Soy Isoflavones in Foods: Database Development

1998 ◽  
pp. 138-149 ◽  
Author(s):  
Patricia A. Murphy ◽  
Kobita Barua ◽  
Tongtong Song
Keyword(s):  
Soy Isoflavones ◽  
Database Development

Protective effects of soy-isoflavones in cardiovascular disease

2008 ◽  
Vol 28 (01/02) ◽  
pp. 85-88 ◽  
Author(s):  
D. Fuchs ◽  
H. Daniel ◽  
U. Wenzel
Keyword(s):  
Cardiovascular Disease ◽  
Endothelial Cells ◽  
Molecular Mechanisms ◽  
Dietary Intervention ◽  
Mononuclear Cells ◽  
Oxidized Ldl ◽  
Proteome Analysis ◽  
Soy Isoflavones ◽  
Protective Effects ◽  
Estrogen Production

SummaryEpidemiological studies indicate that the consumption of soy-containing food may prevent or slow-down the development of cardiovascular disease. In endothelial cells application of a soy extract or a combination of the most abundant soy isoflavones genistein and daidzein both inhibited apoptosis, a driving force in atherosclerosis development, when applied in combination with oxidized LDL or homocysteine. Proteome analysis revealed that the stressorinduced alteration of protein expression profile was reversed by the soy extract or the genistein/daidzein mixture. Only few protein entities that could be functionally linked to mitochondrial dysfunction were regulated in common by both application forms of isoflavones. A dietary intervention with isoflavone-enriched soy extract in postmenopausal women, who generally show strongly increased cardiovascular risk due to diminished estrogen production, led to significant alterations in the steady state levels of proteins from mononuclear blood cells. The proteins identified by proteome analysis revealed that soy isoflavones may increase the anti-inflammatory response in blood mononuclear cells thereby contributing to the atherosclerosispreventive activities of a soy-rich diet. Conclusion: By proteome analysis protein targets were identified in vitro in endothelial cells that respond to soy isoflavones and that may decipher molecular mechanisms through which soy products exert their protective effects in the vasculature.

Withdrawal: Ascent Aerodynamic Force and Moment Database Development for the Space Launch System

2019 ◽  
Author(s):  
Patrick R. Shea ◽  
Jeremy T. Pinier ◽  
Heather P. Houlden ◽  
Amber L. Favaregh ◽  
Michael J. Hemsch ◽  
...  
Keyword(s):  
Aerodynamic Force ◽  
Database Development ◽  
Space Launch

Comparison of different types of stationary phases for the analysis of soy isoflavones by HPLC

2011 ◽  
Vol 400 (5) ◽  
pp. 1251-1261 ◽  
Author(s):  
N. Manchón ◽  
M. D’Arrigo ◽  
A. García-Lafuente ◽  
E. Guillamón ◽  
A. Villares ◽  
...  
Keyword(s):  
Stationary Phases ◽  
Soy Isoflavones ◽  
Different Types

scholarly journals Development of a biomarker database toward performing disease classification and finding disease interrelations

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Shaikh Farhad Hossain ◽  
Ming Huang ◽  
Naoaki Ono ◽  
Aki Morita ◽  
Shigehiko Kanaya ◽  
...  
Keyword(s):  
Rapid Detection ◽  
Molecular Mechanisms ◽  
Clustering Algorithm ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Network Clustering ◽  
Medical Field ◽  
Database Development ◽  
Abnormal State ◽  
Comprehensive Information

Abstract A biomarker is a measurable indicator of a disease or abnormal state of a body that plays an important role in disease diagnosis, prognosis and treatment. The biomarker has become a significant topic due to its versatile usage in the medical field and in rapid detection of the presence or severity of some diseases. The volume of biomarker data is rapidly increasing and the identified data are scattered. To provide comprehensive information, the explosively growing data need to be recorded in a single platform. There is no open-source freely available comprehensive online biomarker database. To fulfill this purpose, we have developed a human biomarker database as part of the KNApSAcK family databases which contain a vast quantity of information on the relationships between biomarkers and diseases. We have classified the diseases into 18 disease classes, mostly according to the National Center for Biotechnology Information definitions. Apart from this database development, we also have performed disease classification by separately using protein and metabolite biomarkers based on the network clustering algorithm DPClusO and hierarchical clustering. Finally, we reached a conclusion about the relationships among the disease classes. The human biomarker database can be accessed online and the inter-disease relationships may be helpful in understanding the molecular mechanisms of diseases. To our knowledge, this is one of the first approaches to classify diseases based on biomarkers. Database URL:  http://www.knapsackfamily.com/Biomarker/top.php

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