Background: The aims of this study were to evaluate the effect of implant site preparation in low-density bone using osseodensification method in terms of implant stability changes during the osseous healing period and peri-implant bone density using CBCT. Material and methods: This prospective observational clinical study included 24 patients who received 46 dental implants that were installed in low-density bone using the osseodensification method. CBCT was used to measure the bone density pre- and postoperatively and implant stability was measured using Periotest® immediately after implant insertion and then after 6 weeks and 12 weeks postoperatively. The data were analyzed using paired t-test and the probability value <0.05 was considered statistically significant. Results: Of the 46 implants, 43 were osseointegrated making the early survival of the implants 93.5%. There was a significant increase in bone density postoperatively; 337.6 ±182.9 compared to 265.3 ±173.9 Hounsfield units preoperatively. The primary implant stability was -2.7 ± 2.13 Periotest values (PTV), at the 6th week it decreased significantly (p<0.0001) to become 0.7 (± 4) PTV, and at the 12th week (secondary stability) it increased significantly (p<0.0001) to become -2.1 (± 2.8) PTV. The difference between primary and secondary stability was statistically non-significant (p=0.0814). Conclusions: Osseodensification resulted in high primary stability and increased peri-implant bone density but it did not prevent the implant stability drop during the first 6 weeks after insertion of implants. Key words:Osseodensification, implant stability, low-density bone.
Axial spondyloarthritis (axSpA) is a chronic rheumatic inflammatory disease affecting mainly the spine and sacroiliac joints. Since the copper-to-zinc ratio (Cu/Zn) indicates an inflammatory response, the change in ratio is expected to correlate with axSpA. This study compared levels of Cu/Zn in the serum of axSpA patients. Serum samples were obtained from 53 patients with axSpA divided according to biological treatment into cohorts A and B, and 28 healthy control as cohort C. Serum levels of Cu and Zn were determined first by a fully automated chemistry analyzer TC-Matrix Plus, then the ratio was obtained. The elevated serum Cu concentration means of cohort B (189.32 ± 13.808 µg/dL) compared to cohort A (168.85 ± 7.244 µg/dL) a
... Show MoreThe temperature control process of electric heating furnace (EHF) systems is a quite difficult and changeable task owing to non-linearity, time delay, time-varying parameters, and the harsh environment of the furnace. In this paper, a robust temperature control scheme for an EHF system is developed using an adaptive active disturbance rejection control (AADRC) technique with a continuous sliding-mode based component. First, a comprehensive dynamic model is established by using convection laws, in which the EHF systems can be characterized as an uncertain second order system. Second, an adaptive extended state observer (AESO) is utilized to estimate the states of the EHF system and total disturbances, in which the observer gains are updated
... Show MoreAbstract: non-alcoholic fatty liver disease (NAFLD) is one of the widespread chronic liver diseases; it is ranging from simple fat buildup in the liver (steatosis) to non-alcoholic steatohepatitis (NASH) presence of inflammation and hepatocyte injury. &nb
... Show MoreThis article aims to determine the time-dependent heat coefficient together with the temperature solution for a type of semi-linear time-fractional inverse source problem by applying a method based on the finite difference scheme and Tikhonov regularization. An unconditionally stable implicit finite difference scheme is used as a direct (forward) solver. While by the MATLAB routine lsqnonlin from the optimization toolbox, the inverse problem is reformulated as nonlinear least square minimization and solved efficiently. Since the problem is generally incorrect or ill-posed that means any error inclusion in the input data will produce a large error in the output data. Therefore, the Tikhonov regularization technique is applie
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