Dependency of hydration and growth conditions on the mechanical properties of oral biofilms
Bibliographic record
Abstract
Within the oral cavity, dental biofilms experience dynamic environments, in part due to changes in dietary content, frequency of intake and health conditions. This can impact bacterial diversity and morpho-mechanical properties. While phenotypic properties of oral biofilms are closely related to their composition, these can readily change according to dynamic variations in the growth environment and nutrient availability. Understanding the interlink between phenotypic properties, variable growth conditions, and community characterization is an essential requirement to develop structure-property relationships in oral-biofilms. In this study, the impact of two distinct growth media types with increasing richness on the properties of oral biofilms was assessed through a new combination of in-vitro time-lapse biophysical methods with microbiological assays. Oral biofilms grown in the enriched media composition presented a decrease in their pH, an increase in soluble EPS production, and a severe reduction in bacterial diversity. Additionally, enriched media conditions presented an increase in biofilm volumetric changes (upon hydration) as well as a reduction in elastic modulus upon indentation. With hydration time considered a major factor contributing to changes in biofilm mechanical properties, we have shown that it is less associated than media richness. Future investigations can now use this time-lapse approach, with a clearer focus on the extracellular matrix of oral biofilms dictating their morpho-mechanical properties.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".