Question · asked by 1 review
Postbiotics from Lactobacillus spp. (cell-free supernatants, biosurfactants, lipoteichoic acid, bacterial lysates) vs No prior SDF application (untreated dentin control): Downregulation of S. mutans virulence/biofilm gene expression (gtfB, gtfC, ftf)
- Population
- Cariogenic bacteria (mainly Streptococcus mutans reference strains) and oral biofilms in vitro
- Intervention
- Postbiotics from Lactobacillus spp. (cell-free supernatants, biosurfactants, lipoteichoic acid, bacterial lysates)
- Comparison
- No prior SDF application (untreated dentin control)
- Outcome
- Downregulation of S. mutans virulence/biofilm gene expression (gtfB, gtfC, ftf)
Postbiotics reduce expression of S. mutans biofilm/virulence genes (gtfB, gtfC, ftf) in vitro (ungraded narrative synthesis).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Postbiotics from Lactobacillus spp. (cell-free supernatants, biosurfactants, lipoteichoic acid, bacterial lysates) vs Untreated control Downregulation of S. mutans virulence/biofilm gene expression (gtfB, gtfC, ftf) · Cariogenic bacteria (mainly Streptococcus mutans reference strains) and oral biofilms in vitro · Exposure 24 h to 7 days, Laboratory (in vitro) Postbiotics reduce expression of S. mutans biofilm/virulence genes (gtfB, gtfC, ftf) in vitro (ungraded narrative synthesis). Postbiotics and Dental Caries: A Systematic Review. (2025) All outcomes | Postbiotics downregulated expression of S. mutans virulence and exopolysaccharide-synthesis genes (e.g., gtfB, gtfC, gtfD, ftf) in 10 in vitro studies; no pooled estimate. | 8 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Postbiotics and Dental Caries: A Systematic Review. 2025 All outcomes | October 2024 | 21 | Critically Low / Critically Low |
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.