Doppler Radar Sensor-Based Fall Detection Using a Convolutional Bidirectional Long Short-Term Memory Model.

Falls among the elderly are a common and serious health risk that can lead to physical injuries and other complications. To promptly detect and respond to fall events, radar-based fall detection systems have gained widespread attention. In this paper, a deep learning model is proposed based on the frequency spectrum of radar signals, called the convolutional bidirectional long short-term memory (CB-LSTM) model. The introduction of the CB-LSTM model enables the fall detection system to capture both temporal sequential and spatial features simultaneously, thereby enhancing the accuracy and reliability of the detection. Extensive comparison experiments demonstrate that our model achieves an accuracy of 98.83% in detecting falls, surpassing other relevant methods currently available. In summary, this study provides effective technical support using the frequency spectrum and deep learning methods to monitor falls among the elderly through the design and experimental validation of a radar-based fall detection system, which has great potential for improving quality of life for the elderly and providing timely rescue measures.

Coronary artery segmentation in CCTA images based on multi-scale feature learning.

BACKGROUND: Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy. OBJECTIVE: A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries. METHODS: The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance. RESULTS: The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets. CONCLUSION: Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine.

Relationship between epicardial fat volume on cardiac CT and atherosclerosis severity in three-vessel coronary artery disease: a single-center cross-sectional study.

BACKGROUND: The ideal treatment strategy for stable three-vessel coronary artery disease (CAD) patients are difficult to determine and for patients undergoing conservative treatment, imaging evidence of coronary atherosclerotic severity progression remains limited. Epicardial fat volume (EFV) on coronary CT angiography (CCTA) has been considered to be associated with coronary atherosclerosis. Therefore, this study aims to evaluate the relationship between EFV level and coronary atherosclerosis severity in three-vessel CAD. METHODS: This retrospective study enrolled 252 consecutive patients with three-vessel CAD and 252 normal control group participants who underwent CCTA between January 2018 and December 2019. A semi-automatic method was developed for EFV quantification on CCTA images, standardized by body surface area. Coronary atherosclerosis severity was evaluated and scored by the number of coronary arteries with ≥ 50% stenosis on coronary angiography. Patients were subdivided into groups on the basis of lesion severity: mild (score = 3 vessels, n = 85), moderate (3.5 vessels ≤ score < 4 vessels, n = 82), and severe (4 vessels ≤ score ≤ 7 vessels, n = 85). The independent sample t-test, analysis of variance, and logistic regression analysis were used to evaluate the associations between EFV level and severity of coronary atherosclerosis. RESULTS: Compared with normal controls, three-vessel CAD patients had significantly higher EFV level (65 ± 22 mL/m2 vs. 48 ± 19 mL/m2; P < 0.001). In patients with three-vessel CAD, there was a progressive decline in EFV level as the score of coronary atherosclerosis severity increased, especially in those patients with a body mass index (BMI) ≥ 25 kg/m2 (75 ± 21 mL/m2 vs. 72 ± 22 mL/m2 vs. 62 ± 17 mL/m2; P < 0.05). Multivariable regression analysis showed that both BMI (OR 3.40, 95% CI 2.00-5.78, P < 0.001) and the score of coronary atherosclerosis severity (OR 0.49, 95% CI 0.26-0.93, P < 0.05) were independently related to the change of EFV level. CONCLUSION: Three-vessel CAD patients do have higher EFV level than the normal controls. While, there may be an inverse relationship between EFV level and the severity of coronary atherosclerosis in patients with three-vessel CAD.

Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals.

Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin's Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.

[Quantitative analysis of the effect of contact pressure on the reflection characteristics of radial pressure wave].

The radial artery pulse wave contains a wealth of physiological and pathological information about the human body, and non-invasive studies of the radial artery pulse wave can assess arterial vascular elasticity in different age groups.The piezoelectric pulse wave transducers were used to non-invasively acquire radial artery pulse waves at different contact pressures in young and middle-aged and elderly populations. The radial artery waveforms were decomposed using a triangular blood flow model fitting method to obtain forward and reflected waves and calculate reflection parameters. Finally a correlation analysis and regression analysis of the contact pressure Psensor with the reflection parameters was carried out. The results showed that the reflection parameters RM, RI and Rd had a strong negative correlation with Psensor in both types of subjects, and the correlation coefficients and slopes of the regression curves were significantly different between the two types of subjects (P<0.05). Based on the results of this study, excessive contact pressure on the transducer should be avoided when detecting radial artery reflection waves in clinical practice. The results also show that the magnitude of the slope of the regression curve between the reflection parameters and the transducer contact pressure may be a potentially useful indicator for quantifying the elastic properties of the vessel.

