Abstract: The aim of this study was to evaluate the effect of bone density value in Hounsfield unit derived from cone beam computed tomography (CBCT), and implant dimensions in relation to implant stability parameters namely the resonance frequency analysis and the insertion torque (IT) value. It included 24 patients who received 42 dental implants (DI). The bone density of the planned implant site was preoperatively measured using cone beam computed tomography. The implant stability was measured using Osstell implant stability quotient (ISQ). The ISQ values were recorded immediately postoperatively and after 16 weeks. The IT value was categorized as 35 N/cm or > 35 N/cm. The mean (standard deviation) primary stability was 79.58 (5.27) ISQ, which was significantly higher than the secondary stability 74.31 (6.34) ISQ (P < 0.0001). There was a significant moderate positive correlation of bone density with primary stability (r = 0.4, P = 0.0099) and no correlation with secondary stability (r = 0.003, P = 0.9867). The bone density of DI with 35 N/cm IT was significantly lower than with > 35 N/cm IT (P = 0.0390). Better stability was recorded with wider implants. Whereas the length of the DI showed a nonsignificant correlation with primary and secondary stability (P = 0.7633 and 0.4670, respectively). The DI dimensions showed a nonsignificant correlation with the IT. Cone beam computed tomography may be considered as a reliable method to assess bone density and predict the implant stability. The diameter of DI affected the implant stability favorably, whereas DI length showed no effect.
S Khalifa E, N Adil A, AS Mazin M…, 2008
Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health
... Show MoreIn current article an easy and selective method is proposed for spectrophotometric estimation of metoclopramide (MCP) in pharmaceutical preparations using cloud point extraction (CPE) procedure. The method involved reaction between MCP with 1-Naphthol in alkali conditions using Triton X-114 to form a stable dark purple dye. The Beer’s law limit in the range 0.34-9 μg mL-1 of MCP with r =0.9959 (n=3) after optimization. The relative standard deviation (RSD) and percentage recoveries were 0.89 %, and (96.99–104.11%) respectively. As well, using surfactant cloud point extraction as a method to extract MCP was reinforced the extinction coefficient(ε) to 1.7333×105L/mol.cm in surfactant-rich phase. The small volume of organi
... Show MoreABSTRACT:
Microencapsulation is used to modify and retard drug release as well as to overcome the unpleasant effect
(gastrointestinal disturbances) which are associated with repeated and overdose of ibuprofen per day.
So that, a newly developed method of microencapsulation was utilized (a modified organic method) through a
modification of aqueous colloidal polymer dispersion method using ethylcellulose and sodium alginate coating materials to
prepare a sustained release ibuprofen microcapsules.
The effect of core : wall ratio on the percent yield and encapsulation efficiency of prepared microcapsules was low, whereas
, the release of drug from prepared microcapsules was affected by core: wall ratio ,proportion of coa