Green Alternatives as Antimicrobial Agents in Mitigating Periodontal Diseases: A Narrative Review
Bibliographic record
Abstract
Periodontal diseases and dental caries are the most common infectious oral diseases impacting oral health globally. Oral cavity health is crucial for enhancing life quality since it serves as the entranceway to general health. The oral microbiome and oral infectious diseases are strongly correlated. Gram-negative anaerobic bacteria have been associated with periodontal diseases. Due to the shortcomings of several antimicrobial medications frequently applied in dentistry, the lack of resources in developing countries, the prevalence of oral inflammatory conditions, and the rise in bacterial antibiotic resistance, there is a need for reliable, efficient, and affordable alternative solutions for the prevention and treatment of periodontal diseases. Several accessible chemical agents can alter the oral microbiota, although these substances also have unfavorable symptoms such as vomiting, diarrhea, and tooth discoloration. Natural phytochemicals generated from plants that have historically been used as medicines are categorized as prospective alternatives due to the ongoing quest for substitute products. This review concentrated on phytochemicals or herbal extracts that impact periodontal diseases by decreasing the formation of dental biofilms and plaques, preventing the proliferation of oral pathogens, and inhibiting bacterial adhesion to surfaces. Investigations examining the effectiveness and safety of plant-based medicines have also been presented, including those conducted over the past decade.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".