The ginsenoside Rg1 prevents transverse aortic constriction-induced left ventricular hypertrophy and cardiac dysfunction by inhibiting fibrosis and enhancing angiogenesis.

BACKGROUND: Ginsenoside Rg1, an important and active ingredient of Panax ginseng, has been shown to exert cardioprotective effects in vivo. The present study aimed to test the hypothesis that ginsenoside Rg1 attenuates cardiac dysfunction in a transverse aortic constriction (TAC)-induced left ventricular hypertrophy in vivo via proangiogenic and antifibrotic effects. METHODS: This study investigated the effects of ginsenoside Rg1 in a rat model of TAC-induced left ventricular hypertrophy. Cardiac function was assessed by echocardiography. The antifibrotic and proangiogenic effects were assessed by histopathology and mRNA expression of procollagen I, III, and vascular endothelial growth factor (VEGF) through quantitative real-time PCR. The expression of phosphorylation of Akt, p38 mitogen-activated protein kinase (MAPK), hypoxia inducible factor-1 (HIF-1), and VEGF proteins were examined by Western blotting. RESULTS: Ginsenoside Rg1 treatment significantly decreased TAC-induced myocardial fibrosis and left ventricular hypertrophy, and preserved cardiac function. Ginsenoside Rg1 administration enhanced angiogenesis by increasing the expression of HIF-1 and VEGF. These cardioprotective effects of ginsenoside Rg1 are partially related to the activation of phospho-Akt and inhibition of p38 MAPK. CONCLUSIONS: Ginsenoside Rg1 exhibited protective effect against TAC-induced left ventricular hypertrophy and cardiac dysfunction, which is potentially associated with phospho-Akt activation and p38 MAPK inhibition.

Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

BACKGROUND: A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. OBJECTIVE: The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. METHODS: The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. RESULTS: The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. CONCLUSIONS: This SOA Web service-based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

Machine intelligence for health information: capturing concepts and trends in social media via query expansion.

INTRODUCTION: We aim to improve retrieval of health information from Twitter. BACKGROUND: The popularity of social media and micro-blogs has emphasised their potential for knowledge discovery and trend building. However, capturing and relating concepts in these short-spoken and lexically extensive sources of information requires search engines with increasing intelligence. METHODS: Our approach uses query expansion techniques to associate query terms with the most similar Twitter terms to capture trends in the gamut of information. RESULTS: We demonstrated the value, defined as improved precision, of our search engine by considering three search tasks and two independent annotators. We also showed the stability of the engine with an increasing number of tweets; this is crucial as large data sets are needed for capturing trends with high confidence. These results encourage us to continue developing the engine for discovering trends in health information available at Twitter.

Tablet PC-enabled application intervention for patients with gastric cancer undergoing gastrectomy.

BACKGROUND: Gastric cancer is among the most common gastrointestinal cancers worldwide. Patients who have undergone surgery for gastric cancer may suffer from malnutrition and potential consequences such as gastrointestinal complications, surgical stress, and cancer cachexia. A tablet PC-based intervention via a mobile application might enhance the early recovery of postgastrectomy patients. OBJECTIVE: The aim of this study was to develop and test a tablet personal computer (PC)-assisted intervention to hasten the recovery of postgastrectomy cancer patients with respect to nutritional status. METHODS: This single-arm pilot study investigated a tablet PC application developed to serve the functions of nutritional monitoring, medical information management, drainage follow-up, and wound care. All services were delivered by medical professionals. RESULTS: Twenty consecutive gastrectomy patients at the National Taiwan University Hospital received perioperative care via this application (App group). During the study period, we retrospectively collected an additional 20 demographically matched gastrectomy cases as a control group. The App group had a lower body weight loss percentage relative to the control group during a 6-month follow-up period (4.8±1.2% vs. 8.7±2.4%; p<0.01). However, the patients in the App group had more outpatient clinic (OPC) visits than did those in the control group (9.8±0.9 vs. 5.6±0.8; p<0.01). CONCLUSIONS: This study supported the feasibility of a tablet PC-based application for the perioperative care of gastric cancer subjects to promote a lower body weight loss and the collection of comprehensive surgical records.