Question · asked by 1 review

Antibacterial S. mutans reduction only in Farzanegan (chitosan+TiO2); Mollabashi (TiO2-only) showed no significant antibacterial effect - not attributable to TiO2 alone vs Conventional/control orthodontic adhesive (e.g., Transbond XT) or negative control: Streptococcus mutans count

Population
Patients undergoing fixed orthodontic (fixed appliance) treatment (adolescents and adults, ages 12 to 35)
Intervention
Antibacterial S. mutans reduction only in Farzanegan (chitosan+TiO2); Mollabashi (TiO2-only) showed no significant antibacterial effect - not attributable to TiO2 alone
Comparison
Conventional/control orthodontic adhesive (e.g., Transbond XT) or negative control
Outcome
Streptococcus mutans count

The antibacterial (S. mutans) effect of TiO2 nanoparticles was conflicting across two RCTs (ungraded).

What each review found

Include earlier review versions
FindingEffectStudiesGRADE certainty
Antibacterial S. mutans reduction only in Farzanegan (chitosan+TiO2); Mollabashi (TiO2-only) showed no significant antibacterial effect - not attributable to TiO2 alone vs Conventional adhesive/composite (Transbond XT)
Antibacterial effect (Streptococcus mutans count, real-time PCR) · Patients undergoing fixed orthodontic (fixed appliance) treatment (adolescents and adults, ages 12 to 35) · 1 day to 6 months, Orthodontic clinical practice (in-vivo human clinical trials); adolescents and adults, Antibacterial (S. mutans)
The antibacterial (S. mutans) effect of TiO2 nanoparticles was conflicting across two RCTs (ungraded).
Can Orthodontic Adhesive Systems Inhibit the Formation and Development of White Spot Lesions During Fixed… (2024) All outcomes
Two RCTs gave conflicting antibacterial results: Farzanegan 2021 reported a significant decrease in S. mutans with the chitosan plus TiO2 adhesive (p=0.01 lateral incisors; p=0.001 second premolars); Mollabashi 2022…2 studies · 64 participantsGRADE not reported

Findings are shown side by side, never pooled across reviews.

The reviews

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.