Analysis and recognition of post-exercise cardiac state based on heart sound features and cardiac troponin I.

PURPOSE: Excessive intensity exercises can bring irreversible damage to the heart. We explore whether heart sounds can evaluate cardiac function after high-intensity exercise and hope to prevent overtraining through the changes of heart sound in future training. METHODS: The study population consisted of 25 male athletes and 24 female athletes. All subjects were healthy and had no history of cardiovascular disease or family history of cardiovascular disease. The subjects were required to do high-intensity exercise for 3 days, with their blood sample and heart sound (HS) signals being collected and analysed before and after exercise. We then developed a Kernel extreme learning machine (KELM) model that can distinguish the state of heart by using the pre- and post-exercise data. RESULTS: There was no significant change in serum cardiac troponin I after 3 days of load cross-country running, which indicates that there was no myocardial injury after the race. The statistical analysis of time-domain characteristics and multi-fractal characteristic parameters of HS showed that the cardiac reserve capacity of the subjects was enhanced after the cross-country running, and the KELM is an effective classifier to recognize HS and the state of the heart after exercise. CONCLUSION: Through the results, we can draw the conclusion that this intensity of exercise will not cause profound damage to the athlete's heart. The findings of this study are of great significance for evaluating the condition of the heart with the proposed index of heart sound and prevention of excessive training that causes damage to the heart.

Copper-based metal-organic framework impedes triple-negative breast cancer metastasis via local estrogen deprivation and platelets blockade.

Metastasis is one of the main causes of failure in the treatment of triple-negative breast cancer (TNBC). Abnormally estrogen level and activated platelets are the key driving forces for TNBC metastasis. Herein, an "ion/gas" bioactive nanogenerator (termed as IGBN), comprising a copper-based MOF and loaded cisplatin-arginine (Pt-Arg) prodrug is developed for metastasis-promoting tumor microenvironment reprogramming and TNBC therapy. The copper-based MOF not only serves as a drug carrier, but also specifically produces Cu2+ in tumors, which catalytic oxidizing estrogen to reduce estrogen levels in situ. Meanwhile, the rationally designed Pt-Arg prodrug reduced into cisplatin to significantly promote the generation of H2O2 in the tumor, then permitting self-augmented cascade NO gas generation by oxidizing Arg through a H2O2 self-supplied way, thus blocking platelet activation in tumor. We clarified that IGBN inhibited TNBC metastasis through local estrogen deprivation and platelets blockade, affording 88.4% inhibition of pulmonary metastasis in a 4T1 mammary adenocarcinoma model. Notably, the locally copper ion interference, NO gas therapy and cisplatin chemotherapy together resulted in an enhanced therapeutic efficacy in primary tumor ablation without significant toxicity. This "ion/gas" bioactive nanogenerator offers a robust and safe strategy for TNBC therapy.

Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network.

BACKGROUND AND OBJECTIVE: Moderate exercise contributes to good health. However, excessive exercise may lead to cardiac fatigue, myocardial damage and even exercise sudden death. Monitoring the heart health has important implication to prevent exercise sudden death. Diagnosis methods such as electrocardiogram, echocardiogram, blood pressure and histological analysis have shown that arrhythmia and left ventricular fibrosis are early warning symptoms of exercise sudden death. Heart sounds (HS) can reflect the changes of cardiac valve, cardiac blood flow and myocardial function. Deep learning has drawn wide attention because of its ability to recognize disease. Therefore, a deep learning method combined with HS was proposed to predict exercise sudden death in New Zealand rabbits. The objective is to develop a method to predict exercise sudden death in New Zealand rabbits. METHODS: This paper proposed a method to predict exercise sudden death in New Zealand rabbits based on convolutional neural network (CNN) and gated recurrent unit (GRU). The weight-bearing exhaustive swimming experiment was conducted to obtain the HS of exercise sudden death and surviving New Zealand rabbits (n = 11/10) at four different time points. Then, the improved Viola integral method and double threshold method were employed to segment HS signals. The segmented HS frames at different time points were taken as the input of a combined CNN and GRU called CNN-GRU network to complete the prediction of exercise sudden death. RESULTS: In order to evaluate the performance of proposed network, CNN and GRU were used for comparison. When the fourth time point segmented HS frames were taken as input, the result shows that the proposed network has better performance with an accuracy of 89.57%, a sensitivity of 89.38% and a specificity of 92.20%. In addition, the segmented HS frames at different time points were input into CNN-GRU network, and the result shows that with the progress of the experiment, the prediction accuracy of exercise sudden death in New Zealand rabbits increased from 50.98 to 89.57%. CONCLUSION: The proposed network shows good performance in classifying HS, which proves the feasibility of deep learning in exploring exercise sudden death. Further, it may have important implications in helping humans explore exercise sudden death.

