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Salivary E‐cadherin as a biomarker for diagnosis and predicting grade of periodontitis
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Abstract<sec><title>Objectives

To determine the abilities of salivary E‐cadherin to differentiate between periodontal health and periodontitis and to discriminate grades of periodontitis.

Background

E‐cadherin is the main protein responsible for maintaining the integrity of epithelial‐barrier function. Disintegration of this protein is one of the events associated with the destructive forms of periodontal disease leading to increase concentration of E‐cadherin in the oral biofluids.

Materials and Methods

A total of 63 patients with periodontitis (case) and 35 periodontally healthy subjects (control) were included. For each patient, periodontal parameters including bleeding on probing (BOP), probing pocket depth (PPD), and clinical attachment level (CAL) were recorded. Concentration of salivary E‐cadherin was determined by ELISA. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to determine the diagnostic potentials of E‐cadherin.

Results

Level of salivary E‐cadherin was significantly higher in periodontitis cases than controls. The ROC analysis showed that salivary E‐cadherin exhibits excellent sensitivity and specificity (AUC 1.000) to differentiate periodontal health from periodontitis with a cutoff concentration equal to 1.325 ng/mL. The AUCs of E‐cadherin to differentiate grade A from grade B and C periodontitis were 0.731 (cutoff point = 1.754 ng/mL) and 0.746 (cutoff point = 1.722 ng/mL), respectively. However, the AUC of salivary E‐cadherin to differentiate grade B from grade C periodontitis was lower (0.541). Additionally, BOP and PPD were significantly and positively correlated with the concentration of salivary E‐cadherin.

Conclusion

Salivary E‐cadherin exhibited excellent sensitivity and specificity to differentiate periodontitis from a healthy periodontium. The level of accuracy of E‐cadherin was also sufficient to recognize grade A periodontitis from grade B and C periodontitis.

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Publication Date
Mon Dec 30 2024
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Formation evaluation for Mauddud Formation in Bai Hassan oilfield, Northern Iraq
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   Formation evaluation is a critical process in the petroleum industry that involves assessing the petrophysical properties and hydrocarbon potential of subsurface rock formations. This study focuses on evaluating the Mauddad Formation in the Bai Hassan oil field by analyzing data obtained from well logs and core samples. Four wells were specifically chosen for this study (BH-102, BH-16, BH-86, and BH-93). The main objectives of this study were to identify the lithology of the Mauddud Formation and estimate key petrophysical properties such as shale volume, porosity, water saturation, and permeability. The Mauddud Formation primarily consists of limestone and dolomite, with some anhydrites present. It is classified as a clean for

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Publication Date
Sat Dec 01 2018
Journal Name
Al-khwarizmi Engineering Journal
Wireless Network Control System for Electro Stati Precipitator in Cement Plants
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    Wireless control networks (WCNs), based on distributed control systems of wireless sensor and actuator networks, integrate four technologies: control, computer network and wireless communications. Electrostatic precipitator (ESP) in cement plants reduces the emissions from rotary kiln by 99.8% approximately. It is an important thing to change the existing systems (wireline) to wireless because of dusty and hazardous environments. In this paper, we designed a wireless control system for ESP using Truetime 2 beta 6 simulator, depending on the mathematical model that have been built using identification toolbox of Matlab v7.1.1. We also study the effect ofusing wireless network on performance and stability of the closed l

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Publication Date
Mon Jan 01 2018
Journal Name
Matec Web Of Conferences
Brain Tumour Detection using Fine-Tuning Mechanism for Magnetic Resonance Imaging
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In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

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Publication Date
Tue Jan 01 2019
Journal Name
Journal Of Global Pharma Technology
Modified ZnO for efficient photo-catalysis by Silver/Graphite oxide nanoparticles
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Publication Date
Sun Apr 01 2018
Journal Name
2018 9th International Conference On Information And Communication Systems (icics)
An intersection-based segment aware algorithm for geographic routing in VANETs
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In networking communication systems like vehicular ad hoc networks, the high vehicular mobility leads to rapid shifts in vehicle densities, incoherence in inter-vehicle communications, and challenges for routing algorithms. It is necessary that the routing algorithm avoids transmitting the pockets via segments where the network density is low and the scale of network disconnections is high as this could lead to packet loss, interruptions and increased communication overhead in route recovery. Hence, attention needs to be paid to both segment status and traffic. The aim of this paper is to present an intersection-based segment aware algorithm for geographic routing in vehicular ad hoc networks. This algorithm makes available the best route f

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Publication Date
Sat May 24 2025
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Iraqi Journal For Computer Science And Mathematics
Intrusion Detection System for IoT Based on Modified Random Forest Algorithm
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An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men

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Publication Date
Tue Dec 05 2023
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Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications
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With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

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Publication Date
Mon Dec 05 2022
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MSRD-Unet: Multiscale Residual Dilated U-Net for Medical Image Segmentation
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Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s

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Publication Date
Sun Jun 12 2011
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The MR affect on optical properties for poly (Vinyl alcohol) films
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optical properties of pure poly(vinyl Alcohol) films and poly(vinyl Alcohol) doped with methyl red were study, different percentage prepared with constant thickness using casting technique. Absorption, Transmission spectra have been recorded in order to study the optical parameters such as absorption coefficient, energy gap, refractive index, Extinction coefficient and dispersion parameters were measured in the wavelength range (200-800)nm. This study reveals that the optical properties of PVA affect by increasing the impurity concentration.

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Publication Date
Wed Jun 20 2018
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Al-academy
Directed by Treatment for single-place events in the cinematographic medium
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The cinematographer mediates through the means of cinema and television a set of elements complementing each other in the light of developments in various sciences, culture and arts for the purpose of conveying the meaning to the recipient and achieve aesthetic taste. Despite the diversity of cinematographic media with its multiple forms, The researcher started from the principle of definition and knowledge of a technical phenomenon that emerged in the cinematographic medium through the treatment of dramatic events through the solutions of the exit line depends on the narrative of events in one place contributes to attract Mam spectator since this interesting phenomenon in the mediator, there .van question arises the adoption of that vis

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