Recent advances in dental biofilm: impacts of microbial interactions on the biofilm ecology and pathogenesis
Bibliographic record
Abstract
The human oral cavity is a complex ecosystem harboring hundreds species of microbes that are largely living on the tooth surfaces as dental biofilms. Most microbes in dental biofilms promote oral health by stimulating the immune system or by preventing invasion of pathogens. Species diversity, high cell density and close proximity of cells are typical of life in dental biofilms, where microbes interact with each other and develop complex interactions that can be either competitive or cooperative. Competition between species is a well-recognized ecological force to drive microbial metabolism, species diversity and evolution. However, it was not until recently that microbial cooperative activities are also recognized to play important roles in microbial physiology and ecology. Importantly, these interactions profoundly affect the overall biomass, function, diversity and the pathogenesis in dental biofilms. It is now recognized that every human body contains a personalized oral microbiome that is essential to maintaining the oral health. Remarkably, the indigenous species in dental biofilms often maintain a relatively stable and harmless relationship with the host, despite regular exposure to environmental perturbations and the host defense factors. Such stability or homeostasis results from a dynamic balance of microbial-microbial and microbial-host interactions. Under certain circumstances, however, the homeostasis may breakdown, predisposing a site to diseases. In this review, we describe several examples of microbial interactions and their impacts on the homeostasis and pathogenesis of dental biofilms. We hope to encourage research on microbial interactions in the regulation of the homeostasis in 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".