scholarly journals Corrigendum to “Suppression of Dynamical Network Biomarker Signals at the Predisease State (Mibyou) before Metabolic Syndrome in Mice by a Traditional Japanese Medicine (Kampo Formula) Bofutsushosan”

2021 ◽  
Vol 2021 ◽  
pp. 1-1
Author(s):  
Keiichi Koizumi ◽  
Makito Oku ◽  
Shusaku Hayashi ◽  
Akiko Inujima ◽  
Naotoshi Shibahara ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Dynamical Network ◽  
Network Biomarker ◽  
Traditional Japanese Medicine ◽  
Dynamical Network Biomarker

scholarly journals Suppression of Dynamical Network Biomarker Signals at the Predisease State (Mibyou) before Metabolic Syndrome in Mice by a Traditional Japanese Medicine (Kampo Formula) Bofutsushosan

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Keiichi Koizumi ◽  
Makito Oku ◽  
Shusaku Hayashi ◽  
Akiko Inujima ◽  
Naotoshi Shibahara ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Treated Group ◽  
The Body ◽  
Control Group ◽  
Scientific Evaluation ◽  
Dynamical Network ◽  
Network Biomarker ◽  
Traditional Japanese Medicine ◽  
First Time ◽  
Dynamical Network Biomarker

Due to the increasing incidence of metabolic syndrome, the development of new therapeutic strategies is urgently required. One promising approach is to focus on the predisease state (so-called Mibyou in traditional Japanese medicine) before metabolic syndrome as a preemptive medical target. We recently succeeded in detecting a predisease state before metabolic syndrome using a mathematical theory called the dynamical network biomarker (DNB) theory. The detected predisease state was characterized by 147 DNB genes among a total of 24,217 genes in TSOD (Tsumura-Suzuki Obese Diabetes) mice, a well-accepted model of metabolic syndrome, at 5 weeks of age. The timing of the predisease state was much earlier than the onset of metabolic syndrome in TSOD mice reported to be at approximately 8–12 weeks of age. In the present study, we investigated whether the predisease state in TSOD mice can be inhibited by the oral administration of a Kampo formula, bofutsushosan (BTS), which is usually used to treat obese patients with metabolic syndrome in Japan, from 3 to 7 weeks of age. We found the comprehensive suppression of the early warning signals of the DNB genes by BTS at 5 weeks of age and later. Specifically, the standard deviations of 134 genes among the 147 DNB genes decreased at 5 weeks of age as compared to the nontreatment control group, and 80 of them showed more than 50% reduction. In addition, at 7 weeks of age, the body weight and blood glucose level were significantly lower in the BTS-treated group than in the nontreatment control group. The results of our study suggest a novel mechanism of BTS; it suppressed fluctuations of the DNB genes at the predisease state before metabolic syndrome and thus prevented the subsequent transition to metabolic syndrome. In conclusion, this study demonstrated the preventive and preemptive effects of a Kampo formula on Mibyou before metabolic syndrome for the first time based on scientific evaluation.

scholarly journals Numerical Study on Zika Epidemic Early Warning Algorithms Driven by Dynamical Network Biomarker

2019 ◽  
Vol 2019 ◽  
pp. 1-5 ◽  
Author(s):  
Qingyi Zhan ◽  
Zhifang Zhang
Keyword(s):  
Numerical Calculation ◽  
Numerical Experiment ◽  
Early Warning ◽  
Numerical Study ◽  
Dynamical Network ◽  
Network Biomarker ◽  
Dynamical Network Biomarker

This paper focuses on the numerical study and precise forecast of Zika epidemic. An early warning index algorithm of Zika epidemic is given, and the applications of this algorithm are investigated. We pay attention to the mathematical approaches to estimate the tendency of Zika epidemic in detail. A numerical experiment is provided to illustrate the effectiveness of the proposed method by the numerical calculation of early warning index of Brazil.

scholarly journals Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Keiichi Koizumi ◽  
Makito Oku ◽  
Shusaku Hayashi ◽  
Akiko Inujima ◽  
Naotoshi Shibahara ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Dynamical Network ◽  
Network Biomarkers
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