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Comparison of survival models to study determinants liver cancer

Cancer is one of the dangerous diseases that afflict a person through injury to cells and tissues in the body, where a person is vulnerable to infection in any age group, and it is not easy to control and multiply between cells and spread to the body. In spite of the great progress in medical studies interested in this aspect, the options for those with this disease are few and difficult, as they require significant financial costs for health services and for treatment that is difficult to provide.

This study dealt with the determinants of liver cancer by relying on the data of cancerous tumours taken from the Iraqi Center for Oncology in the Ministry of Health 2017. Survival analysis has been used as a method for analyzing the study data, because it applies to the phenomenon of interest, by comparing the parameter and semi-parametric models using a standardized (BIC, AIC) to determine the most appropriate model for data analysis, and the study concluded that the Cox parametric model with the lowest values for the evaluation criteria is the most appropriate for the analysis of the study data and from it, the most important variables with a significant effect on liver cancer were identified as Age and method of diagnosis and accurate diagnosis of the disease were significant variables affecting liver cancer. The study also found the significance of the relative risk for each of the variables (age, method of diagnosis, disease location, accurate diagnosis, the extent of prevalence) i.e. increased risk of liver cancer when these variables increase, each according to its specificity

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Publication Date
Mon Nov 02 2020
Journal Name
International Journal Of Pharmaceutical Research
Evaluation of Serum Adropin Levels in Nonalcoholic Fatty Liver Disease as A Complication of Hypothyroidism In Iraqi Patients

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Publication Date
Sat May 15 2004
Journal Name
Journal Of Biotechnology Research Center
The Morphological and Histopathological Liver Abnormalities Caused by Carbamazepine-Induced Injury in Female Albino Mice

Background: The adverse effects of drugs can damage various organs, especially the liver, leading to a hepatic injury known as hepatotoxicity. Drug-induced liver injury (DILI) is challenging nowadays because of the large number of different drugs used, one of the offending medications that cause DILI is carbamazepine (CBZ), since the liver has an array of functions including detoxification, it will deal with several damages caused by exposure to the drugs. Objective: investigate the effect of (CBZ) 20mg/kg/day on female mice liver after 14 and 30 days of treatment on morphological and histopathological levels. Materials and Methods: 20mg/kg/day of CBZ was administered orally for (14) days to (10) female mice, another (10) mice were taking t

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Publication Date
Wed Jul 01 2020
Journal Name
Biochemical And Cellular Archives
Histopathological changes in liver induced by piroxicam administration in adult male albino mice mus musculus

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Publication Date
Fri Jul 01 2011
Journal Name
Saudi Pharmaceutical Journal
Free radical scavenging activity of silibinin in nitrite-induced hemoglobin oxidation and membrane fragility models

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Publication Date
Thu Jan 03 2019
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and Artificial Neural Network

Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the

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Publication Date
Thu Dec 31 2020
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Measurement and accounting disclosure of intellectual capital using accounting models in the Iraqi insurance company

The research aims to shed light on the possibility of measuring the intellectual capital in the Iraqi insurance company using accounting models, as well as disclosing it in the financial statements of the company, where human capital was measured using the present value factor model for discounted future revenues and the intellectual value-added factor model for measuring structural capital It was also disclosed in the financial statements based on the theory of stakeholders. The research problem lies in the fact that the Iraqi insurance company does not carry out the process of measuring and disclosing the intellectual capital while it is considered an important source for the company’s progress in the labor market recently. T

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Publication Date
Tue May 01 2018
Journal Name
Journal Of Engineering
Prediction of Municipal Solid Waste Generation Models Using Artificial Neural Network in Baghdad city, Iraq

The importance of Baghdad city as the capital of Iraq and the center of the attention of delegations because of its long history is essential to preserve its environment. This is achieved through the integrated management of municipal solid waste since this is only possible by knowing the quantities produced by the population on a daily basis. This study focused to predicate the amount of municipal solid waste generated in Karkh and Rusafa separately, in addition to the quantity produced in Baghdad, using IBM SPSS 23 software. Results that showed the average generation rates of domestic solid waste in Rusafa side was higher than that of Al-Karkh side because Rusafa side has higher population density than Al-Karkh side. T

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Publication Date
Tue Jun 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
Performance Evaluation of Al-Karkh Water Treatment Plant Using Model-driven and Data-Driven Models
Abstract<p>There is a great operational risk to control the day-to-day management in water treatment plants, so water companies are looking for solutions to predict how the treatment processes may be improved due to the increased pressure to remain competitive. This study focused on the mathematical modeling of water treatment processes with the primary motivation to provide tools that can be used to predict the performance of the treatment to enable better control of uncertainty and risk. This research included choosing the most important variables affecting quality standards using the correlation test. According to this test, it was found that the important parameters of raw water: Total Hardn</p> ... Show More
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Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Governance And Regulation
An empirical study to detect agency problems in listed corporations: The emerging market study

The aim of this paper is to shed the light on the concepts of agency theory by measuring one of the problems that arise from it, which is represented by earnings management (EM) practices. The research problem is demonstrated by the failure of some Iraqi banks and their subsequent placement under the supervision of the Central Bank of Iraq, which was attributed, in part, to the inadequacy of the agency model in protecting stakeholders in shareholding institutions, as well as EM, pushed professional institutions to adopt the corporate governance model as a method to regulate the problem of accounting information asymmetry between the parties to the agency. We are using the Beneish M-score model and the financial analysis equations in

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Publication Date
Thu Mar 31 2022
Journal Name
Iraqi Geological Journal
Development of Artificial Intelligence Models for Estimating Rate of Penetration in East Baghdad Field, Middle Iraq

It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i

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