The effect of soy isoflavone on bone mineral density in postmenopausal Taiwanese women with bone loss: a 2-year randomized double-blind placebo-controlled study

[[abstract]]The treatment of 300-mg/day isoflavones (aglycone equivalents) (172.5 mg genistein+127.5 mg daidzein) for 2 years failed to prevent lumbar spine and total proximal femur bone mineral density (BMD) from declining as compared with the placebo group in a randomized, double-blind, two-arm designed study enrolling 431 postmenopausal women 45–65 years old. Introduction This study evaluated the effects of soy isoflavones on bone metabolism in postmenopausal women. Methods Four hundred and thirty-one women, aged 45–65 years, orally consumed 300-mg/day isoflavones (aglycone equivalents) or a placebo for 2 years in a parallel group, randomized, double-blind, two-armstudy. Each participant also ingested 600 mg of calcium and 125 IU of vitamin D3 per day. The BMD of the lumbar spine and total proximal femur were measured using dual-energy X-ray absorptiometry at baseline and every half-year thereafter. Serum bone-specific alkaline phosphatase, urinary N-telopeptide of type 1 collagen/creatinine, and other safety assessments were examined regularly. Results Two hundred out of 217 subjects in the isoflavone group and 199 out of 214 cases in placebo group completed the treatment. Serum concentrations of isoflavone metabolites, genistein and daidzein, of the intervention group were remarkably elevated following intake of isoflavones (p< 0.001). However, differences in the mean percentage changes of BMD throughout the treatment period were not statistically significant (lumbar spine, p=0.42; total femur, p=0.39) between the isoflavone and placebo groups, according to the generalized estimating equation (GEE) method. A significant time trend of bone loss was observed at both sites as assessed by the GEE method following repeated measurement of BMD (p<0.001). Differences in bone marker levels were not significant between the two treatment groups

Identification of Stage-Specific Breast Markers using Quantitative Proteomics

YesMatched healthy and diseased tissues from breast cancer patients were analyzed by quantitative proteomics. By comparing proteomic profiles of fibroadenoma (benign tumors, three patients), DCIS (noninvasive cancer, three patients), and invasive ductal carcinoma (four patients), we identified protein alterations that correlated with breast cancer progression. Three 8-plex iTRAQ experiments generated an average of 826 protein identifications, of which 402 were common. After excluding those originating from blood, 59 proteins were significantly changed in tumor compared with normal tissues, with the majority associated with invasive carcinomas. Bioinformatics analysis identified relationships between proteins in this subset including roles in redox regulation, lipid transport, protein folding, and proteasomal degradation, with a substantial number increased in expression due to Myc oncogene activation. Three target proteins, cofilin-1 and p23 (increased in invasive carcinoma) and membrane copper amine oxidase 3 (decreased in invasive carcinoma), were subjected to further validation. All three were observed in phenotype-specific breast cancer cell lines, normal (nontransformed) breast cell lines, and primary breast epithelial cells by Western blotting, but only cofilin-1 and p23 were detected by multiple reaction monitoring mass spectrometry analysis. All three proteins were detected by both analytical approaches in matched tissue biopsies emulating the response observed with proteomics analysis. Tissue microarray analysis (361 patients) indicated cofilin-1 staining positively correlating with tumor grade and p23 staining with ER positive status; both therefore merit further investigation as potential biomarkers.Cyprus Research Promotion Foundation, Yorkshire Cancer Researc

Semantic Similarity for Automatic Classification of Chemical Compounds

With the increasing amount of data made available in the chemical field, there is a strong need for systems capable of comparing and classifying chemical compounds in an efficient and effective way. The best approaches existing today are based on the structure-activity relationship premise, which states that biological activity of a molecule is strongly related to its structural or physicochemical properties. This work presents a novel approach to the automatic classification of chemical compounds by integrating semantic similarity with existing structural comparison methods. Our approach was assessed based on the Matthews Correlation Coefficient for the prediction, and achieved values of 0.810 when used as a prediction of blood-brain barrier permeability, 0.694 for P-glycoprotein substrate, and 0.673 for estrogen receptor binding activity. These results expose a significant improvement over the currently existing methods, whose best performances were 0.628, 0.591, and 0.647 respectively. It was demonstrated that the integration of semantic similarity is a feasible and effective way to improve existing chemical compound classification systems. Among other possible uses, this tool helps the study of the evolution of metabolic pathways, the study of the correlation of metabolic networks with properties of those networks, or the improvement of ontologies that represent chemical information

Toxicological aspects of the use of phenolic compounds in disease prevention

The consumption of a diet low in fat and enhanced by fruits and vegetables, especially rich in phenolic compounds, may reduce risks of many civilization diseases. The use of traditional medicines, mainly derived from plant sources, has become an attractive segment in the management of many lifestyle diseases. Concerning the application of dietary supplements (based on phenolic compounds) in common practice, the ongoing debate over possible adverse effects of certain nutrients and dosage levels is of great importance. Since dietary supplements are not classified as drugs, their potential toxicities and interactions have not been thoroughly evaluated. First, this review will introduce phenolic compounds as natural substances beneficial for human health. Second, the potential dual mode of action of flavonoids will be outlined. Third, potential deleterious impacts of phenolic compounds utilization will be discussed: pro-oxidant and estrogenic activities, cancerogenic potential, cytotoxic effects, apoptosis induction and flavonoid-drug interaction. Finally, future trends within the research field will be indicated