RESUMEN
Reactive oxygen species (ROS) are chemically reactive oxygen containing molecules. ROS consist of radical oxygen species including superoxide anion (O2 â¢-) and hydroxyl radical (â¢OH) and non-radical oxygen species such as hydrogen peroxide (H2O2), singlet oxygen (O2). ROS are generated by mitochondrial oxidative phosphorylation, environmental stresses including UV or heat exposure, and cellular responses to xenobiotics ( Ray et al., 2012 ). Excessive ROS production over cellular antioxidant capacity induces oxidative stress which results in harmful effects such as cell and tissue damage. Sufficient evidence suggests that oxidative stresses are involved in cancers, cardiovascular disease, and neurodegenerative diseases including Alzheimer's disease and Parkinson disease (Waris and Ahsan, 2006). Though excessive level of ROS triggers detrimental effects, ROS also have been implicated to regulate cellular processes. Since ROS function is context dependent, measurement of ROS level is important to understand cellular processes (Finkel, 2011). This protocol describes how to detect intracellular and mitochondrial ROS in live cells using popular chemical fluorescent dyes.
RESUMEN
We illustrate the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature. An emerging field of research can be identified as part of an existing corpus, permitting the implementation of a more coherent community structure for its practitioners.