Background: The Aim Of This Study Is To Determine The Modalities Of Treatment Of GTD In Baghdad Teaching Hospital And To Assess The Efficacy Of Our Management Protocols.
Patients &Methods: Department Of Obstetrics & Gynecology- Baghdad Teaching Hospital. Retrospective Analysis Of Case Records Between January 1999 To December2000. 41 Patients' Data Were Reviewed For Age, Gravidity, Parity, Blood Group, Antecedent Pregnancy And Clinical Presentation At The Time Of Diagnosis. Monitoring Of Hcg Level Before And After Chemotherapy, Other Investigations Were Reviewed, Looking For Number, Size And Site Of Metastasis. The Patient Were Classified According To WHO Scoring System. We Evaluate The Lines )f Ma

     Structure of network, which is known as community detection in networks, has received a great attention in diverse topics, including social sciences, biological studies, politics, etc. There are a large number of studies and practical approaches that were designed to solve the problem of finding the structure of the network. The definition of complex network model based on clustering is a non-deterministic polynomial-time hardness (NP-hard) problem. There are no ideal techniques to define the clustering. Here, we present a statistical approach based on using the likelihood function of a Stochastic Block Model (SBM). The objective is to define the general model and select the best model with high quality. Therefor

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

CD-nanosponges were prepared by crosslinking B-CD with diphenylcarbonate (DPC) using ultrasound assisted technique. 5-FU was incorporated with NS by freeze drying, and the phase solubility study, complexation efficiency (CE) entrapment efficiency were performed. Also, the particle morphology was studied using SEM and AFM. The in-vitro release of 5-FU from the prepared nanosponges was carried out in 0.1N HCl.

5-FU nanosponges particle size was in the nano size. The optimum formula showed a particle size of (405.46±30) nm, with a polydispersity index (PDI) (0.328±0.002) and a negative zeta potential (-18.75±1.8). Also the drug entrapment efficiency varied with the CD: DPC molar ratio from 15.6 % to 30%. The SEM an

The aim of the research is to investigate the effect of cold plasma on the bacteria grown on texture of sesame paste in its normal particle and nano particle size. Starting by using the image segmentation process depending on the threshold method, it is used to get rid of the reflection of the glass slides on which the sesame samples are placed.  The classification process implemented to separate the sesame paste texture from normal and abnormal texture. The abnormal texture appears when the bacteria has been grown on the sesame paste after being left for two days in the air, unsupervised k-mean classification process used to classify the infected region, the normal region and the treated region. The bacteria treated with cold plasma, t

      Background: Left ventricular function and volumes have major diagnostic and prognostic importance in patients with various cardiac diseases, such as ischemic heart disease which is a life-threatening heart disease condition characterized by systolic dysfunction and a decrease in cardiac output. 

According to left ventricular ejection fraction, the degree of ischemic heart disease was classified as mild, moderate, and severe. To determine cardiac function and hemodynamics, the echocardiography technique is used, which is a noninvasive diagnostic method.

Patients and Methods: The study included 216 patients between 25 and 75 years old; 121 males and 95 females; 265 normal individuals (age range: 25 to 75 years ol

In this paper an eco-epidemiological system has been proposed and studied analytically as well as numerically. The boundedness, existence and uniqueness of the solution are discussed. The local and global stability of all possible equilibrium point are investigated. The global dynamics is studied numerically. It is obtained that system has rich in dynamics including Hopf bifurcation.

Vaccination against novel Coronavirus (SARS-CoV-2) become highly recommended. In Iraq, three vaccines are available. They are Pfizer-Biontech, Oxford-AstraZenica, and Sino harm vaccines. A cross-sectional retrospective study was performed to a total of 2399 individual who are vaccinated with one of the available vaccines. People who are infected with Covid-19 before and/or after vaccination of either studied SARS-CoV-2 vaccines were also involved in this study (1175 case). Signs and symptoms have been reported for each of confirmed positive cases of Coronavirus disease. Statistical data analyses were applied to reveal the effect of different SARS-CoV-2 vaccines on the incidence of novel coronavirus disease among Iraqi population. Also, the

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

Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that