Adrenomedullin (ADM) is a strong vasodilator peptide that was first identified in human pheochromocytoma and known to expressed in numerous cell types and believed to have pleiotropic impacts on pregnancy-related vascular adaptations and fetal growth. Mid-regional pro-adrenomedullin (MR-proADM) peptide which secreted in an equimolar concentration to ADM used to quantify ADM in plasma since it has longer half-life with more availability than ADM and for that reason the present study determine the serum MR-proADM level in the plasma of pregnant women with and without GDM and determine any potential correlation between MR-proADM and gestational diabetes mellitus (GDM) in an observational case–control study conducted on

Background: We compared the pattern of cutaneous dermatoses among Iraqi females of all ages between 4 months preceding the coronavirus disease 2019 (COVID-19) pandemic, and the same months 1 year later within the COVID-19 pandemic.

Methods: This was a cross-sectional study, that targeted all female patients attending an outpatient clinic for dermatology and venereology in Al-Kindy teaching hospital, Baghdad between October 2019 to the end of January 2020, and the same 4-month duration 1 year later (October 2020 to the end of January 2021) after the COVID-19 peak period had passed and there was no or partial curfew to exclude seasonal impact.

Results

Polycystic ovary syndrome (PCOS) is reproductive, endocrine, and metabolic disorder affecting females. The pathology of PCOS is complicated and associated to chronic low-grade inflammation, this includes a disruption in pro-inflammatory factor production, leukocytosis, and endothelial cell dysfunction, also associated with high level of pro-inflammatory cytokines, chemokines and leukocyte count. In addition, PCOS is characterized by hormonal and immunological dysfunction. Inflammation of the ovary affects ovulation and induces or aggravates systemic inflammation. Macrophage inflammatory protein-1 (MIP-1), a pro-inflammatory chemokine, is crucial in the recruitment of inflammatory and immunological cells to the place of inflammation

This study aimed to estimate the serum levels of interleukin-1 alpha (IL-1α) and interleukin-36 alpha (IL-36α) in women with polycystic ovary syndrome (PCOS) and to examine their relationship with age and body mass index (BMI). A total number of collected specimens are 75, which were 45 patients and 30 healthy control women participated in this study from different private clinics in Baghdad/ Iraq, for the period starting December 2023 to February 2024. The age ranges for both patients and controls was 18–40 years, with mean ages of 25.60 ± 6.38 years for patients and 29.44 ± 11.61 years for controls. BMI was determined for all participants, revealing a statistically highly

Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reductio

The purpose of current study is to analyze the computer textbooks content for intermediate stage in Iraq according to the theory of multiple intelligence. By answering the following question “what is the percentage of availability of multiple intelligence in the content of the computer textbooks on intermediate stage (grade I, II) for the academic year (2017-2018)? The researcher followed the descriptive analytical research approach (content analysis), and adopted an explicit idea for registration. The research tool was prepared according the Gardner’s classification of multiple intelligence. It has proven validity and reliability. The study found the percentage of multiple intelligence in the content of computer textbooks for the in

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class