CardioML: integrating personal cardiac information for ubiquous diagnosis and analysis

The latest medical diagnosis devices enable the performance of e-diagnosis making the access to these services easier, faster and available in remote areas. However this imposes new communications and data interchange challenges. In this paper a new XML based format for storing cardiac signals and related information is presented. The proposed structure encompasses data acquisition devices, patient information, data description, pathological diagnosis and waveform annotation. When compared with similar purpose formats several advantages arise. Besides the full integrated data model it may also be noted the available geographical references for e-diagnosis, the multi stream data description, the ability to handle several simultaneous devices, the possibility of independent waveform annotation and a HL7 compliant structure for common contents. These features represent an enhanced integration with existent systems and an improved flexibility for cardiac data representation.

Multiple linear and principal component regressions for modelling ecotoxicity bioassay response

The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response.With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.

Vertex component analysis GPU-based implementation for hyperspectral unmixing

Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other methods vertex component analysis ( VCA) has become a very popular and useful tool to unmix hyperspectral data. VCA is a geometrical based method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Many Hyperspectral imagery applications require a response in real time or near-real time. Thus, to met this requirement this paper proposes a parallel implementation of VCA developed for graphics processing units. The impact on the complexity and on the accuracy of the proposed parallel implementation of VCA is examined using both simulated and real hyperspectral datasets.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Análise do sinal cardíaco ECG através da técnica SVD

Este trabalho surge no âmbito da área Electromedicina, uma componente da Engenharia Electrotécnica cada vez mais influente e em permanente desenvolvimento, existindo nela uma constante inovação e tentativa de desenvolvimento e aplicação de novas tecnologias. Este projecto possui como principal objectivo o estudo aprofundado das aplicações da técnica SVD (Singular Value Decomposition), uma poderosa ferramenta matemática que permite a manipulação de sinais através da decomposição de matrizes, ao caso específico do sinal eléctrico obtido através de um electrocardiograma (ECG). Serão discriminados os princípios da operação do sistema eléctrico cardíaco, as principais componentes do sinal ECG (a onda P, o complexo QRS e a onda T) e os fundamentos da técnica SVD. A última fase deste trabalho consistirá na aplicação, em ambiente Matlab, da técnica SVD a sinais ECG concretos, com enfase na sua filtragem, para efeitos de remoção de ruído. De modo verificar as suas vantagens e desvantagens face a outras técnicas, os resultados da filtragem por SVD serão comparados com aqueles obtidos, em condições similares, através da aplicação de um filtro FIR de coeficientes estáticos e de um filtro adaptativo iterativo.

Cardiac beta-receptors in experimental Chagas' disease

Experimental Chagas' disease (45 to 90 days post-infection) showed serious cardiac alterations in the contractility and in the pharmacological response to beta adrenergic receptors in normal and T. cruzi infected mice (post-acute phase). Chagasic infection did not change the beta receptors density (78.591 ± 3.125 fmol/mg protein and 73.647 ± 2.194 fmol/mg protein for controls) but their affinity was significantly diminished (Kd = 7.299 ± 0.426 nM and Kd = 3.759 ± 0.212 nM for the control) p < 0.001. This results demonstrate that the alterations in pharmacological response previously reported in chagasic myocardium are related to a significantly less beta cardiac receptor affinity. During this experimental period serious cardiac cell alterations take place and functional consequences will be detected in the chronic phase.

