The effects of chlorhexidine, povidone-iodine and vancomycin on growth and biofilms of pathogens that cause prosthetic joint infections: an in-vitro model
Bibliographic record
Abstract
BACKGROUND: Chlorhexidine gluconate (CHG) and povidone-iodine (PI) are commonly used to prevent prosthetic joint infection (PJI) during total joint replacement; however, their effective concentrations and impact on biofilms are not well defined. AIM: To determine: (1) the in-vitro minimum inhibitory concentration of CHG and PI against model PJI-causing organisms and clinical isolates; (2) their impact on biofilm formation; (3) whether there is a synergistic benefit to combining the two solutions; and (4) whether adding the antibiotic vancomycin impacts antiseptic activity. METHODS: We measured in-vitro growth and biofilm formation of Staphylococcus epidermidis, meticillin-sensitive and meticillin-resistant Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa and Candida albicans, as well as recent clinical isolates, in the presence of increasing concentrations of CHG and/or PI. Checkerboard assays were used to measure potential synergy of the solutions together and with vancomycin. FINDINGS: CHG and PI inhibited growth and biofilm formation of all model organisms tested at concentrations of 0.0004% and 0.33% or lower, respectively; highly dilute concentrations paradoxically increased biofilm formation. The solutions did not synergize with one another and acted independently of vancomycin. CONCLUSION: CHG and PI are effective at lower concentrations than typically used, establishing baselines to support further clinical trials aimed at optimizing wound disinfection. There is no synergistic advantage to using both in combination. Vancomycin is effective at inhibiting the growth of S. epidermidis and S. aureus; however, it stimulates P. aeruginosa biofilm production, suggesting in the rare case of P. aeruginosa PJI, it could exacerbate infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".