Performance evaluation of carbon quantum dots impregnated glass ionomer cement to avoid peri-implant disease.

Dental cement residues exacerbate peri-implant tissue irritation and peri-implantitis. The present study aims to evaluate the cytotoxicity, physiochemical, optical, and rheological properties of carbon quantum dots (CQDs) impregnated glass ionomer cement (GIC). Surface passivated fluorescent CQDs were synthesized using citric acid via thermal decomposition and blended with GIC. Characterization studies and rheological measurements were made to evaluate their performance. 3D-printed dental implant models cemented with GIC and GIC-CQD were compared to analyze excess cement residues. MTT assay was performed with human dental pulp stem cells (hDPSCs) and statistically analyzed using ANOVA and Tukey's test. CQDs with a particle dimension of ∼2 nm were synthesized. The amorphous property of GIC-CQD was confirmed through XRD. The fluorescence properties of GIC-CQD showed three times higher emission intensity than conventional GIC. GIC-CQD attained maturation with a setting time extended by 64 s than GIC. Cement residue of size 2 mm was detected with a UV light excitation at a distance between 5 to 10 cm. Biocompatibility at 0.125 mg ml-1dilution concentrations of GIC-CQD showed viability greater than 80% to hDPSCs. For the first time, we report that CQDs-impregnated GIC is a unique and cost-effective strategy for in-situ detection of excess cement rapidly using a hand-held device. A novel in-situ rapid detection method enables the dentist to identify residual cement of size less than 2 mm during the implantation. Therefore, GIC-CQD would replace conventional GIC and help in the prevention of peri-implant diseases.

Prediction of excess cement residues using a regression model to avoid peri-implant diseases: An in vitro study.

Dental implant restorations attached to cement can potentially result in peri-implant mucositis and peri-implantitis if cement residues are present. Effectively predicting and eliminating such dental cement residues is crucial for preventing complications. This study focuses on creating a regression model using the pixel values to predict the Excess Cement Residues (ECR) by employing an octagonal surface imaging approach. A model featuring gingival imitation, ten abutments, and ten crowns was created, and the cemented implants underwent thorough photographic and analytical assessment. The ECR was determined through two distinct approaches: the Computerized Planimetric Method (CPM) and the weighing method. Across ten implants in this in vitro study, ECR varied from 0.3 to 21 mg, with an average of 5.69 mg. The findings reveal a higher amount of ECR on the distal, mesiobuccal, and mesial sides. Utilizing Pearson's correlation, a coefficient value of r = 0.786 signifies a strong correlation between CPM and the weighing method. The regression model further aids in predicting ECR based on pixel values. The octagonal surface imaging approach not only vividly captures information about ECR in the implant cementation region but also emphasizes the feasibility of ImageJ as an effective tool for detecting ECR. The congruence between CPM and the weighing method results supports the application of the regression model for precise ECR prediction.