Cardiac sympathetic-parasympathetic balance in rats with experimentally-induced acute chagasic myocarditis

To clarify the mechanism responsible for the transient sinus tachycardia in rats with acute chagasic myocarditis, we have examined the cardiac sympathetic-parasympathetic balance of 29 rats inoculated with 200,000 parasites (Trypanosoma cruzi). Sixteen infected animals and 8 controls were studied between days 18 and 21 after inoculation (acute stage). The remaining 13 infected animals and 9 controls were studied between days 60 and 70 after inoculation (sub-acute stage). Under anesthesia (urethane 1.25 g/kg), all animals received intravenous atenolol (5 mg/kg) and atropine (10 mg/kg). Acute stage: The baseline heart rate of the infected animals was significantly higher than that of the controls (P < 0.0001). The magnitude of the negative chronotropic response to atenolol was 4 times that of the controls (P < 0.00001). This response correlated with the baseline heart rate (r= - 0.72, P < 0.001). The heart rate responses to the beta-blocker and to atropine, of the infected animals studied during the sub-acute stage, were not different from controls. These findings suggest that cardiac sympathetic activity is transiently enhanced and cardiac parasympathetic activity is not impaired, in rats with acute chagasic myocarditis. The transient predominance of cardiac sympathetic activity could explain, in part, the sinus tachycardia observed in the acute stage of experimentally-induced chagasic myocarditis.

Is CK-MB isoenzyme useful for diagnosis of cardiac involvement in icteric leptospirosis?

In the absence of heart failure or cardiogenic shock, cardiac involvement diagnosis in icteric leptospirosis is possible on the basis of abnormal electrocardiograms. As metabolic and electrolytic disorders are frequently seen during acute leptospirosis infection, they may be responsible for some electrocardiograms changes. We conducted a study to assess if creatine phosphokinase isoenzyme determinations are useful in selecting patients with a high cardiac involvement suspicion. Sixty-nine patients were studied prospectively. Ten patients out of 16 with cardiac involvement and 25 without had high CK-MB levels (p>0.05), although mean values of abnormal CK-MB levels were higher in the group with cardiac involvement (p<0.05). Our analysis indicates that serum CK-MB determination does not provide a specific indicator of myocardial involvement in the course of icteric leptospirosis.

Cochineal, a precious source of red: cochineal dyes characterization by high performance liquid chromatography with diode array detection and principal component analysis

Presented at Faculdade de Ciências e Tecnologias, Universidade de Lisboa, to obtain the Master Degree in Conservation and Restoration of Textiles

Factors that Influence Adherence to Exercise among Patients Attending a Cardiac Rehabilitation Program

Objective: The aim of this study was to compare the factors of adherence to physical activity in subjects attending a cardiac rehabilitation program, and subjects who have withdrawal this same program using the Transtheoretical Model of behavior change. Methods: We conducted an observational, cross sectional type study, with a sample of 33 individuals (15 currently participating in the Cardiac Rehabilitation Program and 18 who no more attended the same program), with the questionnaires being personally delivered or sent by mail. For data analysis, we used the computer program SPSS® version 16.0. The significance level was set at 0.05. Results: There were no significant differences in the states of Change, Self-efficacy, Decisional Balance and Change Processes in both groups. We obtained a high Spearman correlation between States of Change and Self-efficacy (r2 = 0.778) and the Pros (r2 = 0.764) and Againsts (r2 = -0.744) in Decisional Balance. However, there were no significant evidence to affirm that States of Change and experiential processes of change (p = 0.465) andbehavioral (p = 0.300) had a correlation. A relationship was found, in terms of proportions between physical activity incorporated or not in a Cardiac Rehabilitation Program and age (p = 0.003), occupation (p = 0.010) and the entity paying the costs of program (p = 0.027). Conclusion: It was concluded that perceived self-efficacy and Pros and Againsts of the Decisional Balance are related to adherence to physical activity. Results also point out that age, profession and the entity paying the costs of the program influences the dropout of Cardiac Rehabilitation Programs.

