Background: Most prevalent chronic liver disease in developed and developing nations is non-alcoholic fatty liver disease. From fatty liver, which often has benign, non-progressive clinical history, to non-alcoholic steatohepatitis, a more serious variant of fatty liver that can lead to cirrhosis and end-stage liver disease, non-alcoholic fatty liver disease encompasses broad spectrum of diseases. The gold standard for determining extent of hepatic fibrosis is still liver biopsy; however, number of noninvasive tests have been established to make diagnosis and assess effectiveness of treatment.

Objective: Aim of study was to assess effectiveness of the combination of fibroscan and

Background Bilateral cleft lip deformity is much more difficult to correct than unilateral cleft lip deformity. The complexity of the deformity and the sensitive relationships between the arrangement of the muscles and the characteristics of the external lip necessitate a comprehensive preoperative plan for management. The purpose of this study was to evaluate the repair of bilateral cleft lip using the Byrd modification of the traditional Millard and Manchester methods. A key component of this repair technique is focused on reconstruction of the central tubercle.

Methods Fourteen patients with mean age of 5.7 months presented with bilateral cleft lip deformity and were operated on using a mod

We studied the relationship between DNA sequencing of interleukin-10 (IL-10) gene promoter for -1082 (A/G) and -592 (A/C) positions with the concentration of IL-10 in blood serum of Iraqi children with type 1 diabetes mellitus (T1D). Fifty blood serum samples collected from children with age ranged between 7-12 years. Thirty-five blood samples collected from patient children with T1D, and compared with 15 healthy children age matched as control sample. The results revealed decreasing in anti-inflammatory IL-10 concentration in T1D patient’s blood serum (0.068 Pg/ml) as compared with the control sample (0.111 Pg/ml). No significant differences were found in interleukin concentration between the studied samples when they analyzed with the M

New series of imidazole[1,2-a]pyridine-sulfonamides was designed and synthesized from 2-aminopyridine, which was reacted with p-bromo phenacyl bromide in the present of MgO to produce the corresponding imidazole[1,2-a]pyridine, which was then reacted with chlorosulfonic acid to produce 2-(4-bromophenyl)imidazole[1,2-a]pyridine-3-sulfonyl chloride [2]. Following that, treatment of (2) with different amines using the grand method to generate imidazole [1,2-a] pyridine sulfonamides. All the synthesized compounds have been characterized by FTIR, 1HNMR and 13CNMR and C.H.N analysis. The DFT, POM analysis and molecular docking were carried out on for all final compounds to investigate drug like attributes, and the results revealed showed that the

Colorectal cancer CRC is the third most commonly diagnosed cancer in males and the second in female, so it is a common and life-threatening disease serum of 48 males and 40 females suffering from CRC disease as group one (G1) without taking a chemotherapy dose, the same patients group tow (G2) after taking the first chemotherapy dose, while group three (G3) whose patients in (G1) and (G2) after second taking a chemotherapy dose. and group four (G4) consist of (30 males and 30 females) healthy Iraqi control. Results showed a significant increase in IL-17, IL-17RA and CEA in G1, while there was a significant decrease in vitamin D concentration in G1 than other groups, also there was positive (+ ve) significant correlation between IL-17 and CE

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimu

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

The recent emergence of sophisticated Large Language Models (LLMs) such as GPT-4, Bard, and Bing has revolutionized the domain of scientific inquiry, particularly in the realm of large pre-trained vision-language models. This pivotal transformation is driving new frontiers in various fields, including image processing and digital media verification. In the heart of this evolution, our research focuses on the rapidly growing area of image authenticity verification, a field gaining immense relevance in the digital era. The study is specifically geared towards addressing the emerging challenge of distinguishing between authentic images and deep fakes – a task that has become critically important in a world increasingly reliant on digital med

ECG is an important tool for the primary diagnosis of heart diseases, which shows the electrophysiology of the heart. In our method, a single maternal abdominal ECG signal is taken as an input signal and the maternal P-QRS-T complexes of original signal is averaged and repeated and taken as a reference signal. LMS and RLS adaptive filters algorithms are applied. The results showed that the fetal ECGs have been successfully detected. The accuracy of Daisy database was up to 84% of LMS and 88% of RLS while PhysioNet was up to 98% and 96% for LMS and RLS respectively.

The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this di