Correlation between polymorphisms in the estrogen receptor α gene and coronary heart disease: A meta-analysis.

The aim of this study was to conduct a systematic evaluation the correlation between polymorphisms in the estrogen receptor α gene (ESRα) and coronary heart disease susceptibility. Case-control studies until August 2015 analyzing the correlation between the ESRα PvuII T/C polymorphism and coronary heart disease were obtained from various electronic databases (CBM, CNKI, Wanfang Data, VIP, and MEDLINE, Cochrane Library, Embase, Springer, and Ovid. The data obtained from these studies were evaluated and valid data was extracted. A meta-analysis was performed using RevMan 5.0. Eleven cases, comprising 1742 patients with coronary heart disease and 2012 controls, that conformed to the inclusion criteria set in this study were extracted. The results of our meta-analysis indicated that the C and T alleles, the TC+CC and TT genotypes, and the CC and TT+TC genotypes did not differ significantly. The results of this meta-analysis confirmed that there was no correlation between polymorphisms in ESRα and coronary heart disease susceptibility in the Chinese population.

Weak cation magnetic separation technology and MALDI-TOF-MS in screening serum protein markers in primary type I osteoporosis.

We investigated weak cation magnetic separation technology and matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) in screening serum protein markers of primary type I osteoporosis. We selected 16 postmenopausal women with osteoporosis and nine postmenopausal women as controls to find a new method for screening biomarkers and establishing a diagnostic model for primary type I osteoporosis. Serum samples were obtained from controls and patients. Serum protein was extracted with the WCX protein chip system; protein fingerprints were examined using MALDI-TOF-MS. The preprocessed and model construction data were handled by the ProteinChip system. The diagnostic models were established using a genetic arithmetic model combined with a support vector machine (SVM). The SVM model with the highest Youden index was selected. Combinations with the highest accuracy in distinguishing different groups of data were selected as potential biomarkers. From the two groups of serum proteins, 123 cumulative MS protein peaks were selected. Significant intensity differences in the protein peaks of 16 postmenopausal women with osteoporosis were screened. The difference in Youden index between the four groups of protein peaks showed that the highest peaks had mass-to-charge ratios of 8909.047, 8690.658, 13745.48, and 15114.52. A diagnosis model was established with these four markers as the candidates, and the model specificity and sensitivity were found to be 100%. Two groups of specimens in the SVM results on the scatterplot were distinguishable. We established a diagnosis model, and provided a new serological method for screening and diagnosis of osteoporosis with high sensitivity and specificity.