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Ability of gingival crevicular fluid volume, E‐cadherin, and total antioxidant capacity levels for predicting outcomes of nonsurgical periodontal therapy for periodontitis patients
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Abstract<sec><title>Objectives

To determine the potential of gingival crevicular fluid (GCF) volume, E‐cadherin and total antioxidant capacity (TAC) levels to predict the outcomes of nonsurgical periodontal therapy (NSPT) for periodontitis patients.

Background

NSPT is the gold‐standard treatment for periodontal pockets < 6 mm in depth, however, successful outcomes are not always guaranteed due to several factors. Periodontitis‐associated tissue destruction is evidenced by the increased level of soluble E‐cadherin and reduced antioxidants in oral fluids which could be used as predictors for success/failure of NSPT.

Materials and Methods

Patients with periodontitis (n = 24) were included in this clinical trial and full‐mouth periodontal charting was recorded for each patient. GCF samples from periodontal pockets with probing pocket depth (PPD) 4–6 mm from the interproximal surfaces of anterior and premolar teeth were obtained. These sites subsequently received NSPT and were clinically re‐evaluated after 1 and 3 months. Levels of GCF E‐cadherin and TAC levels were assayed using ELISA.

Results

All clinical periodontal parameters were significantly improved 3 months after completion of NSPT. These outcomes were associated with a significant decrease in E‐cadherin levels and GCF volume, while TAC levels were significantly increased in samples obtained in follow‐up appointments. Binary regression model analysis showed that PPD, GCF volume, E‐cadherin, and TAC levels could significantly (p < .05) predict the outcomes of NSPT. The cut‐off points for PPD, GCF volume, E‐cadherin and TAC were 5 mm, 4 × 10−3, 1267.97 pg/mL and 0.09 μmol/g, respectively.

Conclusion

NSPT improved clinical parameters along with increased antioxidants capacity and epithelial pocket lining integrity. Discrimination of favorable/unfavorable responsiveness of periodontally diseased sites to NSPT could be possible by using GCF volume, PPD, E‐cadherin and TAC level assessments.

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Tue Mar 30 2021
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Local Dependence for Bivariate Weibull Distributions Created by Archimedean Copula
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In multivariate survival analysis, estimating the multivariate distribution functions and then measuring the association between survival times are of great interest. Copula functions, such as Archimedean Copulas, are commonly used to estimate the unknown bivariate distributions based on known marginal functions. In this paper the feasibility of using the idea of local dependence to identify the most efficient copula model, which is used to construct a bivariate Weibull distribution for bivariate Survival times, among some Archimedean copulas is explored. Furthermore, to evaluate the efficiency of the proposed procedure, a simulation study is implemented. It is shown that this approach is useful for practical situations and applicable fo

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Publication Date
Sun Dec 07 2014
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The Modified Quadrature Method for solving Volterra Linear Integral Equations
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In this paper the modified trapezoidal rule is presented for solving Volterra linear Integral Equations (V.I.E) of the second kind and we noticed that this procedure is effective in solving the equations. Two examples are given with their comparison tables to answer the validity of the procedure.

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Are obese women potential for inflammatory process? A pilot study
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New Commands to Improve the Monitor for the ELWE Microcomputer
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The base of the ELWE MPF-1 microcomputer is the Z80 microprocessor. The Z80 programs are written in assembly language. The main advantage of assembly language is that: it is much faster to code and the mnemonics makes it easier for the user to remember the instruction. The purpose of this paper is to improve the monitor process for didactic microcomputer ELWE MPF-1 by appending new powerful commands (MOVE, FILL, SEARCH and COMPARE) to the existing monitor to make it more useful and flexible. 8085 assembly language is used to execute this program. The letters used for abbreviation: M for MOVE, F for FILL, S for SEARCH and C for COMPARE.

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Fri Jan 01 2021
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Linear Regression Model Using Bayesian Approach for Iraqi Unemployment Rate
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