[Anti-interference hemoglobin analysis system by high performance liquid chromatography].

High performance liquid chromatography (HPLC) is currently the mainstream technology for detecting hemoglobin. Glycated hemoglobin (HbA1c) is a gold indicator for diagnosing diabetes, however, the accuracy of HbA1c test is affected by thalassemia factor hemoglobin F (HbF)/hemoglobin A2 (HbA2) and variant hemoglobin during HPLC analysis. In this study, a new anti-interference hemoglobin analysis system of HPLC is proposed. In this system, the high-pressure three-gradient elution method was improved, and the particle size and sieve plate aperture in the high-pressure chromatography column and the structure of the double-plunger reciprocating series high-pressure pump were optimized. The system could diagnose both HbA1c and thalassemia factor HbF/HbA2 and variant hemoglobin, and the performance of the system was anti-interference and stable. It is expected to achieve industrialization. In this study, the HbA1c and thalassemia factor HbF/HbA2 detection performance was compared between this system and the world's first-line brand products such as Tosoh G8, Bio-Rad Ⅶ and D10 glycosylated hemoglobin analysis system. The results showed that the linear correlation between this system and the world-class system was good. The system is the first domestic hemoglobin analysis system by HPLC for screening of HbA1c and thalassemia factor HbF/HbA2 rapidly and accurately.

Structural and biochemical insights into the catalytic mechanisms of two insect chitin deacetylases of the carbohydrate esterase 4 family.

Insect chitin deacetylases (CDAs) catalyze the removal of acetyl groups from chitin and modify this polymer during its synthesis and reorganization. CDAs are essential for insect survival and therefore represent promising targets for insecticide development. However, the structural and biochemical characteristics of insect CDAs have remained elusive. Here, we report the crystal structures of two insect CDAs from the silk moth Bombyx mori: BmCDA1, which may function in cuticle modification, and BmCDA8, which may act in modifying peritrophic membranes in the midgut. Both enzymes belong to the carbohydrate esterase 4 (CE4) family. Comparing their overall structures at 1.98-2.4 Å resolution with those from well-studied microbial CDAs, we found that two unique loop regions in BmCDA1 and BmCDA8 contribute to the distinct architecture of their substrate-binding clefts. These comparisons revealed that both BmCDA1 and BmCDA8 possess a much longer and wider substrate-binding cleft with a very open active site in the center than the microbial CDAs, including VcCDA from Vibrio cholerae and ArCE4A from Arthrobacter species AW19M34-1. Biochemical analyses indicated that BmCDA8 is an active enzyme that requires its substrates to occupy subsites 0, +1, and +2 for catalysis. In contrast, BmCDA1 also required accessory proteins for catalysis. To the best of our knowledge, our work is the first to unveil the structural and biochemical features of insect proteins belonging to the CE4 family.

Gated recurrent unit-based heart sound analysis for heart failure screening.

BACKGROUND: Heart failure (HF) is a type of cardiovascular disease caused by abnormal cardiac structure and function. Early screening of HF has important implication for treatment in a timely manner. Heart sound (HS) conveys relevant information related to HF; this study is therefore based on the analysis of HS signals. The objective is to develop an efficient tool to identify subjects of normal, HF with preserved ejection fraction and HF with reduced ejection fraction automatically. METHODS: We proposed a novel HF screening framework based on gated recurrent unit (GRU) model in this study. The logistic regression-based hidden semi-Markov model was adopted to segment HS frames. Normalized frames were taken as the input of the proposed model which can automatically learn the deep features and complete the HF screening without de-nosing and hand-crafted feature extraction. RESULTS: To evaluate the performance of proposed model, three methods are used for comparison. The results show that the GRU model gives a satisfactory performance with average accuracy of 98.82%, which is better than other comparison models. CONCLUSION: The proposed GRU model can learn features from HS directly, which means it can be independent of expert knowledge. In addition, the good performance demonstrates the effectiveness of HS analysis for HF early screening.

An Automatic Approach Using ELM Classifier for HFpEF Identification Based on Heart Sound Characteristics.

Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical syndrome. For the purpose of assisting HFpEF diagnosis, a non-invasive method using extreme learning machine and heart sound (HS) characteristics was provided in this paper. Firstly, the improved wavelet denoising method was used for signal preprocessing. Then, the logistic regression based hidden semi-Markov model algorithm was utilized to locate the boundary of the first HS and the second HS, therefore, the ratio of diastolic to systolic duration can be calculated. Eleven features were extracted based on multifractal detrended fluctuation analysis to analyze the differences of multifractal behavior of HS between healthy people and HFpEF patients. Afterwards, the statistical analysis was implemented on the extracted HS characteristics to generate the diagnostic feature set. Finally, the extreme learning machine was applied for HFpEF identification by the comparison of performances with support vector machine. The result shows an accuracy of 96.32%, a sensitivity of 95.48% and a specificity of 97.10%, which demonstrates the effectiveness of HS for HFpEF diagnosis.

Small-size effect on wrinkle and fracture of monolayer graphene subjected to in-plane shear.

Controlling surface patterns are useful in a wide range of applications including flexible electronics, biological templates, microelectromechanical systems and device fabrication. The present paper investigates the wrinkling and fracture of graphene subjected to in-plane shear. It is found that the size of a graphene sheet has significant effect on the wrinkle and fracture based on both molecular dynamics simulation and nonlocal plate theory. The analytical expressions for wrinkle amplitude and wavelength are deduced. The nonlocal parameter of nonlocal plate theory is evaluated. Furthermore, the higher aspect ratio has enhanced the wrinkle resistance and shear strength of graphene. Temperature and chirality have insignificant impact on the wrinkling, but significantly influence the fracture of the graphene sheet. This work is expected to provide a better understanding of the mechanism of nanometer scale wrinkles.

Intrinsic Negative Poisson's Ratio for Single-Layer Graphene.

Negative Poisson's ratio (NPR) materials have drawn significant interest because the enhanced toughness, shear resistance, and vibration absorption that typically are seen in auxetic materials may enable a range of novel applications. In this work, we report that single-layer graphene exhibits an intrinsic NPR, which is robust and independent of its size and temperature. The NPR arises due to the interplay between two intrinsic deformation pathways (one with positive Poisson's ratio, the other with NPR), which correspond to the bond stretching and angle bending interactions in graphene. We propose an energy-based deformation pathway criteria, which predicts that the pathway with NPR has lower energy and thus becomes the dominant deformation mode when graphene is stretched by a strain above 6%, resulting in the NPR phenomenon.

Negative Thermophoresis in Concentric Carbon Nanotube Nanodevices.

Positive and negative thermophoresis in fluids has found widespread applications from mass transport to molecule manipulation. In solids, although positive thermophoresis has been recently discovered in both theoretical and experimental studies, negative thermophoresis has never been reported. Here we reveal via molecular dynamics simulations that negative thermophoresis does exist in solids. We consider the motion of a single walled carbon nanotube nested inside of two separate outer tubes held at different temperatures. It is found that a sufficiently long inner tube will undergo negative thermophoresis, whereas positive thermophoresis is favorable for a relatively short inner tube. Mechanisms for the observed positive thermophoresis and negative thermophoresis are shown to be totally different. In positive thermophoresis, the driving force comes mainly from the thermally induced edge force and the interlayer attraction force, whereas the driving force for negative thermophoresis is mainly due to the thermal gradient force. These findings have enriched our knowledge of the fundamental driving mechanisms for thermophoresis in solids and may stimulate its further applications in nanotechnology.

Nanoscale directional motion towards regions of stiffness.

How to induce nanoscale directional motion via some intrinsic mechanisms pertaining to a nanosystem remains a challenge in nanotechnology. Here we show via molecular dynamics simulations that there exists a fundamental driving force for a nanoscale object to move from a region of lower stiffness toward one of higher stiffness on a substrate. Such nanoscale directional motion is induced by the difference in effective van der Waals potential energy due to the variation in stiffness of the substrate; i.e., all other conditions being equal, a nanoscale object on a stiffer substrate has lower van der Waals potential energy. This fundamental law of nanoscale directional motion could lead to promising routes for nanoscale actuation and energy conversion.

Bioleaching mechanism of Zn, Pb, In, Ag, Cd and As from Pb/Zn smelting slag by autotrophic bacteria.

A few studies have focused on release of valuable/toxic metals from Pb/Zn smelting slag by heterotrophic bioleaching using expensive yeast extract as an energy source. The high leaching cost greatly limits the practical potential of the method. In this work, autotrophic bioleaching using cheap sulfur or/and pyrite as energy matter was firstly applied to tackle the smelting slag and the bioleaching mechanisms were explained. The results indicated autotrophic bioleaching can solubilize valuable/toxic metals from slag, yielding maximum extraction efficiencies of 90% for Zn, 86% for Cd and 71% for In, although the extraction efficiencies of Pb, As and Ag were poor. The bioleaching performance of Zn, Cd and Pb was independent of leaching system, and leaching mechanism was acid dissolution. A maximum efficiency of 25% for As was achieved by acid dissolution in sulfursulfur oxidizing bacteria (S-SOB), but the formation of FeAsO4 reduced extraction efficiency in mixed energy source - mixed culture (MS-MC). Combined works of acid dissolution and Fe(3+) oxidation in MS-MC was responsible for the highest extraction efficiency of 71% for In. Ag was present in the slag as refractory AgPb4(AsO4)3 and AgFe2S3, so extraction did not occur.

Boundary attention with multi-task consistency constraints for semi-supervised 2D echocardiography segmentation.

The 2D echocardiography semantic automatic segmentation technique is important in clinical applications for cardiac function assessment and diagnosis of cardiac diseases. However, automatic segmentation of 2D echocardiograms also faces the problems of loss of image boundary information, loss of image localization information, and limitations in data acquisition and annotation. To address these issues, this paper proposes a semi-supervised echocardiography segmentation method. It consists of two models: (1) a boundary attention transformer net (BATNet) and (2) a multi-task level semi-supervised model with consistency constraints on boundary features (semi-BATNet). BATNet is able to capture the location and spatial information of the input feature maps by using the self-attention mechanism. The multi-task level semi-supervised model with boundary feature consistency constraints (semi-BATNet) encourages consistent predictions of boundary features at different scales from the student and teacher networks to calculate the multi-scale consistency loss for unlabeled data. The proposed semi-BATNet was extensively evaluated on the dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS) and self-collected echocardiography dataset from the First Affiliated Hospital of Chongqing Medical University. Experimental results on the CAMUS dataset showed that when only 25% of the images are labeled, the proposed method greatly improved the segmentation performance by utilizing unlabeled images, and it also outperformed five state-of-the-art semi-supervised segmentation methods. Moreover, when only 50% of the images labeled, semi-BATNet achieved the Dice coefficient values of 0.936, the Jaccard similarity of 0.881 on self-collected echocardiography dataset. Semi-BATNet can complete a more accurate segmentation of cardiac structures in 2D echocardiograms, indicating that it has the potential to accurately and efficiently assist cardiologists.

Detection of Coronary Artery Disease Based on Clinical Phonocardiogram and Multiscale Attention Convolutional Compression Network.

Heart sound is an important physiological signal that contains rich pathological information related with coronary stenosis. Thus, some machine learning methods are developed to detect coronary artery disease (CAD) based on phonocardiogram (PCG). However, current methods lack sufficient clinical dataset and fail to achieve efficient feature utilization. Besides, the methods require complex processing steps including empirical feature extraction and classifier design. To achieve efficient CAD detection, we propose the multiscale attention convolutional compression network (MACCN) based on clinical PCG dataset. Firstly, PCG dataset including 102 CAD subjects and 82 non-CAD subjects was established. Then, a multiscale convolution structure was developed to catch comprehensive heart sound features and a channel attention module was developed to enhance key features in multiscale attention convolutional block (MACB). Finally, a separate downsampling block was proposed to reduce feature losses. MACCN combining the blocks can automatically extract features without empirical and manual feature selection. It obtains good classification results with accuracy 93.43%, sensitivity 93.44%, precision 93.48%, and F1 score 93.42%. The study implies that MACCN performs effective PCG feature mining aiming for CAD detection. Further, it integrates feature extraction and classification and provides a simplified PCG processing case.

[Research on biometric method of heart sound signal based on GMM].

OBJECTIVE: Extraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal. METHODS: Firstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm. RESULTS: The comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM. CONCLUSION: Those results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.

Heart function grading evaluation based on heart sounds and convolutional neural networks.

Accurate and rapid cardiac function assessment is critical for disease diagnosis and treatment strategy. However, the current cardiac function assessment methods have their adaptability and limitations. Heart sounds (HS) can reflect changes in heart function. Therefore, HS signals were proposed to assess cardiac function, and a specially designed pruning convolutional neural network (CNN) was applied to recognize subjects' cardiac function at different levels in this paper. Firstly, the adaptive wavelet denoising algorithm and logistic regression based hidden semi-Markov model were utilized for signal denoising and segmentation. Then, the continuous wavelet transform (CWT) was employed to convert the preprocessed HS signals into spectra as input to the convolutional neural network, which can extract features automatically. Finally, the proposed method was compared with AlexNet, Resnet50, Xception, GhostNet and EfficientNet to verify the superiority of the proposed method. Through comprehensive comparison, the proposed approach achieves the best classification performance with an accuracy of 94.34%. The study indicates HS analysis is a non-invasive and effective method for cardiac function classification, which has broad research prospects.

Phonocardiogram transfer learning-based CatBoost model for diastolic dysfunction identification using multiple domain-specific deep feature fusion.

Left ventricular diastolic dyfunction detection is particularly important in cardiac function screening. This paper proposed a phonocardiogram (PCG) transfer learning-based CatBoost model to detect diastolic dysfunction noninvasively. The Short-Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCCs), S-transform and gammatonegram were utilized to perform four different representations of spectrograms for learning the representative patterns of PCG signals in two-dimensional image modality. Then, four pre-trained convolutional neural networks (CNNs) such as VGG16, Xception, ResNet50 and InceptionResNetv2 were employed to extract multiple domain-specific deep features from PCG spectrograms using transfer learning, respectively. Further, principal component analysis and linear discriminant analysis (LDA) were applied to different feature subsets, respectively, and then these different selected features are fused and fed into CatBoost for classification and performance comparison. Finally, three typical machine learning classifiers such as multilayer perceptron, support vector machine and random forest were employed to compared with CatBoost. The hyperparameter optimization of the investigated models was determined through grid search. The visualized result of the global feature importance showed that deep features extracted from gammatonegram by ResNet50 contributed most to classification. Overall, the proposed multiple domain-specific feature fusion based CatBoost model with LDA achieved the best performance with an area under the curve of 0.911, accuracy of 0.882, sensitivity of 0.821, specificity of 0.927, F1-score of 0.892 on the testing set. The PCG transfer learning-based model developed in this study could aid in diastolic dysfunction detection and could contribute to non-invasive evaluation of diastolic function.

[Analysis of the heart sound with arrhythmia based on nonlinear chaos theory].

In this paper, a new method based on the nonlinear chaos theory was proposed to study the arrhythmia with the combination of the correlation dimension and largest Lyapunov exponent, through computing and analyzing these two parameters of 30 cases normal heart sound and 30 cases with arrhythmia. The results showed that the two parameters of the heart sounds with arrhythmia were higher than those with the normal, and there was significant difference between these two kinds of heart sounds. That is probably due to the irregularity of the arrhythmia which causes the decrease of predictability, and it's more complex than the normal heart sound. Therefore, the correlation dimension and the largest Lyapunov exponent can be used to analyze the arrhythmia and for its feature extraction.

An automatic approach for heart failure typing based on heart sounds and convolutional recurrent neural networks.

Heart failure (HF) is a complex clinical syndrome that poses a major hazard to human health. Patients with different types of HF have great differences in pathogenesis and treatment options. Therefore, HF typing is of great significance for timely treatment of patients. In this paper, we proposed an automatic approach for HF typing based on heart sounds (HS) and convolutional recurrent neural networks, which provides a new non-invasive and convenient way for HF typing. Firstly, the collected HS signals were preprocessed with adaptive wavelet denoising. Then, the logistic regression based hidden semi-Markov model was utilized to segment HS frames. For the distinction between normal subjects and the HF patients with preserved ejection fraction or reduced ejection fraction, a model based on convolutional neural network and recurrent neural network was built. The model can automatically learn the spatial and temporal characteristics of HS signals. The results show that the proposed model achieved a superior performance with an accuracy of 97.64%. This study suggests the proposed method could be a useful tool for HF recognition and as a supplement for HF typing.