Evidence-based personal applications of medical computing models in risk factors of cardiovascular disease for the middle-aged and elderly

Medical applications on cardiovascular disease (CVD) for hybrid computing models are an emerging research area. The CVD, including stroke, hypertension, and high cholesterol, is one of 10 leading causes of death in Taiwan in middle-aged and elderly; in particular, the CVD has become the top killer in advanced countries. Thus, this serious but interesting issue triggers the study to focus on patients of the CVD. The study explores variables, influencing cardiovascular functions for four risk factors of blood pressure, blood glucose, blood fat, and kidney diseases, in the middle-aged and elderly. By the data collection of regular physical examination system from a regional hospital, the original dataset contains 52 variables collected from October 2011 to February 2014. We model a hybrid knowledge-based classification system to organize expert experiences, integrated linear and nonlinear attribute selection methods, data discretization of smart expert method, rough set theory, the LEM2 algorithm, and rule-filtering technique to classify the CVD for the early warning purpose. After data cleaning, 20 attributes with 2027 records are remained. For effectively identifying the variables of CVD subjects, this study reclassifies the above four risk diseases into three classes: no disease, 1&2 diseases, and 3&4 diseases. To verify performance of the proposed procedure, we experience an empirical experiment to compare the full 20 used attributes, the used attributes of integrated linear and nonlinear attribute selections with rule-filtering technique, and various classifiers. Conclusively, the 13 used attributes obtained from optimal accuracy become the key determinants that affect the four risk factors of the CVD. The empirical results and findings benefit doctors’ and medical institutions’ early medical recommendations and treatments with the advantages of significantly reducing morbidity of CVD.     

Detection of heart valve disorders from PCG signals using TQWT,FA-MVEMD,Shannon energy envelope and deterministic learning

Artificial Intelligence Review - Heart valve disorders (HVDs) are the major causes of cardiovascular diseases (CVD), which may be detected at the early stage using routine auscultation examination....     

Multi-task learning with Multi-view Weighted Fusion Attention for artery-specific calcification analysis

In general, artery-specific calcification analysis comprises the simultaneous calcification segmentation and quantification tasks. It can help provide a thorough assessment for calcification of different coronary arteries, and further allow for an efficient and rapid diagnosis of cardiovascular diseases (CVD). However, as a high-dimensional multi-type estimation problem, artery-specific calcification analysis has not been profoundly investigated due to the intractability of obtaining discriminative feature representations. In this work, we propose a Multi-task learning network with Multi-view Weighted Fusion Attention (MMWFAnet) to solve this challenging problem. The MMWFAnet first employs a Multi-view Weighted Fusion Attention (MWFA) module to extract discriminative feature representations by enhancing the collaboration of multiple views. Specifically, MWFA weights these views to improve multi-view learning for calcification features. Based on the fusion of these multiple views, the proposed approach takes advantage of multi-task learning to obtain accurate segmentation and quantification of artery-specific calcification simultaneously. We perform experimental studies on 676 non-contrast Computed Tomography scans, achieving state-of-the-art performance in terms of multiple evaluation metrics. These compelling results evince that the proposed MMWFAnet is capable of improving the effectivity and efficiency of clinical CVD diagnosis.     

Vascular proteomics

The characterization of patients with acute coronary syndromes (ACS) at the molecular and cellular levels provides a novel vision for understanding the pathological and clinical expression of the disease. Recent advances in proteomic technologies permit the evaluation of systematic changes in protein expression in many biological systems and have been extensively applied to cardiovascular diseases (CVD). The cardiovascular system is in permanent intimate contact with blood, making blood-based biomarker discovery a particularly worthwhile approach. Thus, proteomics can potentially yield novel biomarkers reflecting CVD, establish earlier detection strategies, and monitor response to therapy. Here we review the different proteomic strategies used in the study of atherosclerosis and the novel proteins differentially expressed and secreted by atherosclerotic lesions which constitute novel potential biomarkers (HSP-27, Cathepsin D). Special attention is paid to MS-Imaging of atheroma plaque and the generation, for the first time, of 2-D images of lipids, showing the distribution of these molecules in the different areas of the atherosclerotic lesions. In addition new potential biomarkers have been identified in plasma (amyloid A1α, transtherytin), circulating cells (protein profile in monocytes from ACS patients) and individual cells constituents of atheroma plaques (endothelial, VSMC, macrophages) which provide novel insights into vascular pathophysiology.     

