Antimicrobial activity, surface properties, and cytotoxicity of microencapsulated phytochemicals incorporated into three-dimensionally printable dental polymers.

OBJECTIVES: This study aimed to investigate the antimicrobial properties of three dimensionally-printed dental polymers (3DPs) incorporated with microencapsulated phytochemicals (MPs) and to assess their surface characteristics and cytotoxicity. METHODS: MPs derived from phytoncide oil and their specific chemical components were introduced into suspensions of three microbial species: Streptococcus gordonii, Streptococcus oralis, and Candida albicans. Optical density was measured to determine the microbial growth in the presence of MPs for testing their antimicrobial activity. MPs at 5% (w/w) were mixed with dental polymers and dispersants to 3DP discs. These microbial species were then seeded onto the discs and incubated for 24 h. The antibacterial and antifungal activities of MP-containing 3DPs were evaluated by counting the colony-forming units (n = 3). The biofilm formation on the 3DP was assessed by crystal violet staining assay (n = 3). Microbial viability was determined using a live-dead staining and CLSM observation (n = 3). Surface roughness and water contact angle were assessed (n = 10). Cytotoxicity of MP-containing 3DPs for human gingival fibroblast was evaluated by MTT assay. RESULTS: MPs, particularly (-)-α-pinene, suppressed the growth of all tested microbial species. MP-containing 3DPs significantly reduced the colony count (P ≤ 0.001) and biofilm formation (P ≤ 0.009), of all tested microbial species. Both surface roughness (P < 0.001) and water contact angle (P < 0.001) increased. The cytotoxicity remained unchanged after incorporating MPs to the 3DPs (P = 0.310). CONCLUSIONS: MPs effectively controlled the microbial growth on 3DPs as evidenced by the colony count, biofilm formation, and cell viability. Although MPs modified the surface characteristics, they did not influence the cytotoxicity of 3DPs. CLINICAL SIGNIFICANCE: Integration of MPs into 3DPs could produce dental prostheses or appliances with antimicrobial properties. This approach not only provides a proactive solution to reduce the risk of oral biofilm-related infection but also ensures the safety and biocompatibility of the material, thereby improving dental care.

Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study.

OBJECTIVES: This study compared the tooth morphology, internal fit, occlusion, and proximal contacts of dental crowns automatically generated via two deep learning (DL)-based dental software systems with those manually designed by an experienced dental technician using conventional software. METHODS: Thirty partial arch scans of prepared posterior teeth were used. The crowns were designed using two DL-based methods (AA and AD) and a technician-based method (NC). The crown design outcomes were three-dimensionally compared, focusing on tooth morphology, internal fit, occlusion, and proximal contacts, by calculating the geometric relationship. Statistical analysis utilized the independent t-test, Mann-Whitney test, one-way ANOVA, and Kruskal-Wallis test with post hoc pairwise comparisons (α = 0.05). RESULTS: The AA and AD groups, with the NC group as a reference, exhibited no significant tooth morphology discrepancies across entire external or occlusal surfaces. The AD group exhibited higher root mean square and positive average values on the axial surface (P < .05). The AD and NC groups exhibited a better internal fit than the AA group (P < .001). The cusp angles were similar across all groups (P = .065). The NC group yielded more occlusal contact points than the AD group (P = .006). Occlusal and proximal contact intensities varied among the groups (both P < .001). CONCLUSIONS: Crowns designed by using both DL-based software programs exhibited similar morphologies on the occlusal and axial surfaces; however, they differed in internal fit, occlusion, and proximal contacts. Their overall performance was clinically comparable to that of the technician-based method in terms of the internal fit and number of occlusal contact points. CLINICAL SIGNIFICANCE: DL-based dental software for crown design can streamline the digital workflow in restorative dentistry, ensuring clinically-acceptable outcomes on tooth morphology, internal fit, occlusion, and proximal contacts. It can minimize the necessity of additional design optimization by dental technician.

Deep learning-designed implant-supported posterior crowns: Assessing time efficiency, tooth morphology, emergence profile, occlusion, and proximal contacts.

OBJECTIVES: To compare implant supported crowns (ISCs) designed using deep learning (DL) software with those designed by a technician using conventional computer-aided design software. METHODS: Twenty resin-based partially edentulous casts (maxillary and mandibular) used for fabricating ISCs were evaluated retrospectively. ISCs were designed using a DL-based method with no modification of the as-generated outcome (DB), a DL-based method with further optimization by a dental technician (DM), and a conventional computer-aided design method by a technician (NC). Time efficiency, crown contour, occlusal table area, cusp angle, cusp height, emergence profile angle, occlusal contacts, and proximal contacts were compared among groups. Depending on the distribution of measured data, various statistical methods were used for comparative analyses with a significance level of 0.05. RESULTS: ISCs in the DB group showed a significantly higher efficiency than those in the DM and NC groups (P ≤ 0.001). ISCs in the DM group exhibited significantly smaller volume deviations than those in the DB group when superimposed on ISCs in the NC group (DB-NC vs. DM-NC pairs, P ≤ 0.008). Except for the number and intensity of occlusal contacts (P ≤ 0.004), ISCs in the DB and DM groups had occlusal table areas, cusp angles, cusp heights, proximal contact intensities, and emergence profile angles similar to those in the NC group (P ≥ 0.157). CONCLUSIONS: A DL-based method can be beneficial for designing posterior ISCs in terms of time efficiency, occlusal table area, cusp angle, cusp height, proximal contact, and emergence profile, similar to the conventional human-based method. CLINICAL SIGNIFICANCE: A deep learning-based design method can achieve clinically acceptable functional properties of posterior ISCs. However, further optimization by a technician could improve specific outcomes, such as the crown contour or emergence profile angle.

Topographical and crystalline change on surface by sandblasting improve flexural and shear bond strength of niobia-modified yttria-stabilized tetragonal zirconia polycrystal.

This study aimed to investigate the effects of sandblasting on the physical properties and bond strength of two types of translucent zirconia: niobium-oxide-containing yttria-stabilized tetragonal zirconia polycrystals ((Y, Nb)-TZP) and 5 mol% yttria-partially stabilized zirconia (5Y-PSZ). Fully sintered disc specimens were either sandblasted with 125 µm alumina particles or left as-sintered. Surface roughness, crystal phase compositions, and surface morphology were explored. Biaxial flexural strength (n=10) and shear bond strength (SBS) (n=12) were evaluated, including thermocycling conditions. Results indicated a decrease in flexural strength of 5Y-PSZ from 601 to 303 MPa upon sandblasting, while (Y, Nb)-TZP improved from 458 to 544 MPa. Both materials significantly increased SBS after sandblasting (p<0.001). After thermocycling, (Y, Nb)-TZP maintained superior SBS (14.3 MPa) compared to 5Y-PSZ (11.3 MPa) (p<0.001). The study concludes that (Y, Nb)-TZP is preferable for sandblasting applications, particularly for achieving durable bonding without compromising flexural strength.