the exponential phase of biofilm accumulation. Targeted knockdown of lasI using antisense peptide nucleic acids reduced biofilm formation by 63.8% on silicone and 57.2% on catheter material. The rhl system showed a secondary but additive effect, with combined las/rhl inhibition achieving up to 78.4% reduction. These findings suggest that QS interference may serve as a viable anti-biofilm strategy for device-related P. aeruginosa infections, especially when both signaling systems are targeted together.
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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 | 0.000 |
| 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".