Background: osteoporosis is characterized by a reduction in bone mineral density, skeletal microstructure breakdown, increased bone fragility, and fracture susceptibility. Osteopenia is the preceding step to osteoporosis because it causes a decrease in bone mass, osteoporosis reduces a person's quality of life. Periostin (encoded by Postn), its name is derived from the fact that it was first detected in periosteal osteocytes and osteoblasts. Periostin deficiency has been linked to osteoporosis and weak bones. Study objectives: The purpose of this study was to determine periostin levels in serum of Iraqi patients with osteoporosis and osteopenia, and it is also possible to consider periostin as a diagnostic factor to follow the progression o

Background: Prolactin is a hormone, as well as a cytokine which is synthesized and secreted from the anterior pituitary gland and various extra pituitary sites including immune cells under control of a superdistal promoter that contains a single nucleotide polymorphism -1149 G/T. Rheumatoid Arthritis has been associated with increased serum prolactin levels.Objectives: To investigate the association of the extra pituitary -1149 G/T promoter polymorphism among Iraqi rheumatoid arthritis patients and prolactin levels.Methods: We tested 73 patients with rheumatoid arthritis and 40 healthy individuals. The DNA samples were genotyped using the Polymerase Chain Reaction-Restriction fragment Length Polymorphism method and the levels of prolacti

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,

Chronic Kidney Disease (CKD) is a public health problem and many studies support the link between kidney dysfunction and cardiovascular events.  Aldosterone has been shown for decades that a plasma aldosterone concentration is elevated in CKD. Whilst, Osteoprotegerin (OPG), after its capacity to protect bone, also osteoprotegerin is elevated in patients with chronic kidney disease (CKD), where it could predict the deterioration of kidney function, cardiovascular, vascular events and all-cause mortality. On the other hand, fibroblast growth factors (FGFs), in patients with CKD, its levels seem to increase progressively as kidney function worsens. The aim of the present study is to assess the correlations between serum osteoprotegerin

Staphylococcus are cause hospital community acquired infection and they are an important cause of health –care associated infection.The Coagulase positive Staphylococcus are Staphylococcus aureus which can implicated in toxic shock syndrome. Methicillin and Vancomycin Staphylococcus aureus resistant (MRSA, VRSA) become major cause of hospital- acquired infection and community acquired infection.Coagulase negative staphylococcus emerged as major cause of infection in immunocompromised patients.The main objective of this study was to evaluate the distribution of Staphylococci among leukemic patients since it is well known that leukemic patients are prone to be infected easily due to their immunosuppressed status.This study was undertaken b

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when