Urinary proteomics in cardiovascular disease: Achievements, limits and hopes

Cardiovascular disease (CVD) is the major cause of mortality and morbidity worldwide. Diagnosis of CVD and risk stratification of patients with CVD remains challenging despite the availability of a wealth of non-invasive and invasive tests. Clinical proteomics analyses a large number of peptides and proteins in biofluids. For clinical applications, the urinary proteome appears particularly attractive due to the relative low complexity compared with the plasma proteome and the noninvasive collection of urine. In this article, we review the results from pilot studies into urinary proteomics of coronary artery disease and discuss the potential of urinary proteomics in the context of pathogenesis of CVD.     

Applications of OLEDs that use VUV‐CVD films

Abstract— In photo‐CVD (chemical vapor deposition) in which vacuum‐ultraviolet (VUV) excimer lamps (VUV‐CVD) are used, thin films were deposited at room temperature because VUV photons have the energy to decompose material gases. For the use of OMCTS (octamethylcyclotetrasiloxane), an organic siloxane, we can deposit a self‐flatness film for high‐pressure conditions. The reactants generated by VUV photons have excellent migration characteristics for this condition. Also, the VUV‐CVD film demonstrates low stress, comparatively hard hardness, good electrical properties, and good thermal resistance. The VUV‐CVD film is optimum for planarizing film in the over‐coating deposition step in the production of OLEDs, which requires a low‐temperature process.     

Plasma damage‐free SiO2 deposition for low‐temperature poly‐Si AMLCDs

Abstract— High‐quality SiO₂ films have been fabricated at a substrate temperature of 300°C by utilizing a novel deposition method refered to as radical‐shower CVD (RS‐CVD), in which the substrates and material gases are completely separated from the plasma. On this account, SiO₂ deposition is achievable without plasma damage and excessive decomposition of the material gases. The electrical characteristics of RS‐CVD SiO₂ films are comparable to those of thermal SiO₂. Furthermore, the compact parallel‐plate structure of RS‐CVD is suitable for large‐area deposition.     

Silicon-carbide microfabrication by silicon lost molding for glass-press molds

This paper describes two silicon carbide (SiC) microfabrication processes for SiC glass-press molds. One is silicon lost molding combined with SiC chemical-vapor deposition (CVD) and SiC reaction sintering (RS). The other is silicon lost molding combined with SiC CVD and SiC solid-state reaction bonding (SSRB). In both of these processes, an original pattern on a silicon substrate is transferred to a CVD SiC film, and then the film is backed by bulk SiC to obtain rigidity and robustness against pressing force. Finally, the silicon substrate is etched away to release a SiC mold. In the process using SiC CVD and RS, an original pattern on a silicon substrate was transferred to a SiC mold, but the surface roughness of the SiC mold was 0.05-0.08 /spl mu/m Ra, and worse than required by the glass-press mold. This was caused by the transformation of amorphous SiC to polycrystalline SiC in RS, which was confirmed by the X-ray diffraction (XRD) data of the CVD SiC film before and after RS. In the process using SiC CVD and SSRB, the surface of the SiC mold was smooth (0.004-0.008 /spl mu/m Ra) without the crystallization of the amorphous CVD SiC film. The SiC mold was pressed to Pyrex glass to demonstrate its high-temperature strength. The Pyrex glass was deformed by the SiC mold at 850 /spl deg/C without a void, and no significant deformation of the SiC mold was observed.     

Effects of microstructure of films on CVD diamond X-ray detectors

Although the unique properties of chemical vapor deposition (CVD) diamond films have made it a candidate material for radiation detectors, the polycrystalline nature of the films has severely limited the development of CVD diamond detectors. In this work, three CVD diamond films with different microstructure were grown by using a hot-filament chemical vapor deposition (HFCVD) technique and were fabricated as CVD diamond detectors. The electric contact is good ohmic for bias voltage up to 150 V. 5.9 keV ⁵⁵Fe X-ray was used to measure the photocurrent and the pulse height distribution (PHD). For the detector based on the best quality film, the dark-current of 16.0 nA and the net photocurrent of 15.9 nA are obtained at an electric field of 50 kV cm⁻¹. The PHD peak is well separated from the noise pedestal, indicating a high counting efficiency and a low detection limit.     

