Background: Cluster of differentiation 14 (CD14) is a serum/cell surface glycoprotein; and it is a pattern recognition receptor. CD14 expressed on the surface of various cells, or it found soluble in saliva and other body fluids. It has been proposed that soluble CD14 (sCD14) may play a protective role by controlling Gram negative bacterial infections through its capacity to bind lipopolysaccharide. This study was conducted to assess the level of soluble CD14 in saliva of patients with different periodontal diseases and healthy subjects and determine its correlation with clinical periodontal parameters. Materials & Methods: A total of 80 subjects, age ranged (25-50) years old, divided into three main groups, group ? consisted of 45 chronic

Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective

Breast cancer is the commonest cancer affecting women worldwide. Different studies have dealt with the etiological factors of that cancer aiming to find a way for early diagnosis and satisfactory therapy. The present study clarified the relationship between genetic polymorphisms of BRCA1 & BRCA2 genes and some etiological risk factors among breast cancer patients in Iraq. This investigation was carried out on 25 patients (all were females) who were diagnosed as breast cancer patients attended AL-Kadhemya Teaching Hospital in Baghdad and 10 apparently healthy women were used as a control, all women (patients and control) aged above 40 years. The Wizard Promega kit was used for DNA isolation from breast patients and normal individuals. B

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,

Objectives: To identify the frequency and types of microsatellite instability among a group of sporadic CRC patients and to correlate the findings with clinicopathological characteristics. Methods: During an 8-month period, all patients with sporadic CRC who attended to two teaching hospitals in Baghdad, Iraq were recruited to this cross-sectional study regardless of age, sex, ethnicity, or tumor characteristics. Demographic, clinical, and histopathological features were recorded. DNA was extracted from FFPE-blocks of the resected tumors and normal tissues. PCR amplification of five microsatellite mononucleotide repeat loci (BAT25, BAT26, NR-21, NR-24, and MONO-27) and 2 pentanucleotide repeat control markers (Penta C and Pent

Human herpes virus-8 (HHV-8) infection has increased recently in Arabic countries. HHV-8 in healthy persons does not necessarily cause life-threatening infection, and however, it causes a more severe infection among immunocompromised patients. The distribution of HHV-8 genotypes varies according to ethnicity and depends on the geographic region prior rapid development of global travel. A cross sectional prospective study included a hundred healthy blood donor samples with a mean age of (36.60±10.381), 81% were positive for molecular detection of HHV-8 DNA. PCR results for HHV-8 were strongly related with risk factors such as the number of sexual relations, previous surgeries, blood transfusion, dental operation, and the number of b

Cost estimation is considered one of the important tasks in the construction projects management. The precise estimation of the construction cost affect on the success and quality of a construction project. Elemental estimation is considered a very important stage to the project team because it represents one of the key project elements. It helps in formulating the basis to strategies and execution plans for construction and engineering.  Elemental estimation, which in the early stage, estimates the construction costs depending on  . minimum details of the project so that it gives an indication for the initial design stage of a project. This paper studies the factors that affect the elemental cost estimation as well as the rela

This study employs evolutionary optimization and Artificial Intelligence algorithms to determine an individual’s age using a single-faced image as the basis for the identification process. Additionally, we used the WIKI dataset, widely considered the most comprehensive collection of facial images to date, including descriptions of age and gender attributes. However, estimating age from facial images is a recent topic of study, even though much research has been undertaken on establishing chronological age from facial photographs. Retrained artificial neural networks are used for classification after applying reprocessing and optimization techniques to achieve this goal. It is possible that the difficulty of determining age could be reduce

This paper introduces a novel nonparametric hybrid cyber-intelligence-based statistical process control and anomaly detection framework in time series data. It is developed to overcome the shortcomings of the classical control schemes in dealing with complex, abnormal, and noisy input data, especially when it is autocorrelated. The proposed methodology combines three technical pillars: First, it utilizes a bidirectional long-short-term memory architecture (Bi-LSTM) to capture long-term time dependency and learn nonlinear patterns, leaving only true deviations as residuals that remove trends and noises from the market. Second, it adopts the Golden Eagle Optimizer (GEO) algorithm for optimal parameter selection. This intelligent algor