Analysis of R&D Trend for the Treatment of Autoimmune Diseases by Scientometric Method

Introduction Autoimmune diseases (AD), referred to as abnormal immune responses of body against selfantigen, are caused by the loss of immunologic selftolerance resulting in damage to the cells, tissues and organs. The National Institute of Health (NIH) lists more than 80 autoimmune diseases that affect varied organs of the body including rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus and so on. Significant advances of AD have been made in the understanding of clinical and pathological mechanisms involved but, to date, a few elements have been identified as being responsible for the autoimmune process. With a better understanding of the causes and treatments of AD, many potential novel therapies have recently been developed and evaluated, focusing on cellular or molecular targets. Although there have been several research activities carried out with scientometric tools to evaluate scientific output for individual autoimmune diseases such as rheumatoid arthritis, Crohn's and Behchet's disease (Shahram et al., 2013), there was no scientometric studies on the entire autoimmune disease to date. Density-equalizing algorithms, scientometric methods and large scale data analysis were applied to evaluate quality and quantity of scientific researches in rheumatoid arthritis (Schöffel et al., 2010). Various scientometric analysis including literature-related discovery (LRD), text-mining was more broadly performed to produce knowledge discovery such as gene expression and proteomic studies. Data mining and bioinformatics approaches for autoimmune biomarker discovery studies were also attempted (Kostoff, 2014). The purpose of this study is to analyze the status and trends of treatments for AD using scientometric methods, and intend to give researchers and policymakers valuable information in the field of AD.