This study was aimed to determine bone formation markers (OST and BALP) and lysyl oxidase in diabetes and non-diabetes Iraqi acromegaly patients in addition to find the relationship among these parameters. The present study conducted 60 acromegalic patients (30 diabetes & 30 non diabetes) attending National Diabetes Center / AL-Mustansiriya University/Baghdad, and 30 healthy individuals as a control group aged (35-60) years. All patients were administrated Sandostatin drug, and they were diagnosed by physician in the hospital.FBG, GH, IGF-1, OST, BALP, and LOX were determined in all groups. The results showed a highly significant rise in all parameters (GH, IGF-1, FBG, OST, BALP, and LOX values in serum of all pati

This study was aimed to determine bone formation markers (OST and BALP) and lysyl oxidase in diabetes and non-diabetes Iraqi acromegaly patients in addition to find the relationship among these parameters. The present study conducted 60 acromegalic patients (30 diabetes & 30 non diabetes) attending National Diabetes Center / AL-Mustansiriya University/Baghdad, and 30 healthy individuals as a control group aged (35-60) years. All patients were administrated Sandostatin drug, and they were diagnosed by physician in the hospital.FBG, GH, IGF-1, OST, BALP, and LOX were determined in all groups. The results showed a highly significant rise in all parameters (GH, IGF-1, FBG, OST, BALP, and LOX values in serum of all patients when compared with n

Backgrond: One of the major causes of impaired movements post-stroke is the abnormal phasing of paralytic muscles. Many studies suggested that inappropriate muscle phasing may be associated with enhanced transmission in the monosynaptic Group Ia afferent pathway in the affected limb of post-stroke patients and Group Ia reflexes are abnormally elevated and fail to decrease in amplitude during locomotion.

Objectives: This study was conducted to identify the changes in the Soleus muscle H-reflex excitability at rest in the affected lower limb of post-stroke patients as compared to the contra lateral side and of normal controls.

Patients and methods: The excitability of the m

Addition of bioactive materials such as Titanium oxide (TiO₂), and incorporation of bio inert ceramic such as alumina (Al₂O₃), into polyetheretherketone (PEEK) has been adopted as an effective approach to improve bone-implant interfaces. In this paper, hot pressing technique has been adopted as a production method. This technique gave a homogenous distribution of the additive materials in the proposed composite biomaterial. Different compositions and compounding temperatures have been applied to all samples. Mechanical properties and animal model have been studied in all different production conditions. The results of these new TiO₂/Al₂O₃/PEEK biocomposites with different

The research aims to employ one of the most important strategies for recovery from the crisis of the Covid-19 pandemic, which ravaged the economies of the entire world and its various sectors, including the banking sector, through financial technology that is based on digital transformation to achieve financial sustainability and the creation of innovative financial value chains in light of the decline in the banking sector as a result of The negative effects of the Covid-19 pandemic, be guided by the relevant international accounting standards to control the risks associated with financial technology. To recover from the Covid-19 crisis, the research came out with a set of recommendations, most notably financial technology from

Background: Use of magnetic resonance imaging (MRI) to calculate skeletal age is a novel idea. MRI provides excellent soft-tissue contrast and multiplanar cross-sectional imaging capability. It could be used as an alternative method of skeletal age determination.
Objectives: To study the value of MRI in estimating the age of healthy Iraqi adolescent males and to compare the obtained results with other countries records.
Population and methods: This cross sectional study was applied on 179 healthy adolescent males between the ages of 13 to18 years in MRI unit at radiology institute in medical city, Baghdad – Iraq. This study was carried out from November 2011 to December 2012. Magnetic resonance imaging of the left wrist was perfo

The COVID-19 pandemic has profoundly affected the healthcare sector and the productivity of medical staff and doctors. This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. A cross-sectional study was conducted on 960 participants from different specialties between June 1, 2022, and April 5, 2023. The study collected demographic data, including age, gender, and socioeconomic status, as well as information on participants' sleeping habits and any COVID-19 complications they experienced. The findings indicate a significant decline in the productivity of medical staff and doctors, with an average reduction of 23% during the post-COVID-19 period. T

  The Covid-19 virus disease has been shown to affect numerous organs and systems including the liver. The study aimed to compare lipid profiles and liver enzyme levels in individuals who had recovered from Covid-19 infection. To achieve the study objectives, liver Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT), Alkaline phosphatase (ALP),  Random Blood Sugar (RBS) and Lipid profile which include cholesterol, High-Density Lipoprotein (HDL), Triglycerides (T.G), Low-Density Lipoprotein (LDL), and Very low-density Lipoprotein (VLDL) were determined.

One hundred twenty serum samples were obtained, of which fifty samples were utilized as the control healthy persons (not affected by COVID) and seventy samples came f

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings