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BackgroundAcute coronary syndrome (ACS) consists of a range of acute myocardial ischemia-related manifestations. The adverse events of ACS are usually associated with ventricular dysfunction (VD), which could finally develop to heart failure. Currently, there is no satisfactory indicator that could specifically predict the development of ACS and its prognosis. Valosin-containing protein (VCP) has recently been proposed to protect against cardiac diseases. Hence, we aimed to assess whether VCP in serum can serve as a valuable biomarker for predicting ACS and its complication.MethodsHuman serum samples from 291 participants were collected and classified into four groups based on their clinical diagnosis, namely healthy control (n = 64), ACS (n = 40), chronic coronary syndrome (CCS, n = 99), and nonischemic heart disease (non-IHD, n = 88). Clinical characteristics of these participants were recorded and their serum VCP levels were detected by enzyme-linked immunosorbent assay (ELISA). Association of serum VCP with the development of ACS and its complication VD was statistically studied. Subsequently, GWAS and eQTL analyses were performed to explore the association between VCP polymorphism and monocyte count. A stability test was also performed to investigate whether VCP is a stable biomarker.ResultsSerum VCP levels were significantly higher in the ACS group compared with the rest groups. Besides, the VCP levels of patients with ACS with VD were significantly lower compared to those without VD. Multivariate logistic regression analysis revealed that VCP was associated with both the risk of ACS (P = 0.042, OR = 1.222) and the risk of developing VD in patients with ACS (P = 0.035, OR = 0.513) independently. The GWAS analysis also identified an association between VCP polymorphism (rs684562) and monocyte count, whereas the influence of rs684562 on VCP mRNA expression level was further verified by eQTL analysis. Moreover, a high stability of serum VCP content was observed under different preservation circumstances.ConclusionValosin-containing protein could act as a stable biomarker in predicting the development of ACS and its complication VD.

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With the wide use of color in many areas, the interest on the color perception and processing has been growing rapidly. An important topic in color image processing is the development of efficient tools capable of filtering images without blurring them and without changing their original chromatic contents. In this paper, a new technique reducing noise of color image is developed. A class of color-scale morphological operations is introduced, which extend mathematical morphology to color image processing, representing a color image as a vector function. The correlation between color components is utilized to perform noise removal.Color-scale morphological filters with multiple structuring elements (CSMF-MSEs) are proposed.Their properties are discussed and proved. Experimental results show that CSMF-MSEs are suitable and powerful to eliminate noise and preserve edges in color image because of efficient utilization of inherent correlation between color components, and they perform better than vector median filter. Key wordsColor-scale morphological operation (CSMO); Noise removal; RGB color space; Color-scale morphological filter with multiple structuring elements (CSMF-MSE) JOURNAL OF ELECTRONICS Vol.17 color image [41.Mathematical morphology has been widely used in image processing, such as noise removal, shape description, feature extraction, texture analysis and so on [5-71. It was presented to process binary image, referred to as binary morphology, and extended into gray-scale morphology. Some theoretical improvements on gray-scale morphology have been developed by Serra, Maragos, Heijimans, et al. [s-11]. Morphological filters are nonlinear signal transformations that locally modify geometric features of signals [12'131. Alternating sequential filters have been proposed[9,~4], and the design of morphological filters using multiple structuring elements have also been developed [~51. It have been shown that morphological filters can be efficiently used to clean noise in binary image and gray-scale image. The main advantages of morphological filters are able to preserve geometric structure and its simplicity in implementation. However, most of studies on morphological processing were mainly limited to binary and gray-scale morphology.In this paper, a new class of filters for color image processing is developed. The Color-Scale Morphological Operations(CSMOs) in RGB color space are introduced, which are an extension of mathematical morphology from gray-scale image to color image. Color-scale opening and closing are utilized to construct powerful Color-Scale Morphological Filter with Multiple Structuring Elements(CSMF-MSEs), called algebraic opening and algebraic closing. And more powerful filters, that is, color-scale alternating sequential filters, are developed. Properties of these CSMFs are analyzed and proved, they are quite similar to those of grayscale morphological filters including idempotency and edge preservation. Some experimental results show that the CSMF-MSEs are quite suitable and powerful to carr...

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