An Automatic Coronary Microvascular Dysfunction Classification Method Based on Hybrid ECG Features and Expert Features.

OBJECTIVE: In recent years, the early diagnosis and treatment of coronary microvascular dysfunction (CMD) have become crucial for preventing coronary heart disease. This paper aims to develop a computer-assisted autonomous diagnosis method for CMD by using ECG features and expert features. APPROACH: Clinical electrocardiogram (ECG), myocardial contrast echocardiography (MCE), and coronary angiography (CAG) are used in our method. Firstly, morphological features, temporal features, and T-wave features of ECG are extracted by multi-channel residual network with BiLSTM (MCResnet-BiLSTM) model and the multi-source T-wave features (MTF) extraction model, respectively. And these features are fused to form ECG features. In addition, the CFR[Formula: see text] is calculated based on the parameters related to the MCE at rest and stress state, and the Angio-IMR is calculated based on CAG. The combination of CFR[Formula: see text] and Angio-IMR is termed as expert features. Furthermore, the hybrid features, fused from the ECG features and the expert features, are input into the multilayer perceptron to implement the identification of CMD. And the weighted sum of the soft maximum loss and center loss is used as the total loss function for training the classification model, which optimizes the classification ability of the model. RESULT: The proposed method achieved 93.36% accuracy, 94.46% specificity, 92.10% sensitivity, 95.89% precision, and 93.95% F1 score on the clinical dataset of the Second Affiliated Hospital of Zhejiang University. CONCLUSION: The proposed method accurately extracts global ECG features, combines them with expert features to obtain hybrid features, and uses weighted loss to significantly improve diagnostic accuracy. It provides a novel and practical method for the clinical diagnosis of CMD.

[Negative Poisson's Ratio Material Structure Based on 3D Printing and Its Application in Medical Field].

First of all, the overall framework of 3D printing is briefly introduced, including the basic principles of the additive manufacturing process, the classification and summary of the seven processes. Secondly, the common negative Poisson's ratio structure is introduced. Compared with the conventional structure, the negative Poisson's ratio structure has stronger energy absorption capacity, better fracture resistance and better indentation resistance, which are its advantages in printing manufacturing. Finally, 3D printing, the application of negative Poisson's ratio structure and the combination of the two are introduced from the different perspective of medical field, for example, the application of cardiovascular stent, biomedical material structure preparation, and lumbar disc implants. This paper suggests that the structural design of negative Poisson's ratio in 3D printing guides the development of new application directions in the medical field. Negative Poisson's ratio materials have a wide range of applications, not only in the medical field but also in mechanical equipment, automotive manufacturing, aerospace, and other high-tech industries.

Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features.

Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.

[Application Status and Prospect of Rehabilitation Assessment Technology].

Rehabilitation assessment is the basis and important part of rehabilitation diagnosis and treatment. At present, clinical evaluation is usually carried out by observation method and scale method. At the same time, researchers monitor patients' physical condition data through sensor system and other equipment as a supplement. The purpose of this study is to review the application and development of objective rehabilitation assessment technology in clinical practice, and to discuss its limitations and strategies to provide reference for related research.

Early diagnosis of coronary microvascular dysfunction by myocardial contrast stress echocardiography.

Coronary microvascular dysfunction (CMD) is one of the basic mechanisms of myocardial ischemia. Myocardial contrast echocardiography (MCE) is a bedside technique that utilises microbubbles which remain entirely within the intravascular space and denotes the status of microvascular perfusion within that region. Some pilot studies suggested that MCE may be used to diagnose CMD, but without further validation. This study is aimed to investigate the diagnostic performance of MCE for the evaluation of CMD. MCE was performed at rest and during adenosine triphosphate stress. ECG triggered real-time frames were acquired in the apical 4-chamber, 3-chamber, 2-chamber, and long-axis imaging planes. These images were imported into Narnar for further processing. Eighty-two participants with suspicion of coronary disease and absence of significant epicardial lesions were prospectively investigated. Thermodilution was used as the gold standard to diagnose CMD. CMD was present in 23 (28%) patients. Myocardial blood flow reserve (MBF) was assessed using MCE. CMD was defined as MBF reserve < 2. The MCE method had a high sensitivity (88.1%) and specificity (95.7%) in the diagnosis of CMD. There was strong agreement with thermodilution (Kappa coefficient was 0.727; 95% CI: 0.57-0.88, p < 0.001). However, the correlation coefficient (r = 0.376; p < 0.001) was not high.

