The influence of substrate surface conditioning and biofilm age on the composition of <i>Enterococcus faecalis</i> biofilms
Bibliographic record
Abstract
AIM: To investigate the null hypothesis that neither the surface conditioning (collagen, serum, saliva) of hydroxyapatite (HA) discs, nor the biofilm age (3 days vs. 21 days) has a significant effect on the cellular and matrix composition of biofilms, using Enterococcus faecalis as the model organism. METHODOLOGY: Sterile HA discs were conditioned with collagen, saliva or serum, and inoculated with E. faecalis to form 3-day and 21-day-old biofilms. Unconditioned discs served as controls. The biofilms were analysed using culture-dependent and independent (confocal microscopy and biochemical analysis) methods, to determine the colony-forming units and the biofilm matrix composition (polysaccharides and proteins), respectively. Statistical analyses were performed using appropriate parametric and nonparametric tests (P = 0.05). RESULTS: Collagen conditioning significantly increased the number of CFUs in the 21-day biofilms, compared to the 3-day biofilms (P < 0.05). Although the biochemical analysis revealed that surface conditioning had no significant effect on the total carbohydrate content in the 21-day biofilms, confocal microscopic analysis revealed that collagen and saliva conditioning selectively increased the polysaccharide content of 21-day biofilms, compared to the 3-day biofilms (P < 0.05). CONCLUSIONS: The results of this study raise an important methodological concern that the substrate conditioning substances and biofilm age differentially influence the cellular and extracellular matrix components of E. faecalis biofilms.
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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) | 0.000 | 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".