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STATEMENT OF PROBLEM: The osseointegration of dental implant is related to their composition and surface treatment. Titanium zirconium (TiZr) has been introduced as an alternative to the commercially pure titanium and its alloys as dental implant material, which is attributed to its superior mechanical and biological properties. Surface treatments of TiZr have been introduced to enhance their osseointegration ability; however, reliable, easy to use surface modification technique has not been established. PURPOSE: The purpose of this study was to evaluate and compare the effect of neodymium-doped yttrium aluminum garnet (Nd-YAG) laser surface treatment of TiZr implant alloy on their osteogenic potential. MATERIALS AND METHODS: Twenty disc-shaped samples of 5 mm diameter and 2 mm height were milled from the TiZr alloy ingot. The polished discs were ultrasonically cleaned in distilled water. Ten samples each were randomly selected as Group A control samples and Group B consisted of Nd-YAG laser surface etched and conditioned test samples. These were evaluated for cellular response. Cellular adhesion and proliferation were quantified, and the results were statistically analyzed using nonparametric analysis. Cellular morphology was observed using electron and epiflurosence microscopy. RESULTS: Nd-YAG laser surface modified and conditioned TiZr samples increased the osteogenic potential. CONCLUSION: Nd-YAG laser surface modification of TiZr, improves the cellular activity, surface roughness, and wettability, thereby increasing the osteogenic potential.
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BACKGROUND: Gingival aesthetics or pink aesthetics requires a prosthodontic approach to ensure an appealing smile with an optimal muco-gingival appearance by the use of colored materials with gingival shades to match adjacent soft tissues. However, the selection of this adhesive gingival-colored material becomes complex owing to the wide range of gingival color guides and shade tabs currently available on the market. AIM: The study aims to assess the variation in gingival color between two specific regions on the anterior gingival surface through the use of a digital color assessment method. Furthermore, the study seeks to investigate the potential requirements for an innovative soft tissue dual shade guide system. METHODOLOGY: Fifteen participants were examined with an external light source set up in a 45-degree optical configuration. The Frontal view intraoral photographs were taken with a digital Canon 70D camera using a cheek retractor. The photo was white balanced using the color sorter tool in the software (Adobe Photoshop CS6®), and the second quadrant was cropped, two regions were selected (free gingival margin and marginal gingiva) and used for all samples for standardization. The color data were represented in terms of L*, a*, and b* coordinate axes values following the CIELAB color system. The recorded color coordinates were then examined using SPSS software, version 24 (IBM Corp., Armonk, NY). RESULTS: The mean and standard deviation of the coordinate axes were as follows: for L1, 52.33 ± 12.92; for a1, 30.06 ± 4.81; for b1, 18.00 ± 3.89; for L2, 44.53 ± 11.01; for a2, 36.13 ± 7.92; and for b2, 18.26 ± 6.70. Statistically significant differences were found between the L*, a*, and b* color coordinates with a color difference (ΔE) beyond the clinical acceptance (ΔE > 3.7) threshold of ΔE = 4.88, mainly for a* values. CONCLUSIONS: Within the limitations of this study, significant color differences were observed between the selected regions. The a* coordinate was found to be statistically significant (+6.07), indicating a shift towards a lighter shade of redness (+a) in the color-opponent dimensions of redness-greenness within the CIELAB color space system.
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Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.
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Rapid prototyping (RP) is a technology that produces physical models by selectively solidifying ultra violet (UV) sensitive liquid resin using a laser beam. These models can be formed using various techniques. A study was undertaken to compare the dimensional accuracy and surface details of three prototype models with a 3D STL (standard template library) image. In this study the STL file was used to produce three different rapid prototype models namely; model 1-fused deposition model (FDM) using ABS (acrylonitrile butadiene styrene), model 2-Polyjet using a clear resin and model 3-a 3 dimensional printing using a composite material. Measurements were made at various anatomical points. For surface detail reproductions the models were subjected to scanning electron microscopy analysis. The dimensions of the model created by Polyjet were closest to the 3D STL virtual image followed by the 3DP model and FDM. SEM analysis showed uniform smooth surface on Polyjet model with adequate surface details.
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CONTEXT: Implant design influences the stress distribution in an implant-supported distal cantilever fixed partial denture and supporting bone tissue. AIM: The purpose of this study was to investigate the effect of implant design on the stress distribution in the framework, implant, and surrounding bone, using a three-dimensional finite-element analysis. MATERIALS AND METHODS: A three-dimensional finite-element model of a mandibular section of bone with implants placed in the first and second premolar region was created to support a distal cantilever fixed partial denture. A one-piece and two-piece implant and its suprastructure were simulated into wire frame models using Pro engineer (Pro E) program. Four models were created in this study. RESULTS: Comparative analysis of all models showed that the maximum stress overall was in the cervical portion of the secondary abutment. When used in combination, the maximum stress was when the two-piece implant was used as secondary abutment. The one-piece implant showed less stress compared to its counterpart when used as secondary abutment. The maximum stress distribution in the bone was around the neck region of the secondary implant. CONCLUSION: Within the limitations of this study, it can be concluded that stress distribution is better in a one-piece implant design when compared with the two-piece implant design, with stress concentration being more at the junction of the abutment and the implant fixture in the two-piece implant. When implants are used as abutments (either primary or secondary), irrespective of their position and design, the secondary implant shows the maximum amount of stresses.