Advances in novel therapeutic approaches for periodontal diseases
Bibliographic record
Abstract
Periodontal diseases are pathological processes resulting from infections and inflammation affecting the periodontium or the tissue surrounding and supporting the teeth. Pathogenic bacteria living in complex biofilms initiate and perpetuate this disease in susceptible hosts. In some cases, broad-spectrum antibiotic therapy has been a treatment of choice to control bacterial infection. However, increasing antibiotic resistance among periodontal pathogens has become a significant challenge when treating periodontal diseases. Thanks to the improved understanding of the pathogenesis of periodontal disease, which involves the host immune response, and the importance of the human microbiome, the primary goal of periodontal therapy has shifted, in recent years, to the restoration of homeostasis in oral microbiota and its harmonious balance with the host periodontal tissues. This shift in therapeutic goals and the drug resistance challenge call for alternative approaches to antibiotic therapy that indiscriminately eliminate harmful or beneficial bacteria. In this review, we summarize the recent advancement of alternative methods and new compounds that offer promising potential for the treatment and prevention of periodontal disease. Agents that target biofilm formation, bacterial quorum-sensing systems and other virulence factors have been reviewed. New and exciting microbiome approaches, such as oral microbiota replacement therapy and probiotic therapy for periodontal disease, are also discussed.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".