Clinical outcomes of nicorandil administration in patients with acute ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: Primary percutaneous coronary intervention is the treatment of choice in ST-segment elevation myocardial infarction and no-reflow phenomenon is still an unsolved problem. METHODS: We searched PubMed, EmBase, and Cochrane Central Register of Controlled Trials for relevant randomized controlled trials. The primary endpoint was the incidence of major adverse cardiac events and the secondary endpoint was the incidences of no-reflow phenomenon and complete resolution of ST-segment elevation. RESULTS: Eighteen randomized controlled trials were enrolled. Nicorandil significantly reduced the incidence of no-reflow phenomenon (OR, 0.46; 95% CI, 0.36-0.59; P < 0.001; I2 = 0%) and major adverse cardiac events (OR, 0.42; 95% CI, 0.27-0.64; P < 0.001; I2 = 52%). For every single outcome of major adverse cardiac events, only heart failure and ventricular arrhythmia were significantly improved with no heterogeneity (OR, 0.36; 95% CI, 0.23-0.57, P < 0.001; OR, 0.43; 95% CI, 0.31-0.60, P < 0.001 respectively). A combination of intracoronary and intravenous nicorandil administration significantly reduced the incidence of major adverse cardiac events with no heterogeneity (OR, 0.24; 95% CI, 0.13-0.43, P < 0.001; I2 = 0%), while a single intravenous administration could not (OR, 0.66; 95% CI, 0.40-1.06, P = 0.09; I2 = 52%). CONCLUSIONS: Nicorandil can significantly improve no-reflow phenomenon and major adverse cardiac events in patients undergoing primary percutaneous coronary intervention. The beneficial effects on major adverse cardiac events might be driven by the improvements of heart failure and ventricular arrhythmia. A combination of intracoronary and intravenous administration might be an optimal usage of nicorandil.

Heat transfer analysis of blood perfusion in diabetic rats using a genetic algorithm.

Diabetes is frequently associated with structural and functional impairment of the microcirculation. Blood perfusion is an important indicator of both physiological and pathological conditions of the microcirculation. Given that temperature is closely related to blood perfusion and is more easily measured, blood perfusion can be estimated from variations in skin temperature using an inverse method. The aim of this paper was to develop a thermal analysis method for estimation of blood perfusion and apply it in the assessment of skin blood perfusion in diabetic rats. First, diabetes was induced in the rat models of the experimental group. Skin temperature from the rats left hind paws was measured during a 10-min local heating period followed by a 15-min cooling period. A simple one-dimensional heat transfer model, including an arteriolar vessel node, was used to describe the skin heat transfer process. The blood perfusion of the arteriole was estimated by correlating the calculated skin temperature with known experimental temperatures using a genetic algorithm. The results indicated that the average blood perfusion in the control group was higher during local heating and decreased faster during the cooling period, showing dynamic responses to the thermal stimuli. In contrast, the blood perfusion of diabetic rats was reduced compared with that of the control rats during the heating phase and the rate of decrease in perfusion during the cooling stage was similarly reduced, implying a slower response to thermal stimulation in these rats. It is interesting to note that diabetic rats fed a normal diet showed a similar blood perfusion pattern to that in the control rats, implying that diet may be important in the treatment of diabetes-associated microvascular dysfunction.

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module.

Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessary for administrators to identify the people that do not wear hardhats and send out alarms to them. As manual inspection is labor-intensive and expensive, it is ideal to handle this issue by a real-time automatic detector. As such, in this paper, we present an end-to-end convolutional neural network to solve the problem of detecting if workers are wearing hardhats. The proposed method focuses on localizing a person's head and deciding whether they are wearing a hardhat. The MobileNet model is employed as the backbone network, which allows the detector to run in real time. A top-down module is leveraged to enhance the feature-extraction process. Finally, heads with and without hardhats are detected on multi-scale features using a residual-block-based prediction module. Experimental results on a dataset that we have established show that the proposed method could produce an average precision of 87.4%/89.4% at 62 frames per second for detecting people without/with a hardhat worn on the head.

Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal.

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

Highly efficient preparation of active S-phenyl-L-cysteine with tryptophan synthase using a chemoenzymatic method.

BACKGROUND: S-Phenyl-L-cysteine is regarded as having potential applicability as an antiretroviral/protease inhibitor for human immunodeficiency virus (HIV). In the present study, optically active S-phenyl-L-cysteine was prepared in a highly efficient manner from inexpensive bromobenzene using tryptophan synthase through a chemoenzymatic method. RESULTS: The chemoenzymatic method used a four-step reaction sequence. The process started with the reaction of magnesium and bromobenzene, followed by a Grignard reaction, and then hydrolysis and enzymatic synthesis using tryptophan synthase. Through this approach, S-phenyl-L-cysteine was chemoenzymatically synthesized using tryptophan synthase from thiophenol and L-serine as the starting material. CONCLUSIONS: High-purity, optically active S-phenyl-L-cysteine was efficiently and inexpensively obtained in a total yield of 81.3% (> 99.9% purity).

