OBJECTIVETo assess the effects of hormone replacement therapy (HRT) on platelet activation in postmenopausal women compared with premenopausal women.

METHODSThe expressions of CD41 and CD62P in fifteen postmenopausal women before and after HRT were detected using flow cytometry (FCM), with fifteen premenopausal women with a mean age of 47 years as controls.

RESULTSThe expressions of CD41 and CD62P in postmenopausal women were higher than those in the control group. CD62P(%), CD62P(I) and CD41 were reduced from 36.40 +/- 5.9, 37.75 +/- 5.8, and 470.11 +/- 74.0 to 27.97 +/- 5.6, 26.64 +/- 4.9, and 303.23 +/- 72.8 after six months of HRT (P < 0.05).

CONCLUSIONSPlatelet activation in postmenopausal women was higher than in premenopausal women and was reduced significantly after six months of HRT. HRT may have a favorable effect on reduction of platelet activity.

Adult ; Female ; Hormone Replacement Therapy ; Humans ; Middle Aged ; Platelet Activation ; drug effects ; Postmenopause ; physiology

Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) signal features, so that the feature selection is subjective, and the feature extraction is complex, leaving the classification accuracy usually affected. Based on this situation, a new method of arrhythmia automatic classification based on discriminative deep belief networks (DDBNs) is proposed. The morphological features of heart beat signals are automatically extracted from the constructed generative restricted Boltzmann machine (GRBM), then the discriminative restricted Boltzmann machine (DRBM) with feature learning and classification ability is introduced, and arrhythmia classification is performed according to the extracted morphological features and RR interval features. In order to further improve the classification performance of DDBNs, DDBNs are converted to deep neural network (DNN) using the Softmax regression layer for supervised classification in this paper, and the network is fine-tuned by backpropagation. Finally, the Massachusetts Institute of Technology and Beth Israel Hospital Arrhythmia Database (MIT-BIH AR) is used for experimental verification. For training sets and test sets with consistent data sources, the overall classification accuracy of the method is up to 99.84% ± 0.04%. For training sets and test sets with inconsistent data sources, a small number of training sets are extended by the active learning (AL) method, and the overall classification accuracy of the method is up to 99.31% ± 0.23%. The experimental results show the effectiveness of the method in arrhythmia automatic feature extraction and classification. It provides a new solution for the automatic extraction of ECG signal features and classification for deep learning.
Arrhythmias, Cardiac ; classification ; Databases, Factual ; Electrocardiography ; Heart Rate ; Humans ; Neural Networks (Computer)