<i>In Vitro</i> Evaluation of Bacterial Adhesion to Dental and Stainless-Steel Surfaces
Bibliographic record
Abstract
This study aimed to describe the factors associated with biofilms formation in dental pathology by comparison of bacterial growth on dental and stainless-steel surfaces. We studied in vitro the behavior of Staphylococcus aureus Métis in order to observe the capacity of adhesion, to evaluate quantitatively the potential of proliferation and to compare the behavior of this germ in contact with the two surfaces. The biomaterials used were cylinders in Stainless steel (AISI 316L), dental fragments and stainless-steel fragments, all were disinfected for 15 minutes and then sterilized in a wet autoclave at 120˚C for 30 min. Macroscopic observation with a binocular magnifier of bacterial proliferation was carried out regularly after 6 h and 24 h of incubation. Observation by optical microscope based on GRAM staining made it possible to visualize the presence or absence of bacteria and to differentiate them. The adhesion of Staphylococcus aureus Méti S on dental fragments was compared to the one obtained on stainless steel fragments. We also carried a Bacterial count by optical dosing. The results show that the ability of this germ to colonize and develop biofilms on surfaces depends mainly on the characteristics of the surface. Rough surfaces as dental surface are more likely to developing biofilms than smooth surfaces like stainless-steel surface.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".