[Research and Application of Smartphone-based Detection System to Immunoassay].

The emergence of smart phones has not only brought extensive changes to people's way of life, but also provided a new research platform for the expansion and application of science and technology. Technicians have combined immunoassay methods with smart phone sensing technology to develop a variety of smartphone-based biological sample analysis and detection sensing systems, promoting the application of immunoassay methods in POCT fields. In this review, we summarize the researches and applications of smart-phone in the field of immune analysis. According to the different sensors and detection objects, these applications can be divided into four aspects: camera-based spectrometer, camera-based enzyme reader, camera-based strip reader and spectrophotometer based on environmental light sensor. This study also briefly discusses some limitations of current smart phone application in immune analysis, and prospects the future applicability of smart phone sensing system.

Semantic segmentation method for myocardial contrast echocardiogram based on DeepLabV3+ deep learning architecture.

Myocardial contrast echocardiography (MCE) has been proposed as a method to assess myocardial perfusion for the detection of coronary artery diseases in a non-invasive way. As a critical step of automatic MCE perfusion quantification, myocardium segmentation from the MCE frames faces many challenges due to the low image quality and complex myocardial structure. In this paper, a deep learning semantic segmentation method is proposed based on a modified DeepLabV3+ structure with an atrous convolution and atrous spatial pyramid pooling module. The model was trained separately on three chamber views (apical two-chamber view, apical three-chamber view, and apical four-chamber view) on 100 patients' MCE sequences, divided by a proportion of 7:3 into training and testing datasets. The results evaluated by using the dice coefficient (0.84, 0.84, and 0.86 for three chamber views respectively) and Intersection over Union(0.74, 0.72 and 0.75 for three chamber views respectively) demonstrated the better performance of the proposed method compared to other state-of-the-art methods, including the original DeepLabV3+, PSPnet, and U-net. In addition, we conducted a trade-off comparison between model performance and complexity in different depths of the backbone convolution network, which illustrated model application feasibility.

[Research Progress on Enzyme-free Blood Glucose Sensor].

Blood glucose monitoring is of great significance to diabetic patients, and the development of rapid, accurate and real-time glucose detection technology has become a research hotspot nowadays. This study introduces the concept and classification of the enzyme-free glucose sensor, expounds enzymefree glucose sensor electrode characterization methods and the application progress of different materials in enzyme-free blood glucose sensors. Meanwhile, some problems of enzyme-free glucose sensor existing in the current research and its future application prospects also will be discussed.

Compressed sensing based dynamic MR image reconstruction by using 3D-total generalized variation and tensor decomposition: k-t TGV-TD.

PURPOSE: Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique to accelerate dynamic cardiac MR imaging (DCMRI). For DCMRI, the CS-MRI usually exploits image signal sparsity and low-rank property to reconstruct dynamic images from the undersampled k-space data. In this paper, a novel CS algorithm is investigated to improve dynamic cardiac MR image reconstruction quality under the condition of minimizing the k-space recording. METHODS: The sparse representation of 3D cardiac magnetic resonance data is implemented by synergistically integrating 3D total generalized variation (3D-TGV) algorithm and high order singular value decomposition (HOSVD) based Tensor Decomposition, termed k-t TGV-TD method. In the proposed method, the low rank structure of the 3D dynamic cardiac MR data is performed with the HOSVD method, and the localized image sparsity is achieved by the 3D-TGV method. Moreover, the Fast Composite Splitting Algorithm (FCSA) method, combining the variable splitting with operator splitting techniques, is employed to solve the low-rank and sparse problem. Two different cardiac MR datasets (cardiac perfusion and cine MR datasets) are used to evaluate the performance of the proposed method. RESULTS: Compared with the state-of-art methods, such as k-t SLR, 3D-TGV, HOSVD based tensor decomposition and low-rank plus sparse method, the proposed k-t TGV-TD method can offer improved reconstruction accuracy in terms of higher peak SNR (PSNR) and structural similarity index (SSIM). The proposed k-t TGV-TD method can achieve significantly better and stable reconstruction results than state-of-the-art methods in terms of both PSNR and SSIM, especially for cardiac perfusion MR dataset. CONCLUSIONS: This work proved that the k-t TGV-TD method was an effective sparse representation way for DCMRI, which was capable of significantly improving the reconstruction accuracy with different acceleration factors.

