Investigation of probiotic bacteria as dental caries and periodontal disease biotherapeutics
Bibliographic record
Abstract
Oral diseases, specifically dental caries and periodontal disease, are characterised by increases in pathogenic microorganisms, increased demineralisation and increased inflammation and levels of inflammatory markers. Despite the therapeutic strategies, oral diseases have elevated prevalence rates. Recent work has demonstrated that probiotic bio-therapeutics can decrease oral pathogen counts, including caries-causing Streptococcus mutans and oral inflammation. The aim of this work was to investigate putative probiotic bacteria, selected for S. mutans inhibition and for their oral health-promoting characteristics. The probiotic bacteria were screened for S. mutans inhibition, probiotic bacteriocin activity, salivary pH modulation, probiotic nutrient (sucrose) competition, probiotic co-aggregation with S. mutans, bacterial attachment to oral epithelial keratinocytes, bacterial nitric oxide production and bacterial antioxidant activity. The results indicate that Lactobacillus reuteri strains NCIMB 701359, NCIMB 701089, NCIMB 702655 and NCIMB 702656 inhibited S. mutans to non-detectable levels (<10 cfu/ml). L. reuteri strains also demonstrated the highest antioxidant capacity of the tested strains (7.73-13.99 µM Trolox equivalents), suggesting their use as both caries and periodontal disease therapeutics. Although Lactobacillus fermentum NCIMB 5221 inhibited S. mutans at lower levels, it significantly buffered the pH (4.18) of saliva containing S. mutans, co-aggregated with S. mutans (10.09%), demonstrated high levels of sucrose consumption (138.11 mM) and successfully attached to gingival epithelial cells (11%). This study identified four L. reuteri strains and one L. fermentum strain to be further investigated as oral disease biotherapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".