ABSTRACT
The objective of this study is to investigate the expression and influence of adenosine triphosphate-sensitive potassium channel (KATP) in human umbilical arterial smooth muscle cells (HUASMCs) of patients with hypertensive disorders of pregnancy (HDP). Western blotting was used to detect the protein expression levels of KATP inwardly rectifying potassium channel (Kir)6.1 and sulphonylurea receptor (SUR)2B subunits in HUASMCs from patients with normal parturients (NP), gestational hypertension (GH), chronic hypertension (CH), preeclampsia (PE) and chronic hypertension with superimposed preeclampsia (CHSP), respectively. There was no significant difference in the protein expression of Kir6.1 subunit in NP group, GH group, CH group, PE group and CHSP group (P > 0.05). The protein expression of SUR2B subunit was gradually decreased in NP group, GH group, CH group, PE group and CHSP group, with statistically significant difference among the groups (P < 0.05). The altered expression level of KATP SUR2B subunit may be involved in the pathogenesis of HDP. The severity of HDP may be related to the degree of decrease of SUR2B subunit.
Subject(s)
Hypertension, Pregnancy-Induced , Pre-Eclampsia , Pregnancy , Female , Humans , Umbilical Arteries/metabolism , Pre-Eclampsia/genetics , Sulfonylurea Receptors/metabolism , Myocytes, Smooth Muscle/metabolism , Adenosine Triphosphate/metabolism , KATP Channels/genetics , KATP Channels/metabolismABSTRACT
Since most sensor pattern noise based image copy-move forensics methods require a known reference sensor pattern noise, it generally results in non-blinded passive forensics, which significantly confines the application circumstances. In view of this, a novel passive-blind image copy-move forensics scheme is proposed in this paper. Firstly, a color image is transformed into a grayscale one, and wavelet transform based de-noising filter is used to extract the sensor pattern noise, then the variance of the pattern noise, the signal noise ratio between the de-noised image and the pattern noise, the information entropy and the average energy gradient of the original grayscale image are chosen as features, non-overlapping sliding window operations are done to the images to divide them into different sub-blocks. Finally, the tampered areas are detected by analyzing the correlation of the features between the sub-blocks and the whole image. Experimental results and analysis show that the proposed scheme is completely passive-blind, has a good detection rate, and is robust against JPEG compression, noise, rotation, scaling and blurring.
