Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder that found during gestation and is define as hyperglycemia of variable severity with onset or first recognition during gestation that does not clearly characterize any form of the preexisting diabetes (American Diabetes Association [1]). It affects approximately 16.5% of pregnancies worldwide (Plows, et al.[2]). The placenta is an organ that connects the mother and her fetus during pregnancy (Gul, et al.[3]). In the placenta, glucose can be transformed into glycogen for storage by either glycogen synthase or using glycogenin as a prime. However, the function of glycogen deposition stays a matter of debate, it may be the source of fuel for placenta itself or the storage pool for the later use by fetus in the times of need, while the importance of the placental glycogen stays elusive. Increasing evidence indicates that the changed glycogen metabolism and the deposition accompanies with numerous pregnancy complications that harmfully affects fetal development specially
Background: The Apgar score is calculated based on the five features: heart rate, reflexes, color, muscle tone, and respiratory effort. Each element is scored from 0 to 2, with a total probable score of 10 at 1 minute and 5 minute. Aim: To study the influence of gestational diabetes mellitus (GDM) and diabetes mellitus on APGAR score in neonate. Materials and methods: The samples were studied from Department of Obstetrics and Gynaecology in Al-Imamain Al-Kadhimiyain (AS) Medical city, Baghdad Teaching Hospital and Al-Karkh Maternity Hospital in the period between 1 December 2016 and 1 may 2017, after obtaining the approval from Iraqi Ministry of Health. A total of 102 neonates were included in this study which includes 34 neonates of mother
... Show MoreDiabetes Mellitus is a group of metabolic diseases characterized by increasing of glucose level in plasma compared with normal value (hyperglycemia). This disease also causes elevation of lipid profile levels except HDL (High density lipoproteins) which increased relatively. The effects of the polyphenolic mixture (catechins, epicatechins, procyanidin B1, procyanidin B2 and procyanidin C1) on total cholesterol (TC), triacylglycerol (TG), high density lipoprotein (HDL) and low density lipoprotein (LDL) were studied in (30) streptozotocin-induced diabetic mice with (20-25)gm weight. Mice were given (30 mg/mL) of Polyphenolic Cocoa beans Extracts (CE) once daily for (7) days before Streptozotocin STZ injection and for (21 day) there after. A
... Show MoreBackground: EBV infection in tissue micro-environment is challenged by the precisely regulated survivaland apoptosis mechanisms. Abnormal bcl-2 proto-oncogene expression in colonic carcinomas allowsaccumulation and propagation of these genetically altered cells.Objective: To analyze the relevant concordance of BCL-2 gene , EBNA1 s and LMP-1-EBV expression inissues from a group of Iraqi patients with colonic adenocarcinomas.Patients and Methods: One hundred (100) tissue biopsies, belonged to (40) patients with colorectalcancers, (40) patients with benign colon tumors, and (20) apparently normal colorectal control tissues,were enrolled in this study. The detection of EBNA1 s and LMP-1-EBV as well as BCL-2 was done byimmunohistochemist
... Show MoreWe propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St
... Show MoreLO Hamza, Indian Journal of Natural Sciences, 2018 - Cited by 3