Background: Relapse of previously moved teeth, is major clinical problem in orthodontics with respect to the goals of successful treatment. This study investigated the effect of orthodontic relapse on the proliferation of fibroblast and epithelial rests of Malassez cells in periodontal ligament of rat molars. Materials and Methods: Sixteen ten-week- old male Wister rats were randomly divided into four groups composed of four animals each: Group I received no orthodontic force (control). In both Group II and Group III, uniform standardized expansive springs were used for moving the maxillary first molars buccally for periods of one and three weeks respectively. The spring initially generated an average expansive force of 20 g on each side. In Group IV the springs were left for three weeks, until the maxillary first molars moved buccally, after that the springs were removed and the animals were scarified after three weeks of relapse tooth movement. After the humanly scarification of animals, each maxilla in all groups was dissected into two halves each half including the three maxillary molars and processed for histological examination. The number of both fibroblast and ERM cells in each cluster was counted in the PDL of the pressure side of the mesio-buccal roots of the maxillary right and left first molars in all groups and the surface areas of the ERM clusters were also measured in all groups. Results: The number of fibroblast was significantly increased at the end of active movement (Group III) and significantly very highly increased during the relapse period (Group IV). Regarding the ERM cells there were statistically significant increase in both the number of cells in each ERM cluster and the surface areas of the ERM clusters in Group III and highly significant increase in Group IV, while Group II showed no significant differences regarding all measurements. Conclusions: It was concluded that fibroblast and ERM cells may play an important role during orthodontic relapse
This work deals with kinetics and chemical equilibrium studies of esterification reaction of ethanol with acetic acid. The esterification reaction was catalyzed by an acidic ion exchange resin (Amberlyst- 15) using a batch stirred tank reactor. The pseudo-homogenous and Eley-Rideal models were successfully fitted with experimental data. At first, Eley-Rideal model was examined for heterogeneous esterification of acetic acid and ethanol. The pseudo-homogenous model was investigated with a power-law model. The apparent reaction order was determined to be (0.88) for Ethanol and (0.92) for acetic acid with a correlation coefficient (R2) of 0.981 and 0.988, respectively. The reaction order was determined to be 4.1087x10-3 L0.8/(mol0.8.min) with
... Show MoreLet G be a graph with p vertices and q edges and be an injective function, where k is a positive integer. If the induced edge labeling defined by for each is a bijection, then the labeling f is called an odd Fibonacci edge irregular labeling of G. A graph which admits an odd Fibonacci edge irregular labeling is called an odd Fibonacci edge irregular graph. The odd Fibonacci edge irregularity strength ofes(G) is the minimum k for which G admits an odd Fibonacci edge irregular labeling. In this paper, the odd Fibonacci edge irregularity strength for some subdivision graphs and graphs obtained from vertex identification is determined.
The research includes the preparation of a new Schiff base(4-methyl-2-((2-phenyl hydrazineylidene)methyl)naphthalen-1-ol), which was subsequently, used to prepare a series of complexes using chlorides of Mn2+, Co2+, Cu2+, Cr3+, and Fe3+ ions. The synthesized compounds were characterized using various techniques such as elemental microanalysis (C.H.N), chloride content determination using Mohr’s method, FT-IR spectroscopy, UV-Visible, mass spectra, conductivity, DSC (Differential Scanning Calorimetry), and thermogravimetric analysis. Overall, the decay of the ligand and its metal complexes was recorded to determine their thermal stability and weight-loss profiles. The results indicated that ligand acts as a bidentate doner, coordinating wi
... Show MoreBackground:The most common pattern of dyslipidemia in diabetic patients is increased triglyceride (TG) and decreased HDL cholesterol level, The concentration of LDL cholesterol in diabetic patients is usually not significantly different from non diabetic individuals, Diabetic patients may have elevated levels of non-HDL cholesterol [ LDL+VLDL]. However type 2 diabetic patients typically have apreponderance of smaller ,denser LDL particles which possibly increases atherogenicity even if the absolute concentration of LDL cholesterol is not significantly increased. The Third Adult Treatment Panel of the National Cholesterol Education Program (NCEP III) and the American Heart Association (AHA ) have designate diabetes as a coronary heart dis
... Show MoreA step to net-zero of carbon dioxide losses in the microalgae cultivation process was targeted in the current study. This research was carried out by using pre-dissolved inorganic carbon (DIC) as a source of carbon with two doses of twenty-five and fifty millilitres.
In an earlier paper, the basic analytical formula for particle-hole nuclear state densities was derived for non-Equidistant Spacing Model (non-ESM) approach. In this paper, an extension of the former equation was made to include pairing. Also a suggestion was made to derive the exact formula for the particle-hole state densities that depends exactly on Fermi energy and nuclear binding energies. The results indicated that the effects of pairing reduce the state density values, with similar dependence in the ESM system but with less strength. The results of the suggested exact formula indicated some modification from earlier non-ESM approximate treatment, on the cost of more calculation time
Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of
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