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Background and Objective: Myocardial infarction (MI) is a life-threatening condition diagnosed acutely on the electrocardiogram (ECG). Several errors, such as noise, can impair the prediction of automated ECG diagnosis. Therefore, quantification and communication of model uncertainty are essential for reliable MI diagnosis., Methods: A Dirichlet DenseNet model that could analyze out-of-distribution data and detect misclassification of MI and normal ECG signals was developed. The DenseNet model was first trained with the pre-processed MI ECG signals (from the best lead V6) acquired from the Physikalisch-Technische Bundesanstalt (PTB) database, using the reverse Kullback-Leibler (KL) divergence loss. The model was then tested with newly synthesized ECG signals with added em and ma noise samples. Predictive entropy was used as an uncertainty measure to determine the misclassification of normal and MI signals. Model performance was evaluated using four uncertainty metrics: uncertainty sensitivity (UNSE), uncertainty specificity (UNSP), uncertainty accuracy (UNAC), and uncertainty precision (UNPR); the classification threshold was set at 0.3., Results: The UNSE of the DenseNet model was low but increased over the studied decremental noise range (-6 to 24 dB), indicating that the model grew more confident in classifying the signals as they got less noisy. The model became more certain in its predictions from SNR values of 12 dB and 18 dB onwards, yielding UNAC values of 80% and 82.4% for em and ma noise signals, respectively. UNSP and UNPR values were close to 100% for em and ma noise signals, indicating that the model was self-aware of what it knew and didn't., Conclusion: Through this work, it has been established that the model is reliable as it was able to convey when it was not confident in the diagnostic information it was presenting. Thus, the model is trustworthy and can be used in healthcare applications, such as the emergency diagnosis of MI on ECGs., Competing Interests: Declaration of Competing Interest We would also like to declare that there is no real or potential conflict of interest in conjunction with the submission of this manuscript., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Objective. Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. Approach. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. Main results. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. Significance. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals., (© 2022 Institute of Physics and Engineering in Medicine.)