Fixed-Parameter Algorithms for Cluster Vertex Deletion

We initiate the first systematic study of the NP-hard Cluster Vertex Deletion (CVD) problem (unweighted and weighted) in terms of fixed-parameter algorithmics. In the unweighted case, one searches for a minimum number of vertex deletions to transform a graph into a collection of disjoint cliques. The parameter is the number of vertex deletions. We present efficient fixed-parameter algorithms for CVD applying the fairly new iterative compression technique. Moreover, we study the variant of CVD where the maximum number of cliques to be generated is prespecified. Here, we exploit connections to fixed-parameter algorithms for (weighted) Vertex Cover.     

基于HRV分析的可穿戴心电仪精神疲劳检测

精神疲劳是许多慢性疾病如心血管疾病,糖尿病和癌症的关键原因,然而又难以量化评估及测量。本研究提出了一种通过智能穿戴设备检测脑力劳动者疲劳程度的工程可行性的方案。为了检测脑力疲劳程度,本文通过Man-Whitney U检验评估了HRV各项指标在判断精神疲劳状态的统计显著性,并使用随机森林进行特征选择以确定HRV各项指标的重要性。本文研究发现,最重要的HRV指标分别是NN.mean,PNN50,VLF,LF和TP。最后本文采用SVM、Na?ve Bayes、KNN和逻辑回归四种机器学习算法对进行疲劳状态进行识别,实验证明了KNN分类器最为有效,其交叉验证准确率为75.5％和AUC为0.74。     

Impedimetric, diamond-based immmunosensor for the detection of C-reactive protein

The high prevalence of cardiovascular diseases (CVD) demands a reliable and sensitive risk assessment technique. In order to develop a fast and label-free immunosensor for C-reactive protein (CRP), a risk factor for this condition, anti-CRP antibodies were physically adsorbed to the hydrogen (H)-terminated surface of nanocrystalline diamond (NCD). An Enzyme-Linked ImmunoSorbent Assay (ELISA) reference technique showed that this was a suitable substrate for antibody-antigen recognition reactions. Electrochemical Impedance Spectroscopy (EIS) was used to electronically detect CRP recognition. The specificity of the immunosensor was demonstrated by incubation with CRP and plasminogen as reference molecule. A different impedance behavior was observed in real-time after CRP addition as compared to plasminogen addition: the impedance increased only during CRP incubation. Fitting the data showed that this corresponded with a decrease in capacitance of the molecular layer due to its increased thickness by specific CRP recognition. Sensitivity experiments in real-time showed a clear discrimination between 1 μM, 100 nM, and 10 nM of CRP after 10 min at 100 Hz. Since, 10 nM of CRP was still clearly distinguishable from buffer solution, our CRP-directed immunosensor prototype reaches a sensitivity that is within the physiologically relevant concentration range of this biomarker in healthy controls and CVD patients. Moreover, this prototype displayed real-time discriminating power between spiked and unspiked serum, and thus also shows its applicability in this biological matrix.     

Proteomics of plaques and novel sources of potential biomarkers for atherosclerosis

Cardiovascular disease (CVD) is the leading cause of death and loss of productive life years in the world. The underlying syndrome of CVD, atherosclerosis, is a complex disease process, which involves lipid metabolism, inflammation, innate and adaptive immunity, and many other pathophysiological aspects. Furthermore, CVD is influenced by genetic as well as environmental factors. Early detection of CVD and identification of patients at risk are crucial to reduce the burden of disease and to allow personalized treatment. As established risk factors fail to accurately predict which part of the population is likely to suffer from the disease, novel biomarkers are urgently needed. Proteomics can play a significant role in identifying these biomarkers. In this review, we describe the progress made in proteome profiling of the atherosclerotic plaque and several novel sources of potential biomarkers, including circulating cells and plasma extracellular vesicles. The importance of longitudinal biobanking in biomarker discovery is highlighted and exemplified by several plaque proteins identified in the biobank study Athero-Express. Finally, we discuss the PTMs of proteins that are involved in atherosclerosis, which may become one of the foci in the ongoing quest for biomarkers through proteomics of plaque and other matrices relevant to the progression of atherosclerosis.     