Quantitative analysis of cardiac lesions in chronic canine chagasic cardiomyopathy

Lesions observed in chronic chagasic cardiopathy frequently produce electrocardiographic alterations and affect cardiac function. Through a computerized morphometrical analysis we quantified the areas occupied by cardiac muscle, connective and adipose tissues in the right atrium of dogs experimentally infected with Trypanosoma cruzi. All of the infected dogs showed chronic myocarditis with variable reduction levels of cardiac muscle, fibrosis and adipose tissue replacement. In the atrial myocardium of dogs infected with Be78 and Be62 cardiac muscle represented 34 and 50%, fibrosis 28 and 32% and adipose tissue 38 and 18%, respectively. The fibrosis observed was both diffuse and focal and mostly intrafascicular, either partially or completely interrupting the path of muscle bundles. Such histological alterations probably contributed to the appearance of electrocardiographic disturbances verified in 10 out 11 dogs which are also common in human chronic chagasic cardiopathy. Fibrosis was the most important microscopic occurrence found since it produces rearrangements of collagen fibers in relation to myocardiocytes which causes changes in anatomical physiognomy and mechanical behavior of the myocardium. These abnormalities can contribute to the appearance of cardiac malfunction, arrythmias and congestive cardiac insufficiency as observed in two of the analyzed dogs. Strain Be78 caused destruction of atrial cardiac muscle higher than that induced by strain Be62.

Detection of cardiac biomarker proteins using a disposable based on a molecularly imprinted polymer grafted onto graphite

A low-cost disposable was developed for rapid detection of the protein biomarker myoglobin (Myo) as a model analyte. A screen printed electrode was modified with a molecularly imprinted material grafted on a graphite support and incorporated in a matrix composed of poly(vinyl chloride) and the plasticizer o-nitrophenyloctyl ether. The protein-imprinted material (PIM) was produced by growing a reticulated polymer around a protein template. This is followed by radical polymerization of 4-styrenesulfonic acid, 2-aminoethyl methacrylate hydrochloride, and ethylene glycol dimethacrylate. The polymeric layer was then covalently bound to the graphitic support, and Myo was added during the imprinting stage to act as a template. Non-imprinted control materials (CM) were also prepared by omitting the Myo template. Morphological and structural analysis of PIM and CM by FTIR, Raman, and SEM/EDC microscopies confirmed the modification of the graphite support. The analytical performance of the SPE was assessed by square wave voltammetry. The average limit of detection is 0.79 μg of Myo per mL, and the slope is −0.193 ± 0.006 μA per decade. The SPE-CM cannot detect such low levels of Myo but gives a linear response at above 7.2 μg · mL−1, with a slope of −0.719 ± 0.02 μA per decade. Interference studies with hemoglobin, bovine serum albumin, creatinine, and sodium chloride demonstrated good selectivity for Myo. The method was successfully applied to the determination of Myo urine and is conceived to be a promising tool for screening Myo in point-of-care patients with ischemia.

Protein-responsive polymers for point-of-care detection of cardiac biomarker

This work describes a novel use for the polymeric film, poly(o-aminophenol) (PAP) that was made responsive to a specific protein. This was achieved through templated electropolymerization of aminophenol (AP) in the presence of protein. The procedure involved adsorbing protein on the electrode surface and thereafter electroploymerizing the aminophenol. Proteins embedded at the outer surface of the polymeric film were digested by proteinase K and then washed away thereby creating vacant sites. The capacity of the template film to specifically rebind protein was tested with myoglobin (Myo), a cardiac biomarker for ischemia. The films acted as biomimetic artificial antibodies and were produced on a gold (Au) screen printed electrode (SPE), as a step towards disposable sensors to enable point-of-care applications. Raman spectroscopy was used to follow the surface modification of the Au-SPE. The ability of the material to rebind Myo was measured by electrochemical techniques, namely electrochemical impedance spectroscopy (EIS) and square wave voltammetry (SWV). The devices displayed linear responses to Myo in EIS and SWV assays down to 4.0 and 3.5 μg/mL, respectively, with detection limits of 1.5 and 0.8 μg/mL. Good selectivity was observed in the presence of troponin T (TnT) and creatine kinase (CKMB) in SWV assays, and accurate results were obtained in applications to spiked serum. The sensor described in this work is a potential tool for screening Myo in point-of-care due to the simplicity of fabrication, disposability, short time response, low cost, good sensitivity and selectivity.