Myocardial infarction (MI) accounts for a high number of deaths globally. In acute MI, accurate electrocardiography (ECG) is important for timely diagnosis and intervention in the emergency setting. Machine learning is increasingly being explored for automated computer-aided ECG diagnosis of cardiovascular diseases. In this study, we have developed DenseNet and CNN models for the classification of healthy subjects and patients with ten classes of MI based on the location of myocardial involvement. ECG signals from the Physikalisch-Technische Bundesanstalt database were pre-processed, and the ECG beats were extracted using an R peak detection algorithm. The beats were then fed to the two models separately. While both models attained high classification accuracies (more than 95%), DenseNet is the preferred model for the classification task due to its low computational complexity and higher classification accuracy than the CNN model due to feature reusability. An enhanced class activation mapping (CAM) technique called Grad-CAM was subsequently applied to the outputs of both models to enable visualization of the specific ECG leads and portions of ECG waves that were most influential for the predictive decisions made by the models for the 11 classes. It was observed that Lead V4 was the most activated lead in both the DenseNet and CNN models. Furthermore, this study has also established the different leads and parts of the signal that get activated for each class. This is the first study to report features that influenced the classification decisions of deep models for multiclass classification of MI and healthy ECGs. Hence this study is crucial and contributes significantly to the medical field as with some level of visible explainability of the inner workings of the models, the developed DenseNet and CNN models may garner needed clinical acceptance and have the potential to be implemented for ECG triage of MI diagnosis in hospitals and remote out-of-hospital settings., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Rationale: Previous studies have shown that human embryonic stem cell-derived cardiomyocytes improved myocardial recovery when administered to infarcted pig and non-human primate hearts. However, the engraftment of intramyocardially delivered cells is poor and the effectiveness of clinically relevant doses of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) in large animal models of myocardial injury remains unknown. Here, we determined whether thymosin β4 (Tb4) could improve the engraftment and reparative potency of transplanted hiPSC-CMs in a porcine model of myocardial infarction (MI). Methods: Tb4 was delivered from injected gelatin microspheres, which extended the duration of Tb4 administration for up to two weeks in vitro . After MI induction, pigs were randomly distributed into 4 treatment groups: the MI Group was injected with basal medium; the Tb4 Group received gelatin microspheres carrying Tb4; the CM Group was treated with 1.2 × 10 8 hiPSC-CMs; and the Tb4+CM Group received both the Tb4 microspheres and hiPSC-CMs. Myocardial recovery was assessed by cardiac magnetic resonance imaging (MRI), arrhythmogenesis was monitored with implanted loop recorders, and tumorigenesis was evaluated via whole-body MRI. Results : In vitro , 600 ng/mL of Tb4 protected cultured hiPSC-CMs from hypoxic damage by upregulating AKT activity and BcL-XL and promoted hiPSC-CM and hiPSC-EC proliferation. In infarcted pig hearts, hiPSC-CM transplantation alone had a minimal effect on myocardial recovery, but co-treatment with Tb4 significantly enhanced hiPSC-CM engraftment, induced vasculogenesis and the proliferation of cardiomyocytes and endothelial cells, improved left ventricular systolic function, and reduced infarct size. hiPSC-CM implantation did not increase incidence of ventricular arrhythmia and did not induce tumorigenesis in the immunosuppressed pigs. Conclusions : Co-treatment with Tb4-microspheres and hiPSC-CMs was safe and enhanced the reparative potency of hiPSC-CMs for myocardial repair in a large-animal model of MI., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Aims: Recently, we demonstrated that the hearts of neonatal pigs (2-day old) have regenerative capacity, likely driven by cardiac myocyte division, but this potential is lost immediately after postnatal day 3. However, it is unknown if corticosteroid, a broad anti-inflammatory agent, will abrogate the regenerative capacity in the hearts of neonatal pigs. The aim of the current study is to evaluate the effect Dexamethasone (Dex), a broad anti-inflammatory agent, on heart regeneration, structure, and function of the neonatal pigs' post-myocardial infarction (MI)., Methods and Results: Dex (0.2 mg/kg/day) was injected intramuscularly into the neonatal pig (age: 2 days postnatal) during the first week post-MI. Myocardial scar and left ventricular function were determined by cardiac magnetic resonance (CMR) imaging. Bromodeoxyuridine (BrdU) pulse-chase labeling, histology, immunohistochemistry, and flow cytometry were performed to determine inflammatory cell infiltration, CM cytokinesis, and myocardial fibrosis. Dex injection during the first-week suppressed acute inflammation post-MI in the pig hearts. It inhibited BrdU incorporation to pig CMs and CM cytokinesis via inhibiting aurora-B protein expression which was associated with mature scar formation and thinned walls at the infarct site. CMR imaging showed Dex caused left ventricular aneurysm and poor ejection fraction., Conclusions: Dex inhibited CM cytokinesis and functional recovery and caused ventricular aneurysm in the hearts of 2-day old pigs post-MI., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: The adult mammalian heart has limited ability to repair itself after injury. Zebrafish, newts, and neonatal mice can regenerate cardiac tissue, largely by cardiac myocyte (CM) proliferation. It is unknown whether hearts of young large mammals can regenerate., Methods: We examined the regenerative capacity of the pig heart in neonatal animals (ages 2, 3, or 14 days postnatal) after myocardial infarction or sham procedure. Myocardial scar and left ventricular function were determined by cardiac magnetic resonance imaging and echocardiography. Bromodeoxyuridine pulse-chase labeling, histology, immunohistochemistry, and Western blotting were performed to study cell proliferation, sarcomere dynamics, and cytokinesis and to quantify myocardial fibrosis. RNA-sequencing was also performed., Results: After myocardial infarction, there was early and sustained recovery of cardiac function and wall thickness in the absence of fibrosis in 2-day-old pigs. In contrast, older animals developed full-thickness myocardial scarring, thinned walls, and did not recover function. Genome-wide analyses of the infarct zone revealed a strong transcriptional signature of fibrosis in 14-day-old animals that was absent in 2-day-old pigs, which instead had enrichment for cytokinesis genes. In regenerating hearts of the younger animals, up to 10% of CMs in the border zone of the myocardial infarction showed evidence of DNA replication that was associated with markers of myocyte division and sarcomere disassembly., Conclusions: Hearts of large mammals have regenerative capacity, likely driven by cardiac myocyte division, but this potential is lost immediately after birth.

This work presents a novel analysis methodology that utilises high-resolution, multi-dimensional information to better classify regions of the left ventricle after myocardial infarction. Specifically, the focus is to determine degree of infarction in regions of the left ventricle based on information extracted from cardiac magnetic resonance imaging. Enhanced classification accuracy is achieved using three mechanisms: Firstly, a plurality of indices/features is used in the pattern classification process, rather than a single index/feature (hence the term "multi-dimensional). Secondly, the method incorporates not only the indices/features of the region in consideration, but also indices/features from the neighbouring regions (hence the term "proprio-proximus"). Thirdly, advanced machine learning techniques are used for both feature selection and pattern classification process to ameliorate the effect of class-imbalance existing in the data. Numerical results from multiple experiments on real data showed that using multiple features improved the ability to distinguish between infarcted and non-infarcted remote segments, and using neighbouring information improved classification performance. The proposed methodology is general and can be adapted for the analysis of biological functions of other human organs., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Myocardial infarction (MI), also referred to as heart attack, occurs when there is an interruption of blood flow to parts of the heart, due to the acute rupture of atherosclerotic plaque, which leads to damage of heart muscle. The heart muscle damage produces changes in the recorded surface electrocardiogram (ECG). The identification of MI by visual inspection of the ECG requires expert interpretation, and is difficult as the ECG signal changes associated with MI can be short in duration and low in magnitude. Hence, errors in diagnosis can lead to delay the initiation of appropriate medical treatment. To lessen the burden on doctors, an automated ECG based system can be installed in hospitals to help identify MI changes on ECG. In the proposed study, we develop a single-channel single lead ECG based MI diagnostic system validated using noisy and clean datasets. The raw ECG signals are taken from the Physikalisch-Technische Bundesanstalt database. We design a novel two-band optimal biorthogonal filter bank (FB) for analysis of the ECG signals. We present a method to design a novel class of two-band optimal biorthogonal FB in which not only the product filter but the analysis lowpass filter is also a halfband filter. The filter design problem has been composed as a constrained convex optimization problem in which the objective function is a convex combination of multiple quadratic functions and the regularity and perfect reconstruction conditions are imposed in the form linear equalities. ECG signals are decomposed into six subbands (SBs) using the newly designed wavelet FB. Following to this, discriminating features namely, fuzzy entropy (FE), signal-fractal-dimensions (SFD), and renyi entropy (RE) are computed from all the six SBs. The features are fed to the k-nearest neighbor (KNN). The proposed system yields an accuracy of 99.62% for the noisy dataset and an accuracy of 99.74% for the clean dataset, using 10-fold cross validation (CV) technique. Our MI identification system is robust and highly accurate. It can thus be installed in clinics for detecting MI., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The diagnosis of Coronary Artery Disease (CAD), Myocardial Infarction (MI) and carotid atherosclerosis is of paramount importance, as these cardiovascular diseases may cause medical complications and large number of death. Ultrasound (US) is a widely used imaging modality, as it captures moving images and image features correlate well with results obtained from other imaging methods. Furthermore, US does not use ionizing radiation and it is economical when compared to other imaging modalities. However, reading US images takes time and the relationship between image and tissue composition is complex. Therefore, the diagnostic accuracy depends on both time taken to read the images and experience of the screening practitioner. Computer support tools can reduce the inter-operator variability with lower subject specific expertise, when appropriate processing methods are used. In the current review, we analysed automatic detection methods for the diagnosis of CAD, MI and carotid atherosclerosis based on thoracic and Intravascular Ultrasound (IVUS). We found that IVUS is more often used than thoracic US for CAD. But for MI and carotid atherosclerosis IVUS is still in the experimental stage. Furthermore, thoracic US is more often used than IVUS for computer aided diagnosis systems., (Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Early expansion of infarcted zone after Acute Myocardial Infarction (AMI) has serious short and long-term consequences and contributes to increased mortality. Thus, identification of moderate and severe phases of AMI before leading to other catastrophic post-MI medical condition is most important for aggressive treatment and management. Advanced image processing techniques together with robust classifier using two-dimensional (2D) echocardiograms may aid for automated classification of the extent of infarcted myocardium. Therefore, this paper proposes novel algorithms namely Curvelet Transform (CT) and Local Configuration Pattern (LCP) for an automated detection of normal, moderately infarcted and severely infarcted myocardium using 2D echocardiograms. The methodology extracts the LCP features from CT coefficients of echocardiograms. The obtained features are subjected to Marginal Fisher Analysis (MFA) dimensionality reduction technique followed by fuzzy entropy based ranking method. Different classifiers are used to differentiate ranked features into three classes normal, moderate and severely infarcted based on the extent of damage to myocardium. The developed algorithm has achieved an accuracy of 98.99%, sensitivity of 98.48% and specificity of 100% for Support Vector Machine (SVM) classifier using only six features. Furthermore, we have developed an integrated index called Myocardial Infarction Risk Index (MIRI) to detect the normal, moderately and severely infarcted myocardium using a single number. The proposed system may aid the clinicians in faster identification and quantification of the extent of infarcted myocardium using 2D echocardiogram. This system may also aid in identifying the person at risk of developing heart failure based on the extent of infarcted myocardium., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Cross-sectional view echocardiography is an efficient non-invasive diagnostic tool for characterizing Myocardial Infarction (MI) and stages of expansion leading to heart failure. An automated computer-aided technique of cross-sectional echocardiography feature assessment can aid clinicians in early and more reliable detection of MI patients before subsequent catastrophic post-MI medical conditions. Therefore, this paper proposes a novel Myocardial Infarction Index (MII) to discriminate infarcted and normal myocardium using features extracted from apical cross-sectional views of echocardiograms. The cross-sectional view of normal and MI echocardiography images are represented as textons using Maximum Responses (MR8) filter banks. Fractal Dimension (FD), Higher-Order Statistics (HOS), Hu's moments, Gabor Transform features, Fuzzy Entropy (FEnt), Energy, Local binary Pattern (LBP), Renyi's Entropy (REnt), Shannon's Entropy (ShEnt), and Kapur's Entropy (KEnt) features are extracted from textons. These features are ranked using t-test and fuzzy Max-Relevancy and Min-Redundancy (mRMR) ranking methods. Then, combinations of highly ranked features are used in the formulation and development of an integrated MII. This calculated novel MII is used to accurately and quickly detect infarcted myocardium by using one numerical value. Also, the highly ranked features are subjected to classification using different classifiers for the characterization of normal and MI LV ultrasound images using a minimum number of features. Our current technique is able to characterize MI with an average accuracy of 94.37%, sensitivity of 91.25% and specificity of 97.50% with 8 apical four chambers view features extracted from only single frame per patient making this a more reliable and accurate classification., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Myocardial Infarction (MI) or acute MI (AMI) is one of the leading causes of death worldwide. Precise and timely identification of MI and extent of muscle damage helps in early treatment and reduction in the time taken for further tests. MI diagnosis using 2D echocardiography is prone to inter-/intra-observer variability in the assessment. Therefore, a computerised scheme based on image processing and artificial intelligent techniques can reduce the workload of clinicians and improve the diagnosis accuracy. A Computer-Aided Diagnosis (CAD) of infarcted and normal ultrasound images will be useful for clinicians. In this study, the performance of CAD approach using Discrete Wavelet Transform (DWT), second order statistics calculated from Gray-Level Co-Occurrence Matrix (GLCM) and Higher-Order Spectra (HOS) texture descriptors are compared. The proposed system is validated using 400 MI and 400 normal ultrasound images, obtained from 80 patients with MI and 80 normal subjects. The extracted features are ranked based on t-value and fed to the Support Vector Machine (SVM) classifier to obtain the best performance using minimum number of features. The features extracted from DWT coefficients obtained an accuracy of 99.5%, sensitivity of 99.75% and specificity of 99.25%; GLCM have achieved an accuracy of 85.75%, sensitivity of 90.25% and specificity of 81.25%; and HOS obtained an accuracy of 93.0%, sensitivity of 94.75% and specificity of 91.25%. Among the three techniques presented DWT yielded the highest classification accuracy. Thus, the proposed CAD approach may be used as a complementary tool to assist cardiologists in making a more accurate diagnosis for the presence of MI., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

In this work, we present a method to assess left ventricle (LV) regional function from cardiac magnetic resonance (CMR) imaging based on the regional ejection fraction (REF) and regional area strain (RAS). CMR scans were performed for 30 patients after first-time myocardial infarction (MI) and nine age- and sex-matched healthy volunteers. The CMR images were processed to reconstruct three-dimensional LV geometry, and the REF and RAS in a 16-segment model were computed using our proposed methodology. The method of computing the REF was tested and shown to be robust against variation in user input. Furthermore, analysis of data was feasible in all patients and healthy volunteers without any exclusions. The REF correlated well with the RAS in a nonlinear manner (quadratic fit-R(2) = 0.88). In patients after first-time MI, the REF and RAS were significantly reduced across all 16 segments (REF: p < 0.05; RAS: p < 0.01). Moreover, the REF and RAS significantly decreased with the extent of transmural scar obtained from late gadolinium-enhanced CMR images. In addition, we show that the REF and RAS can be used to identify regions with compromised function in the patients with preserved global ejection fraction with reasonable accuracy (more than 78%). These preliminary results confirmed the validity of our approach for accurate analysis of LV regional function. Our approach potentially offers physicians new insights into the local characteristics of the myocardial mechanics after a MI., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

Myocardial infarction (MI) or acute myocardial infarction commonly known as heart attack is one of the major causes of cardiac death worldwide. It occurs when the blood supply to the portion of the heart muscle is blocked or stopped causing death of heart muscle cells. Early detection of MI will help to prevent the infarct expansion leading to left ventricle (LV) remodeling and further damage to the cardiac muscles. Timely identification of MI and the extent of LV remodeling are crucial to reduce the time taken for further tests, and save the cost due to early treatment. Echocardiography images are widely used to assess the differential diagnosis of normal and infarcted myocardium. The reading of ultrasound images is subjective due to interobserver variability and may lead to inconclusive findings which may increase the anxiety for patients. Hence, a computer-aided diagnostic (CAD) technique which uses echocardiography images of the heart coupled with pattern recognition algorithms can accurately classify normal and infarcted myocardium images. In this review paper, we have discussed the various components that are used to develop a reliable CAD system.

Geometric remodelling of the left ventricle (LV) following myocardial infarction reflects on the geometric characteristics directly. This study focuses on a potential index based on curvedness. Nine consecutive normal volunteers and thirty consecutive myocardial infarction patients underwent MRI scan (twenty-seven patients had follow-up scan). Short-axis cine images of all cases were delineated. Three dimensional LV models were reconstructed and restored for possible motion distortion. The curvedness values were computed over 16-segments nomenclature. The curvedness signal for each segment over twenty-two time frames were fitted using a second order Fourier Series. Fourier coefficients were extracted and unsupervised learning was conducted between normal and patient data. An accuracy of 89% and adjusted Rand Index of 0.5374 suggest that these Fourier Series and curvedness based features can be an useful index for prognosis and diagnosis in clinical practice.

A computational method for quantifying left ventricle (LV) remodeling using 3D mesh models reconstructed from magnetic resonance imaging is proposed. The underlying geometry of the LV mesh is obtained by using a quadric fitting method, and its quantification is performed by using a curvedness shape descriptor. To achieve robustness, we have performed detailed studies of the effects of n-ring parameter selection on the accuracy of this method with in vitro and in vivo LV models. We have found that curvedness calculations based on a 5-ring selection can accurately depict anomalies in LV shape despite the presence of noise due to manual image segmentation. Our studies show that patients after myocardial infarction exhibit significant LV shape alteration in terms of curvedness, in particular at the apex. The diastole-to-systole change in regional curvedness was significantly lower suggesting regional differences in hypokinesis due to infarcted myocardium. This approach may add new insights into ventricular deformation and enable better discrimination between normal and pathologic conditions., (Crown Copyright © 2011. Published by Elsevier Ireland Ltd. All rights reserved.)

Geometric remodeling of the left ventricle (LV) after myocardial infarction is associated with changes in myocardial wall stress. The objective of this study was to determine the regional curvatures and wall stress based on three-dimensional (3-D) reconstructions of the LV using MRI. Ten patients with ischemic dilated cardiomyopathy (IDCM) and 10 normal subjects underwent MRI scan. The IDCM patients also underwent delayed gadolinium-enhancement imaging to delineate the extent of myocardial infarct. Regional curvedness, local radii of curvature, and wall thickness were calculated. The percent curvedness change between end diastole and end systole was also calculated. In normal heart, a short- and long-axis two-dimensional analysis showed a 41 +/- 11% and 45 +/- 12% increase of the mean of peak systolic wall stress between basal and apical sections, respectively. However, 3-D analysis showed no significant difference in peak systolic wall stress from basal and apical sections (P = 0.298, ANOVA). LV shape differed between IDCM patients and normal subjects in several ways: LV shape was more spherical (sphericity index = 0.62 +/- 0.08 vs. 0.52 +/- 0.06, P < 0.05), curvedness at end diastole (mean for 16 segments = 0.034 +/- 0.0056 vs. 0.040 +/- 0.0071 mm(-1), P < 0.001) and end systole (mean for 16 segments = 0.037 +/- 0.0068 vs. 0.067 +/- 0.020 mm(-1), P < 0.001) was affected by infarction, and peak systolic wall stress was significantly increased at each segment in IDCM patients. The 3-D quantification of regional wall stress by cardiac MRI provides more precise evaluation of cardiac mechanics. Identification of regional curvedness and wall stresses helps delineate the mechanisms of LV remodeling in IDCM and may help guide therapeutic LV restoration.

Background: We sought to compare host immune cell kinetics, survival profile of donor skeletal myoblasts, and skeletal myoblast graft efficacy after autologous and allogeneic skeletal myoblast transplantation into a rat model of myocardial infarction., Methods: One week after myocardial infarction, 128 animals were divided into four groups: group 1 (n = 24, receiving medium only), group 2 (n = 24, receiving medium and cyclosporine), group 3 (n = 40, autologous skeletal myoblast transplantation), and group 4 (n = 40, allogeneic skeletal myoblast transplantation with cyclosporine treatment). Rats were euthanized 10 minutes, 1 day, and 4, 7, and 28 days later. Host immune cell kinetics were assessed by immunohistochemical studies for macrophages, and CD4+ and CD8+ lymphocytes. Donor skeletal myoblast survival was confirmed by tracking prelabeled signals, and quantified by beta-gal assay. Heart function was evaluated by echocardiography., Results: A transient immune cell infiltration was demonstrated in group 3, with macrophage infiltration on day 1 and day 4, CD8+ cell infiltration on day 4 and day 7, and CD4+ cell infiltration on day 4. In group 4, immunocyte infiltration was slightly more severe than that in group 3. Automyoblasts and allomyoblasts showed no significant difference of survival from day 1 to day 7 (p > 0.10); however, on day 28, automyoblasts showed better survival than allomyoblasts (p < 0.05). Transplantation of allomyoblasts increased systolic heart function and limited heart dilation after myocardial injury to a similar degree as automyoblasts (p > 0.10)., Conclusions: The use of allomyoblasts is feasible and effective for cardiac repair with immunosuppressive treatment as compared with automyoblasts.

Objective: We sought to investigate immune cell kinetics in relation to skeletal myoblast survival and heart function improvement after nonautologous skeletal myoblast transplantation in a rat model of myocardial infarction., Methods: One week after myocardial infarction, 208 Wistar rats were grouped into group 1 (n = 24, receiving 150 muL of medium only), group 2 (n = 24, receiving 150 muL of medium and cyclosporine [INN: ciclosporin]), group 3 (n = 40, human skeletal myoblast transplantation), group 4 (n = 40, human skeletal myoblast transplantation with cyclosporine treatment), group 5 (n = 40, rat skeletal myoblast transplantation), and group 6 (n = 40, rat skeletal myoblast transplantation with cyclosporine treatment). The hearts were harvested at 10 minutes and 1, 4, 7, and 28 days after cell transplantation. Skeletal myoblast survival was confirmed by means of immunohistochemical studies and quantified by using real-time polymerase chain reaction. Host immune responses were assessed by immunostaining for macrophages and CD4+ and CD8+ lymphocytes. Heart function was evaluated by means of echocardiographic analysis., Results: The majority of macrophages and lymphocytes infiltrated in the acute phase (from day 1 to day 7) and then subsided by day 28. The donor skeletal myoblasts survived and differentiated well in all skeletal myoblast transplantation groups. Allogeneic skeletal myoblasts showed a superior survival rate than xenogeneic skeletal myoblasts (P < .01). Cyclosporine inhibited the infiltration of the immunocytes, enhanced skeletal myoblast survival, and improved heart performance compared with that seen in the groups not receiving cyclosporine treatment (P < .05)., Conclusions: Allomyoblasts survive better than do xenomyoblasts after transplantation into infarcted myocardium. After inhibition of immunocyte infiltration by means of immunosuppressive treatment, skeletal myoblast survival is enhanced, with improved heart performance. These findings suggest the feasibility of nonautologous myoblast transplantation with immunosuppressive treatment.

Unlabelled: We compare the effectiveness of direct adenoviral angiopoietin-1 (Ad-Ang-1) injection with transplantation of skeletal myoblasts (SkMs) over-expressing angiopoietin-1 (Ang-1) for angiogenic response and improvement of heart function in an experimental porcine model of myocardial infarction (MI)., Methods: Ad-Ang-1 was used for intramyocardial injection or transduction of SkMs. Three weeks after coronary artery ligation in 32 female pigs, animals were grouped to receive multiple intramyocardial injections of DMEM without cells (group-1; n=7), or containing 3 x 10(8)Lac-z labelled SkMs transduced with Ad-Null vector carrying no gene (group-2; n=7), or 1 x 10(10) PFU Ad-Ang-1 (group-3; n=9), or 3 x 10(8)Lac-z labelled SkMs transduced with Ad-Ang-1 (group-4; n=9). The animals were immunosuppressed for 6-weeks. After euthanasia, their heart tissue was processed for histological studies., Results: Extensive survival of Lac-z positive SkMs was observed in and around the infarct 6 and 12-weeks after transplantation. Fluorescent immunostaining for vWF-VIII at 6-weeks revealed increased blood vessel density (x100) in group-4 (p<0.05) as compared with other groups. Regional blood flow (ml/g/min) in the peri-infarct area was improved in group-4 (2.7; p<0.05) as compared with group-1 (1.2+/-0.1), group-2 (1.1+/-0.4) and group-3 (1.7+/-0.1) at 6-weeks. Similarly, ejection fraction was significantly higher in group-4 (49.2+/-5.9%, p=0.03) as compared with group-1 (36.8+/-3%) at 6 weeks., Conclusion: SkMs mediated Ang-1 delivery is associated with improved angiogenic response, regional myocardial perfusion and heart function as compared with direct Ad-Ang-1 administration.

Objective: To achieve angiogenic interaction between VEGF(165) and angiopoietin-1 (Ang-1) using a novel adenoviral bicistronic vector (Ad-Bic) encoding the two factors and delivered ex vivo using sex-mismatched human skeletal myoblasts., Methods and Results: A myocardial infarction model was developed in 29 female pigs; randomised into four groups: DMEM (group-1, n=6); Adenovirus null (Ad-null) vector-myoblast (group-2, n=5); Ad-Ang-1 myoblast (group 3, n=7) and Ad-Bic-myoblast (group-4, n=11). Three weeks later, 5 ml DMEM without myoblasts or containing 3 x 10(8) myoblasts carrying lac-z gene and transduced with Ad-null, Ad-Ang-1 or Ad-Bic were injected intra-myocardially in and around the infarct. 2D-echocardiography and fluorescent microsphere studies 6- and 12-weeks post-treatment revealed significantly improved cardiac performance and regional blood flow in groups 3 and 4. Histological studies and Y-chromosome analysis revealed extensive survival of lac-z positive myoblasts staining positive for human proteins in the pig heart. ELISA, immunostaining and RT-PCR revealed that Ad-Bic transduced myoblasts concomitantly but transiently expressed hVEGF(165) and Ang-1 both in vitro and in vivo. Double fluorescent immunostaining of the tissue sections for vWFactor-III and smooth muscle actin showed significantly higher vascular density of mature blood vessels per low power microscopic field in groups 3 and 4 at 6- and 12-weeks., Conclusion: Our combined approach led to enhanced angiogenesis with a greater percentage of functionally mature blood vessels in a porcine heart.

Anticoagulants play a vital role as part of the antithrombotic therapy of myocardial infarction and are complementary to antiplatelet therapies. In the acute setting, the rationale for their use is to antagonize the ongoing clotting cascade including during percutaneous coronary intervention. Anticoagulation may be an important part of the longer-term antithrombotic strategy especially in patients who have other existing indications (e.g. atrial fibrillation) for their use. In this narrative review, the authors provide a contemporary summary of the anticoagulation strategies of patients presenting with NSTEMI, both in terms of anticoagulation during the acute phase as well as suggested antithrombotic regimens for patients who require long-term anticoagulation for other indications. Patients presenting with non-ST-elevation myocardial infarction (NSTEMI) should be initiated on anticoagulation (e.g. heparin/low molecular weight heparin) for the initial hospitalization period for those medically managed or until percutaneous coronary intervention. Longer term management of NSTEMI for patients with an existing indication for long-term anticoagulation should comprise triple antithrombotic therapy of anticoagulant (preferably DOAC) with aspirin and clopidogrel for up to 1 month (typically 1 week or until hospital discharge), followed by DOAC plus clopidogrel for up to 1 year, and then DOAC monotherapy thereafter. [ABSTRACT FROM AUTHOR]

The emergence of new cardiac diagnostics and therapeutics of the heart has given rise to the challenging field of virtual design and testing of technologies in a patient-specific environment. Given the recent advances in medical imaging, computational power and mathematical algorithms, patient-specific cardiac models can be produced from cardiac images faster, and more efficiently than ever before. The emergence of patient-specific computational fluid dynamics (CFD) has paved the way for the new field of computer-aided diagnostics. This article provides a review of CFD methods, challenges and opportunities in coronary and intra-cardiac flow simulations. It includes a review of market products and clinical trials. Key components of patient-specific CFD are covered briefly which include image segmentation, geometry reconstruction, mesh generation, fluid-structure interaction, and solver techniques. [ABSTRACT FROM AUTHOR]

Parasternal and apical echocardiography images captured from different cross-sectional planes (short-axis and four chambers) convey significant information about the structure and function of infarcted Left Ventricular (LV) myocardium. Thus, features from these cross-sectional views of echocardiograms extracted using computer-aided techniques may aid in characterizing Myocardial Infarction (MI). Therefore, this paper proposes a new algorithm for automated MI characterization using features extracted from parasternal short axis and apical four chambers cross-sectional views of 160 subjects (80 with MI and 80 normal) echocardiograms. The Stationary Wavelet Transform (SWT) method is used to extract the Relative Wavelet Energy and Entropy (RWE and RWEnt) features from the two cross-sectional views of echocardiography images separately. These features are ranked and subjected to classification in two different steps: (i) the features from each view are separately ranked using entropy, t-test and Wilcoxon ranking tests and fed to the classifier, and (ii) later, the features from both the views are combined and ranked. Finally, these ranked features are subjected to the Support Vector Machine (SVM) classifier for characterization of normal and MI using a minimum number of features. The proposed method is able to identify MI with 95.0% of accuracy, 93.7% of sensitivity and 96.2% of specificity using 32 features extracted from parasternal short-axis view; an accuracy of 96.2%, sensitivity of 97.5% and specificity of 95.0% with 18 apical four chamber view features. The results show that by combining the features from both views enables the confirmation of MI LVs with an accuracy of 96.8%, sensitivity of 93.7% and specificity of 100% using 16 features extracted from only two frames. Software development is in progress which can be incorporated into the echocardiography ultrasound machine for automated detection of MI patients. [ABSTRACT FROM AUTHOR]

The left ventricle (LV) is modelled as a fluid-filled, thick-walled finite-elasticity cylindrical shell, subject to internal pressure increase during isovolumic contraction. Our objective is to elucidate that the tremendous internal pressure build-up during isovolumic contraction is caused by stress development in the spirally-wound myocardial fibers due to their contraction. The LV model data consists of LV chamber pressure, LV dynamic geometry and LV twist angle. In our analysis, the LV chamber pressure increase and LV (radial, longitudinal, and twist) deformations are formatted to be caused by the contractile stresses in the LV myocardial fibers (based on the hyper-elastic constitutive property of the LV myocardial wall, expressed in terms of the strain energy density function). The LV wall stresses are expressed in terms of the strain energy density function, and hence in terms of the measured LV wall strains and the material parameters. Then, by satisfying the stress boundary conditions, from the measured data on LV deformation state and LV pressure, we first determine the LV wall's constitutive properties, and then the instantaneous stress state in the LV. The stress generated in the LV cylindrical model is equivalent to the development of active compression force and torsion within the model, as a mechanism for the high intra-LV cavity pressure build-up during isovolumic contraction. We in turn adopt (i) the principal compressive stress to be the stresses developed in the myocardial fibers (by their contraction), and (ii) the principal stress trajectory to correspond to the orientation of the myocardial fibers. The results show that the myocardial fiber orientation changes during the isovolumic phase, as the LV contracts. Hence, an important determinant of our analysis is the orientation of the myocardial fibers. Conversely, it can be said that the fibers are so optimally oriented, that their contraction causes LV deformation to in turn cause the appropriate increase in intra-LV pressure. Another important outcome of our analysis is the determination of the "LV torque vs twist angle" relationship, which has the potential to be employed as an index of contractility. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To achieve angiogenic interaction between VEGF165 and angiopoietin-1 (Ang-1) using a novel adenoviral bicistronic vector (Ad-Bic) encoding the two factors and delivered ex vivo using sex-mismatched human skeletal myoblasts. Methods and results: A myocardial infarction model was developed in 29 female pigs; randomised into four groups: DMEM (group-1, n =6); Adenovirus null (Ad-null) vector-myoblast (group-2, n =5); Ad-Ang-1 myoblast (group 3, n =7) and Ad-Bic-myoblast (group-4, n =11). Three weeks later, 5 ml DMEM without myoblasts or containing 3×108 myoblasts carrying lac-z gene and transduced with Ad-null, Ad-Ang-1 or Ad-Bic were injected intra-myocardially in and around the infarct. 2D-echocardiography and fluorescent microsphere studies 6- and 12-weeks post-treatment revealed significantly improved cardiac performance and regional blood flow in groups 3 and 4. Histological studies and Y-chromosome analysis revealed extensive survival of lac-z positive myoblasts staining positive for human proteins in the pig heart. ELISA, immunostaining and RT-PCR revealed that Ad-Bic transduced myoblasts concomitantly but transiently expressed hVEGF165 and Ang-1 both in vitro and in vivo. Double fluorescent immunostaining of the tissue sections for vWFactor-III and smooth muscle actin showed significantly higher vascular density of mature blood vessels per low power microscopic field in groups 3 and 4 at 6- and 12-weeks. Conclusion: Our combined approach led to enhanced angiogenesis with a greater percentage of functionally mature blood vessels in a porcine heart. [Copyright &y& Elsevier]

Highlights • An efficient deep learning model for the diagnosis of MI from the standard 12 lead ECG. • The proposed model can successfully distinguish between 10 different MI types. • Experiments with the public physiobank (PTB) ECG database. • Accuracy and sensitivity performance over 99.00% on all lead ECG signals. Abstract Myocardial infarction (MI), commonly known as heart attack, causes irreversible damage to heart muscles and even leads to death. Rapid and accurate diagnosis of MI is critical to avoid death. Blood tests and electrocardiogram (ECG) signals are used to diagnose acute MI. However, for an increase in blood enzyme values, a certain time must pass after the attack. This time lag may delay MI diagnosis. Hence, ECG diagnosis is still very important. Manual ECG interpretation requires expertise and is prone to inter-observer variability. Therefore, computer aided diagnosis may be useful in automatic detection of MI on ECG. In this study, a deep learning model with an end-to-end structure on the standard 12-lead ECG signal for the diagnosis of MI is proposed. For this purpose, the most commonly used technique, convolutional neural network (CNN) is used. Our trained CNN model with the proposed architecture yielded impressive accuracy and sensitivity performance over 99.00% for MI diagnosis on all ECG lead signals. Thus, the proposed model has the potential to provide high performance on MI detection which can be used in wearable technologies and intensive care units. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence, there is a need of computer aided techniques to be applied for the ECG signal analysis process. Going further, there is a need for incorporating this computerized software into the ECG equipment, so as to enable automated detection of MIs in clinics. Therefore, this paper proposes a novel method of automated detection and localization of MI by using ECG signal analysis. In our study, a total of 200 twelve lead ECG subjects (52 normal and 148 with MI) involving 611,405 beats (125,652 normal beats and 485,753 beats of MI ECG) are segmented from the 12 lead ECG signals. Firstly, ECG signal obtained from 12 ECG leads are subjected to discrete wavelet transform (DWT) up to four levels of decomposition. Then, 12 nonlinear features namely, approximate entropy ( E a x ), signal energy ( Ω x ), fuzzy entropy ( E f x ), Kolmogorov–Sinai entropy ( E k s x ), permutation entropy ( E p x ), Renyi entropy ( E r x ), Shannon entropy ( E s h x ), Tsallis entropy ( E t s x ), wavelet entropy ( E w x ), fractal dimension ( F D x ), Kolmogorov complexity ( C k x ), and largest Lyapunov exponent ( E L L E x ) are extracted from these DWT coefficients. The extracted features are then ranked based on the t value. Then these features are fed into the k -nearest neighbor (KNN) classifier one by one to get the highest classification performance by using minimum number of features. Our proposed method has achieved the highest average accuracy of 98.80%, sensitivity of 99.45% and specificity of 96.27% in classifying normal and MI ECG (two classes), by using 47 features obtained from lead 11 (V 5 ). We have also obtained the highest average accuracy of 98.74%, sensitivity of 99.55% and specificity of 99.16% in differentiating the 10 types of MI and normal ECG beats (11 class), by using 25 features obtained from lead 9 (V 3 ). In addition, our study results achieved an accuracy of 99.97% in locating inferior posterior infarction by using only lead 9 (V 3 ) ECG signal. Our proposed method can be used as an automated diagnostic tool for (i) the detection of different (10 types of) MI by using 12 lead ECG signal, and also (ii) to locate the MI by analyzing only one lead without the need to analyze other leads. Thus, our proposed algorithm and computerized system software (incorporated into the ECG equipment) can aid the physicians and clinicians in accurate and faster location of MIs, and thereby providing adequate time available for the requisite treatment decision. [ABSTRACT FROM AUTHOR]