IKKγ Facilitates the Activation of NF-κB by Hepatitis C Virus Core Protein

Hepatitis C virus (HCV) is a major cause of chronic hepatitis, liver cirrhosis and hepatocellular carcinoma. HCV core protein has been shown to modulate various cellular signaling pathways including the nuclear factor κB (NF-κB) pathway which is associated with inflammation, cell proliferation and apoptosis. However, there have been conflicting reports about the effect of HCV core protein on NF-κB pathway, and the mechanism by which the core protein affects NF-κB activity remains nuclear. In this study, the functional interaction of HCV core protein and IκB kinase γ (IKKγ) was investigated using the expression plasmids of core and the components of IKK complex. The data revealed that HCV core protein activates NF-κB. Also, HCV core protein up-regulated the phosphorylation and degradation of IκBα. The activating effect of HCV core protein on NF-κB was synergistically elevated by IKKγ. It was noticed that the N-terminal IKKβ binding site, C-terminal leucine zipper, and zinc finger domains of IKKγ are not necessary for its synergistic effect. HCV core protein and IKKγ appeared to activate NF-κB by up-regulating the IKKβ activity resulting in the degradation of IκBα. As expected, HCV core protein induced the expression of NF-κB-targeted pro-inflammatory genes such as iNOS, IL-1β and IL-6 in the transcription level. These results suggest that HCV core protein induces NF-κB through the interaction with IKKγ and may play a critical role in the development of inflammation and related liver diseases.

Prediction Model for Health-Related Quality of Life of Elderly with Chronic Diseases using Machine Learning Techniques / 대한의료정보학회지

OBJECTIVES: The purposes of this study were to identify the factors that affect the health-related quality of life (HRQoL) of the elderly with chronic diseases and to subsequently develop from such factors a prediction model to help identify HRQoL risk groups that require intervention. METHODS: We analyzed a set of secondary data regarding 716 individuals extracted from the Korea National Health and Nutrition Examination Survey from 2008 to 2010. The statistical package of SPSS and MATLAB were used for data analysis and development of the prediction model. The algorithms used in the study were the following: stepwise logistic regression (SLR) analysis and machine learning (ML) techniques, such as decision tree, random forest, and support vector machine methods. RESULTS: Five factors with statistical significance were identified for HRQoL in the elderly with chronic diseases: 'monthly income', 'diagnosis of chronic disease', 'depression', 'discomfort', and 'perceived health status.' The SLR analysis showed the best performance with accuracy = 0.93 and F-score = 0.49. The results of this study provide essential materials that will help formulate personalized health management strategies and develop interventions programs towards the improvement of the HRQoL for elderly people with chronic diseases. CONCLUSIONS: Our study is, to our best knowledge, the first attempt to identify the influencing factors and to apply prediction models for the HRQoL of the elderly with chronic diseases by using ML techniques as an alternative and complement to the traditional statistical approaches.

Predictors of Medication Adherence in Elderly Patients with Chronic Diseases Using Support Vector Machine Models / 대한의료정보학회지

OBJECTIVES: The aim of this study was to establish a prediction model of medication adherence in elderly patients with chronic diseases and to identify variables showing the highest classification accuracy of medication adherence in elderly patients with chronic diseases using support vector machine (SVM) and conventional statistical methods, such as logistic regression (LR). METHODS: We included 293 chronic disease patients older than 65 years treated at one tertiary hospital. For the medication adherence, Morisky's self-report was used. Data were collected through face-to-face interviews. The mean age of the patients was 73.8 years. The classification process was performed with LR (SPSS ver. 20.0) and SVM (MATLAB ver. 7.12) method. RESULTS: Taking into account 16 variables as predictors, the result of applying LR and SVM classification accuracy was 71.1% and 97.3%, respectively. We listed the top nine variables selected by SVM, and the accuracy using a single variable, self-efficacy, was 72.4%. The results suggest that self-efficacy is a key factor to medication adherence among a Korean elderly population both in LR and SVM. CONCLUSIONS: Medication non-adherence was strongly associated with self-efficacy. Also, modifiable factors such as depression, health literacy, and medication knowledge associated with medication non-adherence were identified. Since SVM builds an optimal classifier to minimize empirical classification errors in discriminating between patient samples, it could achieve a higher accuracy with the smaller number of variables than the number of variables used in LR. Further applications of our approach in areas of complex diseases, treatment will provide uncharted potentials to researchers in the domains.