Nonalcoholic fatty liver disease (NAFLD) is a common liver disease that ranges from simple steatosis to nonalcoholic steatohepatitis (NASH). So far, the underlying mechanism remains poorly understood. Here, we show that hepatic carboxylesterase 2 (CES2) is markedly reduced in NASH patients, diabetic db/db mice, and high‐fat diet (HFD)‐fed mice. Restoration of hepatic CES2 expression in db/db or HFD‐fed mice markedly ameliorates liver steatosis and insulin resistance. In contrast, knockdown of hepatic CES2 causes liver steatosis and damage in chow‐ or Western diet‐fe

Abstract Background: The human epidermal growth factor receptor 2(HER2) proto-oncogene is overexpressed or amplified in approximately 15%-25% of invasive breast cancers. Approximately 35% of HER2-amplified breast cancers have coamplification of the topoisomerase II-alpha (TOP2A) gene encoding an enzyme that is a major target of anthracyclines. Hence, the determination of genetic alteration (amplification or deletion) of both genes is considered as an important predictive factor that determines the response of breast cancer patients to treatment. The aims of this study are to determinate TOP2A status gene amplification in a set of Iraqi patients with breast cancer that have had an equivocal (2+) and positive HER2/neu by immunohistochemistry

To determine the potential of gingival crevicular fluid (GCF) volume, E‐cadherin and total antioxidant capacity (TAC) levels to predict the outcomes of nonsurgical periodontal therapy (NSPT) for periodontitis patients.

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

Today, the role of cloud computing in our day-to-day lives is very prominent. The cloud computing paradigm makes it possible to provide demand-based resources. Cloud computing has changed the way that organizations manage resources due to their robustness, low cost, and pervasive nature. Data security is usually realized using different methods such as encryption. However, the privacy of data is another important challenge that should be considered when transporting, storing, and analyzing data in the public cloud. In this paper, a new method is proposed to track malicious users who use their private key to decrypt data in a system, share it with others and cause system information leakage. Security policies are also considered to be int

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

Cloud-based Electronic Health Records (EHRs) have seen a substantial increase in usage in recent years, especially for remote patient monitoring. Researchers are interested in investigating the use of Healthcare 4.0 in smart cities. This involves using Internet of Things (IoT) devices and cloud computing to remotely access medical processes. Healthcare 4.0 focuses on the systematic gathering, merging, transmission, sharing, and retention of medical information at regular intervals. Protecting the confidential and private information of patients presents several challenges in terms of thwarting illegal intrusion by hackers. Therefore, it is essential to prioritize the protection of patient medical data that is stored, accessed, and shared on

Most of the medical datasets suffer from missing data, due to the expense of some tests or human faults while recording these tests. This issue affects the performance of the machine learning models because the values of some features will be missing. Therefore, there is a need for a specific type of methods for imputing these missing data. In this research, the salp swarm algorithm (SSA) is used for generating and imputing the missing values in the pain in my ass (also known Pima) Indian diabetes disease (PIDD) dataset, the proposed algorithm is called (ISSA). The obtained results showed that the classification performance of three different classifiers which are support vector machine (SVM), K-nearest neighbour (KNN), and Naïve B

Purpose – The Cloud computing (CC) and its services have enabled the information centers of organizations to adapt their informatic and technological infrastructure and making it more appropriate to develop flexible information systems in the light of responding to the informational and knowledge needs of their users. In this context, cloud-data governance has become more complex and dynamic, requiring an in-depth understanding of the data management strategy at these centers in terms of: organizational structure and regulations, people, technology, process, roles and responsibilities. Therefore, our paper discusses these dimensions as challenges that facing information centers in according to their data governance and the impa

Correlation equations for expressing the boiling temperature as direct function of liquid composition have been tested successfully and applied for predicting azeotropic behavior of multicomponent mixtures and the kind of azeotrope (minimum, maximum and saddle type) using modified correlation of Gibbs-Konovalov theorem. Also, the binary and ternary azeotropic point have been detected experimentally using graphical determination on the basis of experimental binary and ternary vapor-liquid equilibrium data.

            In this study, isobaric vapor-liquid equilibrium for two ternary systems: “1-Propanol – Hexane – Benzene” and its binaries “1-Propanol –