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Effect of Photodynamic Therapy with Four Light-Sensitive Materials on the Bond Strength of Fiber Posts to Root Dentin.

Sedighe Sadat HashemikamangarShadi PourahmadiNasim Chiniforush
Published in: Journal of lasers in medical sciences (2024)
Introduction: Fiber-reinforced composite posts (FRCP) have become popular due to their multiple advantages in teeth with extensive crown destruction. Proper disinfection is essential for the successful bonding of these posts. Commonly used solutions for cleaning and disinfecting the root canals adversely affect the bond strength (BS). Photodynamic therapy is an alternative method for irrigating the root canal and disinfecting the post space.This study was designed to evaluate the impact of photodynamic therapy on the BS of fiber posts to root canal dentin. Methods: Human maxillary canines were recruited for this study. The tooth crowns were removed at the cervical line and endodontically treated. After fiber post spaces were prepared, the teeth were assigned to five groups based on the light-sensitive material: deionized water, indocyanine green and 810-nm laser, methylene blue and 660-nm laser, toluidine blue and 635-nm laser, curcumin and LED. The posts were cemented after photodynamic therapy. Cervical, middle, and apical samples were prepared by transverse sectioning. Push-out bond strength (PBS) values were measured in a universal testing machine. Finally, the data underwent statistical analyses with ANOVA and Howell-Games tests. Results: One-way ANOVA revealed no significant differences between the groups ( P <0.001). The Games-Howell test showed that curcumin (7.23±3.75) and the control group (5.92±4.04) had a similar BS ( P >0.005). The BS was lower in the methylene blue (3.34±2.15), indocyanine green (2.59±3.16), and toluidine blue (2.45±1.73) groups than in the control group ( P <0.005). Conclusion: Unlike other light-sensitive materials, curcumin did not adversely affect the BS.
Keyphrases
  • photodynamic therapy
  • fluorescence imaging
  • endothelial cells
  • high speed
  • deep learning
  • drinking water
  • electronic health record
  • big data
  • artificial intelligence