ABSTRACT
Objective To study the expression and the mechanism of miR-155in the villi of patients with unexplained recurrent spontaneous abortion (URSA).Methods The expression of miR-155 in the villi of 36 cases with URSA (URSA group) and 25 women with normal early pregnancy (control group) were detected by stem-loop real-time reverse transcription (RT) qPCR.Expression of hypoxia inducible factor-1 (HIF-1α),vascular endothelial cell growth factor(VEGF) and micro lymphatic vessel density (MVD) in the villi of were measured by immnohistochemical staining among two groups.Results (1) miR-155 expression:the mean miR-155 expression were 1.456 (0.489,2.459) in URSA group and 2.833 (1.740,3.794) in control group,which reached statistical difference (P ＜0.05).The mean expression of miR-155 of 1.683 (0.902,2.459) in URSA group with abortion times (≤ 3) was significantly higher than 1.229 (0.489,1.719) in URSA group with more than 4 times abortion (P ＜ 0.05).(2) Indexes:the expression of HIF-1α,VEGF and MVD value were 121 ± 12,134 ± 12,36 ± 6 in URSA group and 99 ± 10,109 ± 10,28 ±4 in control group,which reached statistical difference(P ＜ 0.01).The expression of HIF-1α,VEGF and MVD value of 119 ± 12,134 ± 12,35 ± 5 in URSA group with less than 3 times abortion was significantly lower than 128 ± 12,138 ± 12,43 ± 6 in URSA group with more than 4 times abortion (P ＜ 0.01).Conclusions The expression of miR-155 and HIF-1α is topically stimulated by oxygen signal.HIF-1α adjusts the transcription and translation of VEGF,which together involved in placental trophoblast invasion and placental angiogenesis.The low expression of miR-155 could interfere with expression of HIF-1α and VEGF,which might be involved in villous vascular dysplasia in URSA.
ABSTRACT
This paper summarized the internal and external development of medical metrology, posed its main problem and studied its development trend.
Subject(s)
Biometry , Epidemiologic MeasurementsABSTRACT
It is important for cardiac therapy devices such as the automated external defibrillator to discriminate different cardiac disorders based on Electrocardiogram analysis. A phase space analysis based algorithm is proposed to detect cardiac arrhythmias effectively. Firstly, the phase space of the signal is reconstructed. Then from the viewpoint of geometry and information theory, the distribution entropy of the point density in the two-dimensional reconstructed phase space is calculated as the features in the further classification. Finally the nearest-neighbour method based on Mahalanobis distance is used to classify the sinus rhythm (SR), supraventricular tachyarrhythmia (SVTA), atrial flutter (AFL) and atrial fibrillation (AF). To evaluate the sensitivity, specificity and accuracy of this proposed method in the cardiac arrhythmias classification, the MIT-BIH arrhythmias database and the canine endocardial database are studied respectively. Experiment results demonstrate that the proposed method can detect SR, SVTA, AFL and AF signals rapidly and accurately with the simple computation. It promises to find application in automated devices for cardiac arrhythmias therapy.