[Design and Application of Intelligent Management System of Artificial Airway Airbag Pressure in Intensive Care].

In order to solve the problem of continuous monitoring and automatic regulation of patient airbag pressure in intensive care unit, the study designed an intelligent management system of artificial airway airbag pressure. It can realize real-time monitoring and automatic control of airbag pressure. Its pressure data was sent to the PC in real time by the serial port. It can realize the display, store, review and analysis of pressure data. Its clinical application effect was discussed. Experiments showed that the system can monitor airbag pressure in real time and control the pressure to stabilize at 25~30 cmH2O. Compared with the control group, the experimental group had a statistically significant difference in the operation time of monitoring patients' airbag pressure, changes in airbag pressure, the instantaneous maximum value during nursing operation, and the number of aspiration and reflux cases. The clinical application of the system can reduce the workload of medical staff greatly, effectively reduce the number of patients with aspiration and reflux, reduce the incidence of ventilator pneumonia.

[Magnet Quench Factors in Ramping-up Procedure].

Ramping-up is the magnet current injection procedure which is under the control of resistance, voltage, current lead temperature, magnet pressure, temperature and so on. In this procedure, the factors related to the stability of the magnet such as, magnet temperature, pressure and currents are constantly changing. This procedure is the main step which the magnet-quench occurs in. This study uses the data collected during 7 years and SIMENS MRI ramping-up theory, in order to help engineers understand the key factors to reduce the magnet quench during the ramping up procedure.

[The SAR Problem and Trouble Shooting Strategy].

The modifications of slices, flip angle, SAR mode and TR time are required when the SAR exceeds the limits. The scan time and image quality are affected by those. This study analyzes the SAR from the basic side. With the principle diagram of SIEMENS 1.5 T AVANTO and 3.0 T VERIO MRIS, the trouble shooting procedure of SAR problem is reached both in application and problem sides.

Increased intrinsic default-mode network activity as a compensatory mechanism in aMCI: a resting-state functional connectivity MRI study.

Numerous studies have investigated the differences in the mean functional connectivity (FC) strength between amnestic mild cognitive impairment (aMCI) patients and normal subjects using resting-state functional magnetic resonance imaging. However, whether the mean FC is increased, decreased or unchanged in aMCI patients compared to normal controls remains unclear. Two factors might lead to inconsistent results: the determination of regions of interest and the reliability of the FC.We explored differences in FC and the degree centrality (Dc) constructed by the bootstrap method, between and within networks (default-mode network (DN), frontoparietal control network (CN), dorsal attention network (AN)), and resulting from a hierarchical-clustering algorithm.The mean FC within the DN and CN was significantly increased (P < 0.05, uncorrected) in patients. Significant increases (P < 0.05, uncorrected) in the mean FC were found in patients between DN and CN and between DN and AN. Five pairs of FC (false discovery rate corrected) and the Dc of six regions (Bonferroni corrected) displayed a significant increase in patients. Lower cognitive ability was significantly associated with a greater increase in the Dc of the left superior temporal sulcus.Our results demonstrate that the early dysfunctions in aMCI disease are mainly compensatory impairments.

[Optimization of Soft Endoscope Cleaning and Disinfection Equipment].

