image: Systems Medicine: Journal of Medical Systems Biology and Network Medicine focuses on interdisciplinary approaches to exploiting the power of big data by applying systems biology and network medicine. view more
Credit: Mary Ann Liebert, Inc., publishers
New Rochelle, NY, August 5, 2019--A new study supports the use of virtual patients and in silico clinical studies to evaluate the effectiveness of blue light to reduce the symptoms of psoriasis. Researchers also demonstrated that this in silico approach can be used to improve the treatment response of patients with psoriasis to blue light by modifying the settings of the therapeutic protocol, as reported in the study published in Systems Medicine, an open access journal from Mary Ann Liebert, Inc., publishers. Click here to read the full article free on the Systems Medicine: Journal of Medical Systems Biology and Network Medicine website through September 5, 2019.
"In silico Clinical Studies on the Efficacy of Blue Light for Treating Psoriasis in Virtual Patients" was coauthored by Zandra Félix Garza, Peter Hilbers, and Natal van Riel, Eindhoven University of Technology, The Netherlands, and Joerg Liebmann and Matthias Born, Philips Electronics Netherlands BV, Eindhoven. The researchers note that the current computational model for studying the efficacy of blue light therapy only reproduces the response in the average patient in clinical trials and does not take into account individual variations amongst patients. Use of a computational model combined with a refined pool of virtual patients can adequately capture the patient variability in the response to treatment with blue light and the decrease in disease severity seen in previous clinical investigations. The authors suggest that a minimum of 2,500 virtual patients, which they refined down from an initial pool of 500,000 virtual patients, are needed to reproduce the responses seen in clinical investigations.
"This is a highly promising approach towards using statistical learning on virtual patient populations to draw actionable clinical conclusions on real patients, and thus a major step forward to precision medicine," says Co-Editor-in-Chief Prof. Dr. Jan Baumbach from Technical University of Munich.
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About the Journal
Systems Medicine: Journal of Medical Systems Biology and Network Medicine is the premier open access, peer-reviewed journal focused on interdisciplinary approaches to exploiting the power of big data by applying systems biology and network medicine. Led by Co-Editors-in-Chief H.H.H.W. Schmidt, MD, PhD, PharmD, Maastricht University, The Netherlands and Jan Baumbach, PhD, Technical University of Munich, Germany, Systems Medicine yields major breakthroughs towards mechanism-based re-definitions of diseases for high-precision diagnostics and treatments. The Journal is collaborative partners with the European Cooperation in Science and Technology (COST), Italian Association for Systems Medicine and Healthcare (ASSIMSS), and European Association for Systems Medicine (EASYM). Complete tables of content can be viewed on the Systems Medicine website.
About the Publisher
Mary Ann Liebert, Inc., publishers is a privately held, fully integrated media company known for establishing authoritative peer-reviewed journals in many promising areas of science and biomedical research, including Assay and Drug Development Technologies, Big Data, and OMICS: A Journal of Integrative Biology. Its biotechnology trade magazine, GEN (Genetic Engineering & Biotechnology News, was the first in its field and is today the industry's most widely read publication worldwide. A complete list of the firm's 80 journals, books, and newsmagazines is available on the Mary Ann Liebert, Inc., publishers website.
Journal
Systems Medicine