Lutein Protects against Methotrexate-Induced and Reactive Oxygen Species-Mediated Apoptotic Cell Injury of IEC-6 Cells

Purpose High-dose chemotherapy using methotrexate (MTX) frequently induces side effects such as mucositis that leads to intestinal damage and diarrhea. Several natural compounds have been demonstrated of their effectiveness in protecting intestinal epithelial cells from these adverse effects. In this paper, we investigated the protection mechanism of lutein against MTX-induced damage in IEC-6 cells originating from the rat jejunum crypt. Methods: The cell viability, induced-apoptosis, reactive oxygen species (ROS) generation, and mitochondrial membrane potential in IEC-6 cells under MTX treatment were examined in the presence or absence of lutein. Expression level of Bcl2, Bad and ROS scavenging enzymes (including SOD, catalase and Prdx1) were detected by quantitative RT-PCR. Results: The cell viability of IEC-6 cells exposed to MTX was decreased in a dose- and time-dependent manner. MTX induces mitochondrial membrane potential loss, ROS generation and caspase 3 activation in IEC-6 cells. The cytotoxicity of MTX was reduced in IEC-6 cells by the 24 h pre-treatment of lutein. We found that pre-treatment of lutein significantly reduces MTX-induced ROS and apoptosis. The expression of SOD was up-regulated by the pre-treatment of lutein in the MTX-treated IEC-6 cells. These results indicated that lutein can protect IEC-6 cells from the chemo-drugs induced damage through increasing ROS scavenging ability. Conclusion: The MTX-induced apoptosis of IEC-6 cells was shown to be repressed by the pre-treatment of lutein, which may represent a promising adjunct to conventional chemotherapy for preventing intestinal damages

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie