Background: Although expression of the HER-
2/neuoncogene may be of some prognostic importance
in advanced ovarian cancer, its role in early-stage
disease has not been established. The current study
examined the prevalence and significance of HER-
2/neu expression in different grades of different types
of surface epithelial ovarian carcinoma.
Methods: Thirty eight female patients with surface
epithelial ovarian cancer were included in this study.
The blocks of corresponding formalin fixed, paraffinembedded
ovarian biopsies were retrieved from the
archives and hematoxylin-eosin slides of each ovarian
biopsy were reviewed and marked their grades of
differentiation , then a new sections from each sampl

Background: There is a significant molecular response to imatinib given at standard doses in individuals with chronic myeloid leukemia (CML) whose ABCB1 polymorphisms are present. Objective: To investigate the impact of the polymorphism in the ABCB1 gene rs1128503 on the effectiveness of nilotinib or imatinib therapy. Methods: From May 2022 until the end of January 2023, the current study was carried out in a single research institution, the National Center of Hematology, Baghdad Teaching Hospital at Medical City, Iraq. 76 people with chronic phase myeloid leukemia (CML-CP), who had previously received a diagnosis using the European Leukemia Net (ELN) criteria, enrolled in the trial. The PCR product was delivered to Macrogen Corpora

In this paper the queuing system (M/Er/1/N) has been considered in equilibrium. The method of stages introduced by Erlang has been used. The system of equations which governs the equilibrium probabilities of various stages has been given. For general N the probability of j stages of service are left in the system, has been introduced. And the probability for the empty system has been calculated in the explicit form.

Rationing is a commonly used solution for shortages of resources and goods that are vital for the citizens of a country. This paper identifies some common approaches and policies used in rationing as well asrisks that associated to suggesta system for rationing fuelwhichcan work efficiently. Subsequently, addressing all possible security risks and their solutions. The system should theoretically be applicable in emergency situations, requiring less than three months to implement at a low cost and minimal changes to infrastructure.

 Active worms have posed a major security threat to the Internet, and many research efforts have focused on them. This paper is interested in internet worm that spreads via TCP, which accounts for the majority of internet traffic. It presents an approach that use a hybrid solution between two detection algorithms: behavior base detection and signature base detection to have the features of each of them. The aim of this study is to have a good solution of detecting worm and stealthy worm with the feature of the speed. This proposal was designed in distributed collaborative scheme based on the small-world network model to effectively improve the system performance.

The organization and coordination of any communication is based on the system of turn-taking which refers to the process by which a participant in a conversation takes the role of speaker. The progression of any conversation is achieved by the change of roles between speaker and hearer which, in its turn, represents the heart of the turn-taking system. The turn-taking system is not a random process but it is a highly organized process governed by a set of rules. Thus, this system has certain features and rules which exist in any English communicative process. These rules, if applied by speakers, help to achieve successful exchange of turns in any conversation. This paper attempts to present full exposition of the concepts of conversation

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