The systemic and resistant nature of metastatic castration-resistant prostate cancers (mCRPC) renders it largely incurable even after intensive multimodal therapy. Proliferation, survival, and epithelial-mesenchymal transition (EMT) are three fundamental events that are deeply linked to carcinogenesis.  Hence, it is necessary to find a new combination of several therapies, targeting those vital mechanisms without causing side effects. Significant research works have shown differential low expression of the metabolic Farnesoid X receptor (FXR) in primary and metastatic prostate cancer suggesting their importance in prostate pathogenesis. Obticholic acid (INT 747), a potent FXR agonist is widely used in primary biliary chola

Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

The aim of this study was investigating the correlation between elevation of Prolactin levels and the increase of the concentrations of total sialic acids. The study was performed on 149 women consisted of 93 infertile hyperprolactinimic women (patients), age ranged16-38 years old, and 56 normoprolactinemic women as a control group, 18-37 years old. Serum prolactin (PRL) and gonadotroph hormones (Follicle stimulating hormone FSH and Luteinizing hormone LH) were measured using enzymatic immunoassay (EIA) method, resorcinol method for serum total sialic acids (SIA). Patients were divided into four groups, each group represented the level of prolactin of infertile women as follow: G1= (21-30), G2= (31-40), G3= (41-50), and G4= (51-60) ng/mL. S

Aim: This study aims to determine both the level  of knowledge, attitude, and practice  in clinical breast examination in women and the relationship between knowledge and practice.Data collection was performed within a three-month period starting from February 2019 on.  Subjects and Method: A cross-sectional analytical study included 657 non-randomly selected women visiting primary health care centers in Baghdad city with an average of age between (20-59) years old.  Results: Only 51.8% ever heard of clinical breast examination. The mean knowledge score for clinical breast examination was (51.2±44). Only 5.5% of women performed regular clinical breast examinations. Participants who adequately practice clinical breast examinatio

Objectives: Osteoporosis (OP) is a systemic skeletal disorder characterized with bone mass loss and microstructure, resulting in fragility fractures. Continued secretion of Osteopontin (OPN), osteonectin (ON), osteocalcin (OCN), Parathyroid hormone (PTH) and Ca+2 lead to bone remodeling disorders, followed by bone loss and osteoporosis (OP). The current study aims to investigate the biochemical proteins OPN, OCN, and ON in postmenopausal women with osteoporosis and determine whether we could use them as good indicators for OP diagnostics. Materials and Methods: Case- control study carried out between December 2022 and July 2023. OP disease was confirmed among 108 Iraqi postmenopausal women randomly selected from different Iraqi hospitals, B

The 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

Background: Quality of life in brain tumor patients is an emerging issue and has prompted neurosurgeons to recon¬sider the need for cognitive assessment in the course of treatment. To date there has been a lack of comprehensive neuropsychological assessment performed preoperatively and in the acute postoperative period in our hospitals.Objectives: to establish the effects of tumors and their surgical treatment, from a neuropsychological perspective, on cognitive functioning in patients with cerebral Gliomas. Methods: This is a prospective study conducted in the Neurosurgical Hospital in Baghdad, Iraq, during the period from January 1999 to January 2001. Any patient admitted during the period of the study with clinical history, signs, sy

The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

This study is carried out on patients with type 2 diabetes mellitus to assess the lipid profile, malondialdehyde and glutathione. Our study is concerned with 51 (Iraqi Arab females) patients of type 2 diabetes mellitus compared with 31 control subjects unified in age, sex and ethnic background. Lipid profile is measured by using commercially available kits, while the serum MDA and glutathione levels are measured by means of sandwich ELISA test using commercially available kits. Serum MDA is significantly higher (P<0.001) while glutathione is significantly lower (P<0.001) in type 2 diabetic patients when compared to the control. The normal levels of MDA (3.82 ± 0.77n mol/ml) and GSH (2.23 ± 0.54 µg/ml) recorded for the non-diabetic female