Background: Gingival crevice fluid (GCF) is a mixture of substances derived from serum, leukocytes, and structural cells of periodontium and oral bacteria. These substances possess a great potential for serving as indicators of periodontal disease and healing after therapy the main purpose of this study was to find if there is a difference in albumin concentration between healthy and diseased periodontal tissues and to compare between diseased group according to pocket depth Materials and methods: total sample composed of 60 pockets found in 35 patients all of them had no history of any systemic disease, The samples were divided in to three main group that include two diseased groups divided according to the depth of the periodontal pocket (group I were the pocket depth less than 6mm and group II were the pocket depth is equal or more than 6mm) and one healthy group (group III). Sampling of GCF were taken from patients in the second visits of periodontal treatment A previously weighed strips of filter paper size 30 were gently inserted in to the selected pocket depth until resistance was felt the filter paper left in place for 30 seconds and after removal they were weighed on a chemical balance. The difference between the weights of filter paper before and after absorption of exudates was calculated and each filter strips was placed in a tube containing o.3ml of normal saline then transferred and stored at -20C.on the day of analysis the samples were centrifuged at 10.000rpm for 20 minutes .the supernatant was used for assessment of Albunim colorimetrically similar to that of blood. Results: Comparison for gingival fluid weight were shown a non significant difference in the weight between group I&II at a P values >0.05 while there were a highly significant difference between group I&III and between group II&III at P values 0.05 intra groups correlation between albumin content in gingival fluid and periodontal parameter there were a significant negative correlation between plaque index and albumin in group I and II while anon significant correlation in group III also a significant and highly significant correlation were found between albumin and weight of gingival fluid in group I and II while anon significant differences in group III as shown in the table while there were anon significant differences between albumin content of gingival fluid and the gingival index, probing pocket depth and clinical attachment loss. Conclusion: the gingival crevicular fluid is an aqueous component in which is true trasudate and inflammatory exudates after the initial periodontal treatment in which it increased in weight as the inflammation present but the concentration of albumin may became a no significantly different compared with clinically healthy gingiva. As the initial periodontal treatment took place for each patients.
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The key objective of the study is to understand the best processes that are currently used in managing talent in Australian higher education (AHE) and design a quantitative measurement of talent management processes (TMPs) for the higher education (HE) sector.
The three qualitative multi-method studies that are commonly used in empirical studies, namely, brainstorming, focus group discussions and semi-structured individual interviews were considered. Twenty
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This research investigates manganese (Mn) extraction from Electric Arc Furnace Steel Slag (EAFS) by using the Liquid-liquid extraction (LLE) method. The chemical analysis was done on the slag using X-ray fluorescence, X-ray diffraction, and atomic absorption spectroscopy. This work consisted of two parts: the first was an extensive study of the effect of variables that can affect the leaching process rate for Mn element from slag (reaction time, nitric acid concentration, solid to liquid ratio, and stirring speed), and the second part evaluates the extraction of Mn element from leached solution. The results showed the possibility of leaching 83.5 % of Mn element from the slag at a temperature of 25°C, nitric acid co
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The present work reports on the performance of three types of nanofiltration membranes in the removal of highly polluting and toxic lead (Pb2+) and cadmium (Cd2+) from single and binary salt aqueous solutions simulating real wastewaters. The effect of the operating variables (pH (5.5-6.5), types of NF membrane and initial ions concentration (10-250 ppm)) on the separation process and water flux was investigated. It was observed that the rejection efficiency increased with increasing pH of solution and decreasing the initial metal ions concentrations. While the flux decreased with increasing pH of solution and increasing initial metal ions concentrations. The maximum rejection of lead and cadmium ion
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Corrosion of steel reinforcement is one of the biggest problems facing all countries in the world like bridges in the beach area and marine constructions which lead to study these problems and apply some economical solutions. According to the high cost of repair for these constructions, were studied the effect of using kind of chemical compounds sodium nitrite(NaNO2) and sodium silicate(Na2SiO3) as corrosion inhibitors admixture for steel bars that immersed partially in electrolyte solution (water + sodium chloride in 3% conc.) (Approximately similar to the concentration of salt in sea water). The two inhibitors above added each one to the electrolyte solution at concentrations (0.5%, 1% and 2%) for both
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Error control schemes became a necessity in network-on-chip (NoC) to improve reliability as the on-chip interconnect errors increase with the continuous shrinking of geometry. Accordingly, many researchers are trying to present multi-bit error correction coding schemes that perform a high error correction capability with the simplest design possible to minimize area and power consumption. A recent work, Multi-bit Error Correcting Coding with Reduced Link Bandwidth (MECCRLB), showed a huge reduction in area and power consumption compared to a well-known scheme, namely, Hamming product code (HPC) with Type-II HARQ. Moreover, the authors showed that the proposed scheme can correct 11 random errors which is considered a high
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