Oral rehabilitation with dental implants has resulted in high success rates. However, some complications have been described, such as the loss of the prosthetic screw. Some manufacturers sell screws with different coatings to avoid screw loosening, but even these types of screws can come loose. We aimed to investigate the screw coatings that can be applied during a dental appointment to avoid screw loosening. Following PRISMA Guidelines, we searched PubMed/Medline, Embase and Web of Science for studies published up to January 2024. All studies of single dental implant crowns, in which the prosthetic screw was coated with a lubricant and the preload and/or the removal torque value (RTV) was recorded, were analyzed. We excluded studies applying the finite element method (FEM) as well as studies without a control group. The risk of bias was assessed with a tool developed by our research group. Of the 1959 records identified, 19 were selected. Ten studies were considered to have a low risk of bias, and nine were considered to have a medium risk of bias. The coatings tested were adhesives, saliva, chlorhexidine, Vaseline, silicone gel, Polytetrafluoroethylene (PTFE) tape, blood, fluoride, Listerine^® Mouthwash and normal saline. The preload, the RTV with and without cyclic loading and the percentage of RTV loss were recorded. Some coatings show promise, although there is no clear evidence that any option is superior in minimizing screw loosening.

This study aimed to evaluate the effect of two coating materials, a silicone sealing gel and a polytetrafluoroethylene (PTFE) tape, on the screw preload and removal torque value (RTV) to develop strategies to prevent prosthetic screw loosening. We examined 45 complexes comprising an implant, abutment, and prosthetic screw, of which 15 samples were uncoated, 15 were coated with GapSeal^® (Hager & Werken GmbH & Co., Duisburg, Germany), and 15 were coated with PTFE tape (MIARCO^®, Valencia, Spain). The screws were tightened to register the preload and then untightened to register the RTV. The preload values showed a statistically significant difference only in the PTFE group, suggesting that this lubricant negatively affects the preload. The RTVs showed statistically significant differences among all groups, with the GapSeal^® group and PTFE group showing the highest and lowest values, respectively. It can be concluded that the application of the PTFE tape on the screw significantly reduced the preload and RTV. The silicone sealing gel did not affect the preload but increased the RTV. Therefore, the use of GapSeal^® should be considered to prevent prosthetic screw loosening, while the use of PTFE tape should be avoided.

(1) Background: In order to conduct in vitro studies regarding muco-supported dentures, it is necessary to have a simulation model that simulates the oral mucosa, as it is the main influencing factor for their retention and stabilisation. The aim of this study is to perform tensile and wettability tests in three different elastomers to identify the best material for simulating the oral mucosa. (2) Methods: A tensile test was performed according to ISO 527-1 and a compressive test was performed according to ISO 604:2002, at a constant speed of 10 mm/min. The E-modulus was calculated. A wettability test was performed according to ISO 19403-2. Shore A hardness was measured according to ISO 868:2003. All values were compared with the oral mucosa data. (3) Results: Tensile E-modulus calculation revealed no significant difference between Molloplast^® B and EXA’lence^TM Light Body. The mean drop angle calculation revealed no significant difference between Ufi Gel^® SC and Molloplast^® B. The compression E-modulus showed significant differences for Ufi Gel^® SC and EXA’lence^TM Light Body, while Molloplast^® B showed no significant deviation. Ufi Gel^® SC has a similar Shore A hardness to the other materials. (4) Conclusions: Molloplast^® B and Ufi Gel^® SC are the most similar elastomers to the alveolar mucosa.