Biomarkers for cardiovascular risk assessment in autoimmune diseases

Autoimmune diseases, such as antiphospholipid syndrome, systemic lupus erythematosus, and rheumatoid arthritis, are characterized by a high prevalence of cardiovascular (CV) disease (CVD), which constitutes the leading causes of morbidity and mortality among such patients. Although such effects are partly explained by a higher prevalence of traditional CV risk factors, many studies indicate that such factors do not fully explain the enhanced CV risk in these patients. In addition, risk stratification algorithms based upon traditional CV risk factors are not as predictive in autoimmune diseases as in the general population. For these reasons, the timely and accurate assessment of CV risk in these high-risk populations still remains an unmet clinical need. An enhanced contribution of different inflammatory components of the immune response, as well as autoimmune elements (e.g. autoantibodies, autoantigens, and cellular response), has been proposed to underlie the incremental CV risk observed in these populations. Recent advances in proteomic tools have contributed to the discovery of proteins involved in CVDs, including some that may be suitable to be used as biological markers. In this review we summarize the main markers in the field of CVDs associated with autoimmunity, as well as the recent advances in proteomic technology and their application for biomarker discovery in autoimmune disease.     

Cardiac ScoreCard: A diagnostic multivariate index assay system for predicting a spectrum of cardiovascular disease

Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard—a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC = 0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.     

CVD参数数据采集系统

在CVD金刚石膜生长过程中,衬底温度、热丝电流、反应室真空度和气源流量等是其重要的工艺参数.针对CVD金刚石膜沉积生长中四个重要的工艺参数,本文设计了数据采集系统.选用片内集成模拟量采集模块的8098单片机为下位机,上位机采用工控机,通过RS232串口进行通信.该主从式数据采集系统,硬件配置简单,抗干扰能力强,软件编程方便,人机界面友好.该系统已通过调试,运行良好.     

Mass spectrometric immunoassay and MRM as targeted MS-based quantitative approaches in biomarker development: Potential applications to cardiovascular disease and diabetes

Type 2 diabetes mellitus (T2DM) is an important risk factor for cardiovascular disease (CVD)—the leading cause of death in the United States. Yet not all subjects with T2DM are at equal risk for CVD complications; the challenge lies in identifying those at greatest risk. Therapies directed toward treating conventional risk factors have failed to significantly reduce this residual risk in T2DM patients. Thus newer targets and markers are needed for the development and testing of novel therapies. Herein we review two complementary MS-based approaches—mass spectrometric immunoassay (MSIA) and MS/MS as MRM—for the analysis of plasma proteins and PTMs of relevance to T2DM and CVD. Together, these complementary approaches allow for high-throughput monitoring of many PTMs and the absolute quantification of proteins near the low picomolar range. In this review article, we discuss the clinical relevance of the high density lipoprotein (HDL) proteome and Apolipoprotein A-I PTMs to T2DM and CVD as well as provide illustrative MSIA and MRM data on HDL proteins from T2DM patients to provide examples of how these MS approaches can be applied to gain new insight regarding cardiovascular risk factors. Also discussed are the reproducibility, interpretation, and limitations of each technique with an emphasis on their capacities to facilitate the translation of new biomarkers into clinical practice.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

金刚石薄膜的制备研究综述

金刚石薄膜具有优异的物理化学性质,在耐磨涂层、生物医学、薄膜微传感器、微机电系统等诸多领域有着广阔的应用前景.文章综述了近年来在金刚石薄膜领域研究的最新进展,着重介绍了目前主流的金刚石薄膜制备方法及优缺点,阐述了薄膜的生长机理以及提高金刚石薄膜沉积速率和质量的技术方法,为该领域的研究人员提供参考.