Sodium Salicylate Influences the Pseudomonas aeruginosa Biofilm Structure and Susceptibility Towards Silver
Bibliographic record
Abstract
Hard-to-heal wounds are typically infected with biofilm-producing microorganisms, such as Pseudomonas aeruginosa, which strongly contribute to delayed healing. Due to the global challenge of antimicrobial resistance, alternative treatment strategies are needed. Here, we investigated whether inhibition of quorum sensing (QS) by sodium salicylate in different P. aeruginosa strains (QS-competent, QS-mutant, and chronic wound strains) influences biofilm formation and tolerance to silver. Biofilm formation was evaluated in simulated serum-containing wound fluid in the presence or absence of sodium salicylate (NaSa). Biofilms were established using a 3D collagen-based biofilm model, collagen coated glass, and the Calgary biofilm device. Furthermore, the susceptibility of 48-h-old biofilms formed by laboratory and clinical strains in the presence or absence of NaSa towards silver was evaluated by assessing cell viability. Biofilms formed in the presence of NaSa were more susceptible to silver and contained reduced levels of virulence factors associated with biofilm development than those formed in the absence of NaSa. Biofilm aggregates formed by the wild-type but not the QS mutant strain, were smaller and less heterogenous in size when grown in cultures with NaSa compared to control. These data suggest that NaSa, via a reduction of cell aggregation in biofilms, allows the antiseptic to become more readily available to cells.
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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".