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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Heterocyclic systems, which are essential in medicinal chemistry due to their promising cytotoxic activity, are one of the most important families of organic molecules found in nature or produced in the laboratory. As a result of coupling N-(4-nitrophenyl)-3-oxo-butanamide (3) using thiourea, indole-3-carboxaldehyde, or piperonal, the pyrimidine derivatives (5a and 5b) were produced. Furthermore, pyrimidine 9 was synthesized by reacting thiophene-2-carboxaldehyde with ethyl cyanoacetate and urea with potassium carbonate as a catalyst. The chalcones 11a and 11b were synthesized by reacting equal molar quantities of 1-naphthaldehy
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In this study, a packed bed was used to remove pathogenic bacteria from synthetic contaminated water. Two types of packing material substrates, sand and zeolite, were used. These substrates were coated with silver nanoparticles (AgNPs), which were prepared by decomposition of Ag ions from AgNO3 solution. The prepared coated packings were characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy and transmission electron microscopy. The packed column consisted of a PVC cylinder of 2 cm diameter and 20 cm in length. The column was packed with silver nanoparticlecoated substrates (sand or zeolite) at a depth of 10 cm. Four types of bacteria were studied: Escherichia coli, Shigella dysenteriae, Pseudomonas aerugi
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Objective(s): To assess Baghdad University students’ knowledge and attitudes toward HIV/AIDS, and to find out
the relationship of Baghdad University students’ knowledge and attitudes with certain variables (gender,
socioeconomic status, field of study).
Methodology: A descriptive analytic study was used to assess the knowledge and attitudes of Baghdad University
Students’ toward HIV/AIDS. The study was conducted (November 1st 2012 to July 15th 2013). A non-probability
(purposive sample) of 400 students (males-138 and females-262) were selected from four colleges and they were
in the fourth class, a probability (stratified random) method was used to select four colleges at University of
Baghdad as a study settin
Nanoparticles (NPs) have unique capabilities that make them an eye-opener opportunity for the upstream oil industry. Their nano-size allows them to flow within reservoir rocks without the fear of retention between micro-sized pores. Incorporating NPs with drilling and completion fluids has proved to be an effective additive that improves various properties such as mud rheology, filtration, thermal conductivity, and wellbore stability. However, the biodegradability of drilling fluid chemicals is becoming a global issue as the discharged wetted cuttings raise toxicity concerns and environmental hazards. Therefore, it is urged to utilize chemicals that tend to break down and susceptible to biodegradation. This research presents the pra
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The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when
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