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.
In present project, new Schiff base of 4, 4'- (((1E, 1'E)-1,4-.phenylenebis- (methane-ylylidene))-bis-(azane-ylylidene)) bis-(5-(4-chlorophenyl) -4H -1,2,4-triazole-3-thione) (L3) has been synthesized by condensation of 4-amino-5-(4-chlorophenyl)-2,4-dihydro-3H-1,2,4-triazole-3-thione with benzene-1,4-dicarboxaldehyde. The new asymmetrical Schiff base (L3) used as a ligand to synthesize a new complex with Co(II), Ni(II), Cu(II), Pd(II), and Pt(IV) metal ions by 1:2 (Metal: ligand) ratio. New ligand and their complexes have been exanimated and Confirmed by Fourier-transform infrared (FT-IR), Ultraviolet-visible (UV-visible), Proton nuclear magnetic resonance (1HNMR), carbon13 nuclear magnetic resonance (13CNMR), carbon-hydrogen nitrogen sulf
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In his study, the researcher highlighted the most important methods of authorship in the fundamentals of jurisprudence and speech. Fundamentalist rules and extraction and access method; they also distinct from each other that each has special divisions of the subjects of jurisprudence.
A nano-sensor for nitrotyrosine (NT) molecule was found by studying the interactions of NT molecule with new B24N24 nanocages. It was calculated using density functionals in this case. The predicted adsorption mechanisms included physical and chemical adsorption with the adsorption energy of −2.76 to −4.60 and −11.28 to −15.65 kcal mol−1, respectively. The findings show that an NT molecule greatly increases the electrical conductivity of a nanocage by creating electronic noise. Moreover, NT adsorption in the most stable complexes significantly affects the Fermi level and the work function. This means the B24N24 nanocage can detect NT as a Φ–type sensor. The recovery time was determined to be 0.3 s. The sensitivity of pure BN na
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One of the most important virulence factors in Pseudomonas aeruginosa is biofilm formation, as it works as a barrier for entering antibiotics into the bacterial cell. Different environmental and nutritional conditions were used to optimize biofilm formation using microtitre plate assay by P. aeruginosa. The low nutrient level of the medium represented by tryptic soy broth (TSB) was better in biofilm formation than the high nutrient level of the medium with Luria Broth (LB). The optimized condition for biofilm production at room temperature (25 °C) is better than at host temperature (37 °C). Moreover, the staining with 0.1% crystal violet and reading the biofilm with wavelength 360 are considered essential factors in
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A time series analysis can help to observe the behavior of the system and specify the system faults. In addition, it also helps to explain the various energy flows in the system and further aid in reducing the thermodynamic losses. The intelligent supervisory LabVIEW software can monitor the incoming data from the system by using Arduino microcontroller and calculates the important parameters. Energy, exergy, and anergy analysis present in this paper to investigate the system performance as well as its components. To accomplish this, a 4-ton vertical split air conditioner based on vapor compression refrigeration cycle charged with refrigerant R-22 was modified for experimental analysis. The results showed that during 540
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Fourier Transform-Infrared (FT-IR) spectroscopy was used to analyze gasoline engine oil (SAE 5W20) samples that were exposed to seven different oxidation times (0 h, 24 h, 48 h, 72 h, 96 h, 120 h, and 144 h) to determine the best wavenumbers and wavenumber ranges for the discrimination of the oxidation times. The thermal oxidation process generated oil samples with varying total base number (TBN) levels. Each wavenumber (400–3900 cm−1) and wavenumber ranges identified from the literature and this study were statistically analyzed to determine which wavenumbers and wavenumber ranges could discriminate among all oxidation times. Linear regression was used with the best wavenumbers and wavenumber ranges to predict oxidation time.
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