Background: Diabetes mellitus and osteoporosis are two common medical disorders that are becoming more common as the population ages. T2DM patients have a higher fracture hazard, having a high BMD, which is primarily due to the raise hazard of falling. Macrophage colony-stimulating factor (M-CSF) is one of the hematopoietic growth factor family, and It plays an important function in fracture repair by attracting stem cells to the fracture site and influencing the production of hard calluses by promoting osteoclast genesis.Aims of study: The purpose of this research was to assess the blood level of macrophage colony-stimulating factor in Iraqi osteoporotic patients with and without type 2 diabetes. in addition, that M-CSF may be a predictive marker for osteoporosis in T2DM patients .Subjects & Methods: This study was conducted between October 2021 to March 2022 in Medical City of Baghdad Teaching Hospital. The current study included 92 individuals (females and males) aged 40-65 years’ old, 67 of them are patients and 25as a control. The lumber spine's bone mineral density was determined using dual energy x-ray absorptiometry (DEXA)scan to diagnose these patients. Patients divided into (20) person as T2DM patients, (27) person as osteoporosis patients, and (20) as osteoporosis patients with T2DM. Results: The current study showed an important increase in serum M-CSF of osteoporosis patients with and without T2DM groups when compared with control, also, there was no significance increase in M-CSF level in T2DM patients comparing with control. Also, there was an important negative relation between M-CSF and bone mineral density (BMD) In osteoporosis patients, there was a substantial positive connection between M-CSF, FBS, and HbA1C. Conclusions: The current study demonstrated that serum macrophage colony-stimulating factor (M-CSF) levels was significantly elevated in osteoporosis patients with and without T2DM, Therefore, this parameter may be a diagnostic marker for osteoporotic patients. In addition, that diabetic patients may be prone to osteoporosis, and M-CSF may be a predictive biochemical marker for development of osteoporosis in type 2 diabetic patients.
Background: Osteoporosis is one of the major public health problems from which more and more people in the world are suffering. There is evidence suggesting that osteoporosis knowledge is one contributor to osteoporosis preventive behavior. Aim of the Study: To assess the knowledge regarding osteoporosis risk factors, prevention, and management in women of reproductive ages. To identify any association between knowledge and studied factors.
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Objectives: to assess Socio Demographic, Reproductive Characteristics, and healthy dietary behaviors. among women with osteoporosis . To determine the relationship between the socio demographic characteristics, reproductive data and dietary related behaviors. Methodology: A descriptive analytic design was conducted on Non- Probability ( purposive sample) of (90) women who have suffering from osteoporosis attend to (DEXADual-Energy X-ray Absorptiometry) unit in Merjan Teaching Hospital in Hilla City. A questionnaire has been used as a tool of data collection and consists of three part ;including : Soci
Epithelial ovarian cancer is the leading cause of cancer deaths from gynecological malignancies. Angiogenesis is considered essential for tumor growth and the development of metastases. VEGF and IL?8 are potent angiostimulatory molecules and their expression has been demonstrated in many solid tumors, including ovarian cancer.VEGF and IL-8 concentrations were measured by ELISA test (HumanVEGF,IL-8). Bioassay ELISA/ US Biological / USA).The median VEGF and IL-8 levels were significantly higher in the sera of ovarian cancer patients than in those with benign tumors and in healthy controls.Pretreatment VEGF and IL-8 serum levels might be regarded as an additional tool in the differentiation of ovarian tumors.
Background. Diabetes mellitus (DM) is a prevalent disease that, if not appropriately managed, can lead to a variety of problems, including diabetic foot. Glycated hemoglobin A1c (HbA1c), FBS, amylase, and lipase are important diabetic management indicators now employed as diagnostic tests. Objective. This study aimed to evaluate the value of amylase and lipase as predictive markers in patients with diabetic foot. Patients and methods. This study included 50 patients who reported to Baghdad Hospital with diabetic feet between November 2023 and February 2025. All patients had their HbA1c, amylase, lipase, and FBS levels tested. Means, independent t-tests, and the F-test were used in the statistical analysis. Results. The study evaluat
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A new algorithm is proposed to compress speech signals using wavelet transform and linear predictive coding. Signal compression based on the concept of selecting a small number of approximation coefficients after they are compressed by the wavelet decomposition (Haar and db4) at a suitable chosen level and ignored details coefficients, and then approximation coefficients are windowed by a rectangular window and fed to the linear predictor. Levinson Durbin algorithm is used to compute LP coefficients, reflection coefficients and predictor error. The compress files contain LP coefficients and previous sample. These files are very small in size compared to the size of the original signals. Compression ratio is calculated from the size of th
... Show MoreBackground Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm
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