Gastroesophageal reflux disease (GERD) is a prevalent clinical condition, that affects millions of individuals worldwide. Objective: To assess the level of soluble HLA-E (sHLA-E) as a biomarker in the diagnosis and immunopathogenesis of GERD patients. Methods: The case-control prospective study included 40 GERD patients who were consulted at the Gastroenterology Unit of AlKindy Teaching Hospital, as along with 40 healthy control subjects. The study period extended from January 2023 to May 2024. Blood was drawn from both groups and serum was separated to assesssHLA-E using a sandwich enzyme-linked immunosorbent assay (ELISA) kit. Results: There was a statistically significant difference in sHLA-E levels between GERD patients and healthy controls (P=0.021). The median sHLA-E level was significantly higher in GERD patients (0.370 ng/mL) compared to controls (0.148 ng/mL). A receiver operating characteristic (ROC) curve was generated to evaluate the diagnostic performance of soluble HLA-E (sHLA-E) in predicting GERD. The area under the ROC curve (AUC) was calculated to assess the discriminatory ability of sHLA-E with a vlue of 0.649 (95% CI: 0.534-0.752, p = 0.021). The optimal cutoff value for sHLA-E was determined to be ≤0.65 ng/mL, with a sensitivity of 85.1%, specificity of 27.3%, positive predictive value of 65.9%, negative predictive value of 69.4%, and accuracy of 35.0%. Conclusion: The study provides evidence of an association between elevated sHLA-E levels and GERD. It also suggests that sHLA-E has a moderate discriminatory ability as a biomarker in predicting GERD. © 2025, Shiraz University of Medical Sciences. All rights reserved.
Aim of the study is to find any correlation between obesity (insulin resistance) and type I diabetes in children. Obesity and diabetes mellitus are the common health problems, and obesity is common cause of the insulin resistance. The results revealed marked increased in glucose, insulin, HbAlc and insulin resistance in obese diabetic type I patients comparing to control group they were obese and non-obese found to be within normal values for glucose, insulin, FIbAlc , and insulin resistance.
Six species of aquatic snails were sorted from three sites, the irrigation canal of Baghdad University campus (S1), River Tigris at Al-Za'afaraniah district / Baghdad(S2) , and River Euphrates at Al-Haideriah district Al-Najaf province(S2). The species included Melanopsis nodosa ;Melanoides tuberculata ; Thaiodaxsas jordani ; Lymnaea auricularia ; Physa acuta and Bellamya bengalensis. The first specis recorded the highest total number and was found in high density in the R. Euphrates site (S3), while the last species was the most widely distributed species, and found in all study sites. The last three species were found in Tigris river (S2) , while the first and last species were collected from the irrigation canal (S1).The result reveal
... Show MoreObjectives: The study aims at assessing the parental treatment and aggressive behaviors among adolescents and to find out the association between parental treatment and aggressive behavior.
Methodology: A descriptive correlational design that is initiated for the period of January 1st to July 5th, 2021; The sample of the study includes 220 from the intermediate school male students in schools in the Karkh and Rusafa in Baghdad have ranged in age from (13-15) years, the researcher used the convenient sampling method (non-probability sample) in which the students were selected purposively. Parental Treatment Scal
... Show MoreIn order to select the optimal tracking of fast time variation of multipath fast time variation Rayleigh fading channel, this paper focuses on the recursive least-squares (RLS) and Extended recursive least-squares (E-RLS) algorithms and reaches the conclusion that E-RLS is more feasible according to the comparison output of the simulation program from tracking performance and mean square error over five fast time variation of Rayleigh fading channels and more than one time (send/receive) reach to 100 times to make sure from efficiency of these algorithms.
The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to resul
... Show MoreThe precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences
... Show MoreThe pancreatic ductal adenocarcinoma (PDAC), which represents over 90% of pancreatic cancer cases,
has the highest proliferative and metastatic rate in comparison to other pancreatic cancer compartments. This
study is designed to determine whether small nucleolar RNA, H/ACA box 64 (snoRNA64) is associated with
pancreatic cancer initiation and progression. Gene expression data from the Gene Expression Omnibus (GEO)
repository have shown that snoRNA64 expression is reduced in primary and metastatic pancreatic cancer as
compared to normal tissues based on statistical analysis of the in Silico analysis. Using qPCR techniques,
pancreatic cancer cell lines include PK-1, PK-8, PK-4, and Mia PaCa-2 with differ