Background: This study aimed to evaluate the influence of a screw coating on the screw preload and removal torque value (RTV) with and without the application of a cyclic load (CL) to make screws with greater untightening resistance to prevent screw loosening. Methods: Ninety complexes composed of implants, abutments, and prosthetic screws were examined and tested under CL oral conditions (n = 45) and non-CL conditions (nCL, n = 45). Each group was divided into three subgroups (n = 15): a control group (CG) without a screw coating, a GapSeal^®-coated screw group (GG), and a polytetrafluoroethylene (PTFE) tape-wrapped screw group (PG). All screws were tightened at 30 Ncm, and the preload was recorded. In the nCL group, the screws were untightened to record the RTV. In the CL group, the screws were tightened, subjected to a CL in distillated water at a temperature of 37 °C, and then untightened to record the RTV. Micro-Ct analysis was conducted on two samples from each group before CL. SEM analyses of two samples per subgroup before and after CL were also performed. Results: The preload in the PG was significantly lower under nCL (29.92 Ncm) compared with CG (30.95 Ncm) and GG (31.19 Ncm) and also under a CL (PG: 30.92 Ncm) compared with CG (31.72 Ncm) and GG (31.42 Ncm). The RTVs of the PG were significantly lower under nCL (15.30 Ncm) compared with CG (27.98 Ncm) and GG (28.46 Ncm). Under CL, the RTVs of the PG were significantly higher (31.50 Ncm) compared with CG (26.00 Ncm) and GG (27.44 Ncm). Conclusions: Wrapping the screw with PTFE tape significantly reduced the preload but resulted in a significantly greater RTV under CL conditions in the simulated oral environment, suggesting that this could be a solution to decrease the risk of screw loosening.

Background: Stereolithography is a rapid prototyping and 3D printing technique that creates solid three-dimensional models. An accurate and functional 3D model using stereolithography is invaluable in scientific research, particularly in studies involving edentulous patients. Additive manufacture and CAD systems help achieve accurate measurements and procedures and be easily replicated by lowering human error mistakes. The main objective of this study was to develop an in vitro simulation model with a reduced alveolar ridge with the same characteristics as mandibular edentulous patients using stereolithography. Methods: A mandibular model with a resorbed mandibular crest was scanned, and the STL model was aligned to the XYZ reference system. A reduction in the alveolar ridge corresponding to the mandibular mucosa of an edentulous patient was achieved. A negative model also derived from the original model was made to ensure the space for oral simulation material. A dimensional stability test was performed to validate the model. Results: The maximal mean displacement of the model was 0.015 mm, and the minimal mean displacement was 0.004 mm. The oral mucosa had a displacement of approximately 1.6 mm. Conclusions: An in vitro 3D simulation model of a complete edentulous patient mucosa was achieved.

Fully automated artificial intelligence (AI)-based dental crown design has recently emerged as an alternative to conventional workflows, including technician-assisted digital design and traditional wax-up techniques. However, the available evidence remains heterogeneous, and no clear consensus exists regarding its performance in terms of time efficiency, fit, and morphological accuracy. We aimed to systematically evaluate whether fully automated AI-based crown design provides comparable or superior outcomes compared with conventional approaches. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed/MEDLINE, Embase, Web of Science, and Cochrane. All studies evaluating single dental crowns designed using fully automated AI systems and assessing outcomes such as time efficiency, marginal gap, internal fit, and/or morphological accuracy were included. Studies involving implant-supported or partial crowns, noncomparative simulations unrelated to crown fabrication, or those without a conventional comparator were excluded. The risk of bias was assessed using the QUIN (Quality Assessment Tool for In Vitro Studies) tool and Newcastle-Ottawa Scale (NOS). Evidence from the selected studies was synthesized. This study is registered in Prospective Register of Systematic Reviews (CRD420261359758). Of the 1291 records identified, 9 were included. Four studies were considered to have a low risk of bias, and four were considered to have a moderate risk of bias and the clinical study with good methodology quality. Time efficiency was consistently improved in AI-based workflows compared with conventional digital or wax-up techniques. Marginal gap and internal fit were generally comparable between AI and conventional digital workflows, with most values remaining within clinically acceptable thresholds. Regarding morphological accuracy, the results were heterogeneous, with some studies reporting superior outcomes for AI systems, whereas others favored technician-assisted digital workflows. Limitations of the available evidence include methodological heterogeneity, inconsistent definitions of outcomes such as “accuracy” and “work time,” variability in software systems and evaluation protocols, and the predominance of in vitro studies conducted under idealized conditions. Overall, fully automated AI-based crown design demonstrates promising potential, particularly in reducing working time while maintaining clinically acceptable fit and morphological outcomes comparable to those of conventional digital workflows. However, the current evidence does not support systematic superiority of AI across all evaluated parameters. Further standardized and clinically oriented studies are needed to validate the routine integration of AI-driven crown design into prosthetic dental practice.