Quantification of initial adhesion of Enterococcus faecalis to medical grade polymers using a DNA-based fluorescence assay
Bibliographic record
Abstract
This paper reports on the use of a DNA-based fluorescence assay to study and quantify the initial interactions of the uropathogen Enterococcus faecalis with different polymers commonly used for the fabrication of medical devices and implants, including polyurethane (PU), silicone (SI), high-density polyethylene (HDPE), polyamide (PA), poly(methyl methacrylate) (PMMA) and polytetrafluoroethylene (PTFE). To follow the kinetics of E. faecalis adhesion, polymer samples were incubated in bacterial solution for various times and the relative concentration of adhered bacteria was obtained using two methods: commonly used CFU plate counting and a DNA quantification assay. Results obtained from DNA-based fluorescence assays showed that E. faecalis adhesion on PU is 3-times higher than that on PTFE following exposure to bacteria for 180 min. Neither surface wettability nor surface roughness of the studied polymers was found to correlate with E. faecalis adhesion, suggesting the involvement of much more complex adhesion mechanisms of bacteria onto surfaces. SEM micrographs of adhered bacteria illustrated that adhesion was different depending on the type of polymeric substrate: adhesion on PU samples was characterized by the aggregation of bacterial cells in dense clusters, as well as by the presence of fimbriae between cells and the substrate, which could explain the high adhesion to PU compared to the other polymers. This work demonstrated that the bacterial adhesion to polymers occurs at an early stage of the contact and suggests that the initial adhesion stage should be controlled, in order to prevent subsequent biofilm formation and, thus, reduce the risk of implant-associated infections.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".