Fu Jen Catholic University

About: Fu Jen Catholic University is a education organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Population & Hazard ratio. The organization has 6842 authors who have published 9512 publications receiving 171005 citations. The organization is also known as: FJU & Fu Jen.

The interplay of reactive oxygen species and the epidermal growth factor receptor in tumor progression and drug resistance.

Abstract: The epidermal growth factor receptor (EGFR) plays important roles in cell survival, growth, differentiation, and tumorigenesis. Dysregulation of the EGFR is a common mechanism in cancer progression especially in non-small cell lung cancer (NSCLC). Suppression of the EGFR-mediated signaling pathway is used in cancer treatment. Furthermore, reactive oxygen species (ROS)-induced oxidative stress from mitochondrial dysfunction or NADPH oxidase (NOX) overactivation and ectopic expression of antioxidative enzymes were also indicated to be involved in EGFR-mediated tumor progression (proliferation, differentiation, migration, and invasion) and drug resistance (EGFR tyrosine kinase inhibitor (TKI)). The products of NOX, superoxide and hydrogen peroxide, are considered to be major types of ROS. ROS are not only toxic materials to cells but also signaling regulators of tumor progression. Oxidation of both the EGFR and downstream phosphatases by ROS enhances EGFR-mediated signaling and promotes tumor progression. This review primarily focuses on the recent literature with respect to the roles of the EGFR and ROS and correlations between ROS and the EGFR in tumor progression and EGFR TKI resistance. The evidence discussed in this article can serve as a basis for basic and clinical research to understand how to modulate ROS levels to control the development and drug resistance of cancers.

Validating the Chinese version of the Verbal Learning Test for screening Alzheimer's disease.

Abstract: Episodic memory tasks are one of the most sensitive tools to discriminate Alzheimer's disease (AD). This study aimed to validate a shorter version verbal memory test that will efficiently assess Chinese elderly with memory complaints. One hundred and eighty-five elderly with normal cognition (NC) and 217 AD patients were evaluated. Each participant received the Chinese Version Verbal Learning Test (CVVLT) consisting of 9 two-character nouns with 4 learning trials, 2 delayed recalls in 30 seconds and 10 minutes, and a word recognition test. In the NC elderly, age and sex had significant effects on recall scores in CVVLT, while education level showed an inverse correlation with 3 different patterns of errors made during the learning, recall, and recognition trials. AD patients had lower scores across all recall tests. In those with lower educational level, NC elderly had higher perseveration errors than AD patients. The cutoff value between the AD and NC groups in the 10-minute recall was 4/5 for those aged >75 years and 5/6 for those aged <75 years. This study has good validity in discriminating AD participants and the data here can help in diagnosing AD and mild cognitive impairment using the CVVLT.

Measurement of the branching fraction B(Λc+ → pK-π+).

Abstract: We present the first model-independent measurement of the absolute branching fraction of the Lambda(+)(c) -> pK(-)pi(+)decay using a data sample of 978 fb(-1) collected with the Belle detector at the KEKB asymmetric-energy e(+)e(-) collider. The number of Lambda(+)(c) baryons is determined by reconstructing the recoiling D(*)(-)(p) over bar pi(+)system in events of the type e(+)e(-) -> D(*)(-)(p) over bar pi(+)Lambda(+)(c). The branching fraction is measured to be B(Lambda(+)(c) -> pK(-)pi(+)) = (6.84 +/- 0.24(-0.27)(+0.21))%, where the first and second uncertainties are statistical and systematic, respectively.

Hybrid intelligent modeling schemes for heart disease classification

Abstract: Heart disease is the leading cause of death among both men and women in most countries in the world. Thus, people must be mindful of heart disease risk factors. Although genetics play a role, certain lifestyle factors are crucial contributors to heart disease. Traditional approaches use thirteen risk factors or explanatory variables to classify heart disease. Diverging from existing approaches, the present study proposes a new hybrid intelligent modeling scheme to obtain different sets of explanatory variables, and the proposed hybrid models effectively classify heart disease. The proposed hybrid models consist of logistic regression (LR), multivariate adaptive regression splines (MARS), artificial neural network (ANN), and rough set (RS) techniques. The initial stage of the proposed process includes the use of LR, MARS, and RS techniques to reduce the set of explanatory variables. The remaining variables are subsequently used as inputs for the ANN method employed in the second stage. A real heart disease data set was used to demonstrate the development of the proposed hybrid models. The modeling results revealed that the proposed hybrid schemes effectively classify heart disease and outperform the typical, single-stage ANN method.