According to the national soft endoscope cleaning standard and the difference between the previous and latest version, the origin decontamination equipment is upgraded. Sensory control monitoring results in the last year shows that the average cleaning time for a single endoscope has been reduced from 20 min 55 s to 17 min 16 s. The number of bacterial colony has been reduced from 6.8 to 3.1. The pass rate of sensory control monitoring keeps steady. The upgraded decontamination equipment not only meets the requirements of the latest version of the standard,but also improves the efficiency and effectiveness of electronic endoscope disinfection.

[Research Progress of 3D Printing Technology in Medical Field].

3D printing technology is a kind of manufacturing technology for increasing materials. Based on its advantages of rapid prototyping, digitalization and customization, it can effectively meet the needs of personalized and accurate medical treatment, and has been widely studied and applied in the medical field. This paper introduces the principle, technology and materials of 3D printing technology, and describes the research and application status of the technology in medical field. Meanwhile, some problems of 3D printing technology existing in the current development and its future application prospects also will be discussed.

HF-SENSE: an improved partially parallel imaging using a high-pass filter.

BACKGROUND: One of the major limitations of MRI is its slow acquisition speed. To accelerate data acquisition, partially parallel imaging (PPI) methods have been widely used in clinical applications such as sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE is a popular image-domain partially parallel imaging method, which suffers from residual aliasing artifacts when the reduction factor goes higher. Undersampling the k-space data and then reconstruct images with artificial sparsity is an efficient way to accelerate data acquisition. By exploiting artificial sparsity with a high-pass filter, an improved SENSE method is proposed in this work, termed high-pass filtered SENSE (HF-SENSE). METHODS: First, a high-pass filter was applied to the raw k-space data, the result of which was used as the inputs of sensitivity estimation and undersampling process. Second, the adaptive array coil combination method was adopted to calculate sensitivity maps on a block-by-block basis. Third, Tikhonov regularized SENSE was then used to reconstruct magnetic resonance images. Fourth, the reconstructed images were transformed into k-space data, which was filtered with the corresponding inverse filter. RESULTS: Both simulation and in vivo experiments demonstrate that HF-SENSE method significantly reduces noise level of the reconstructed images compared with SENSE. Furthermore, it is found that HF-SENSE can achieve lower normalized root-mean-square error value than SENSE. CONCLUSIONS: The proposed method explores artificial sparsity with a high-pass filter. Experiments demonstrate that the proposed HF-SENSE method can improve the image quality of SENSE reconstruction. The high-pass filter parameters can be predefined. With this image reconstruction method, high acceleration factors can be achieved, which will improve the clinical applicability of SENSE. This retrospective study (HF-SENSE: an improved partially parallel imaging using a high-pass filter) was approved by Institute Review Board of 2nd Affiliated Hospital of Zhejiang University (ethical approval number 2018-314). Participant for all images have informed consent that he knew the risks and agreed to participate in the research.

Intravenous aminophylline provides no additional renal protection in patient with severe atherosclerotic renal artery stenosis treated by delicate percutaneous renal intervention.

BACKGROUND: Clinical trials on contrast-induced nephropathy (CIN) prevention with different approaches generated various results. In this study, we investigated whether intravenous aminophylline preceding percutaneous transluminal renal artery stenting (PTRS) might provide better renal protection. METHODS: Patients with severe atherosclerotic renal artery stenosis and undergoing PTRS were prospectively studied. Intravenous aminophylline 250 mg was administered 30 min before PTRS. RESULTS: The aminophylline group included 15 patients (mean age, 68+/-4 years) and the case-matched control group consisted of another 15 patients (mean age, 71+/-2 years). After a mean follow-up of 5-6 months, both groups showed similar serum creatinine (1.7+/-0.2 vs. 1.6+/-0.1 mg/dl, P=NS), BUN (20.0+/-3.0 vs. 24.0+/-3.0 mg/dl, P=NS), fall in systolic (-15+/-13 vs. -11+/-3 mm Hg, P=NS) and diastolic BP (-2+/-4 vs. -6+/-5 mm Hg, P=NS). The incidence of post-operative renal deterioration was 6.7% in the study group and none in the control group. CONCLUSION: Pre-treatment intravenous aminophylline provides no additional renal protective effect in delicately practiced PTRS.