A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure.

The physiological processes and mechanisms of an arterial system are complex and subtle. Physics-based models have been proven to be a very useful tool to simulate actual physiological behavior of the arteries. The current physics-based models include high-dimensional models (2D and 3D models) and low-dimensional models (0D, 1D and tube-load models). High-dimensional models can describe the local hemodynamic information of arteries in detail. With regard to an exact model of the whole arterial system, a high-dimensional model is computationally impracticable since the complex geometry, viscosity or elastic properties and complex vectorial output need to be provided. For low-dimensional models, the structure, centerline and viscosity or elastic properties only need to be provided. Therefore, low-dimensional modeling with lower computational costs might be a more applicable approach to represent hemodynamic properties of the entire arterial system and these three types of low-dimensional models have been extensively used in the study of cardiovascular dynamics. In recent decades, application of physics-based models to estimate central aortic pressure has attracted increasing interest. However, to our best knowledge, there has been few review paper about reconstruction of central aortic pressure using these physics-based models. In this paper, three types of low-dimensional physical models (0D, 1D and tube-load models) of systemic arteries are reviewed, the application of three types of models on estimation of central aortic pressure is taken as an example to discuss their advantages and disadvantages, and the proper choice of models for specific researches and applications are advised.

Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset.

BACKGROUND: Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning. METHODS: We proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch, respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their performances are evaluated by eightfold cross-validation. A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods. RESULTS: A total of 121,728 patches are generated to train and validate the CNN models. After the parameter optimization, our CNN model achieves an average F-score of 0.9917 and an area of curve up to 0.9991 for classification of lung parenchyma and non-lung-parenchyma. The obtain model can segment the lung parenchyma accurately for 201 subjects with heterogeneous lung diseases and CT scanners. The overlap ratio between the manual segmentation and the one by our method reaches 0.96. CONCLUSIONS: The results demonstrated that the proposed clustering algorithm based method can generate the training dataset for CNN models. The obtained CNN model can segment lung parenchyma with very satisfactory performance and have the potential to locate and analyze lung lesions.

Detection of Physiological Signals Based on Graphene Using a Simple and Low-Cost Method.

Despite that graphene has been extensively used in flexible wearable sensors, it remains an unmet need to fabricate a graphene-based sensor by a simple and low-cost method. Here, graphene nanoplatelets (GNPs) are prepared by thermal expansion method, and a sensor is fabricated by sealing of a graphene sheet with polyurethane (PU) medical film. Compared with other graphene-based sensors, it greatly simplifies the fabrication process and enables the effective measurement of signals. The resistance of graphene sheet changes linearly with the deformation of the graphene sensor, which lays a solid foundation for the detection of physiological signals. A signal processing circuit is developed to output the physiological signals in the form of electrical signals. The sensor was used to measure finger bending motion signals, respiration signals and pulse wave signals. All the results demonstrate that the graphene sensor fabricated by the simple and low-cost method is a promising platform for physiological signal measurement.

Appropriate Mother Wavelets for Continuous Gait Event Detection Based on Time-Frequency Analysis for Hemiplegic and Healthy Individuals.

Gait event detection is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. Recently, the continuous wavelet transform (CWT) based methods have been increasingly proposed for gait event detection due to their robustness. However, few investigations on determining the appropriate mother wavelet with proper selection criteria have been performed, especially for hemiplegic patients. In this study, the performances of commonly used mother wavelets in detecting gait events were systematically investigated. The acceleration signals from the tibialis anterior muscle of both healthy and hemiplegic subjects were recorded during ground walking and the two core gait events of heel strike (HS) and toe off (TO) were detected from the signal recordings by a CWT algorithm with different mother wavelets. Our results showed that the overall performance of the CWT algorithm in detecting the two gait events was significantly different when using various mother wavelets. By using different wavelet selection criteria, we also found that the accuracy criteria based on time-error minimization and F1-score maximization could provide the appropriate mother wavelet for gait event detection. The findings from this study will provide an insight on the selection of an appropriate mother wavelet for gait event detection and facilitate the development of adequate rehabilitation aids.

Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography.

This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.

Influence of vascular geometry on local hemodynamic parameters: phantom and small rodent study.

BACKGROUND: Many studies have demonstrated that the geometry of the carotid bifurcation enables prediction of blood flow variation associated with atherosclerotic plaque formation. The phase angle between the arterial wall circumferential strain and its instantaneous wall shear stress is known as stress phase angle (SPA). This parameter is used to evaluate hemodynamic factors of atherogenesis. Note that SPA can be numerically computed for the purpose of locating atherosclerosis in different artery geometries. However, there is no experimental data to verify its role in the location of atherosclerosis in different artery geometries. In this study, we use an ultrasonic biomechanical method to experimentally evaluate the role of SPA for locating atherosclerosis in carotid bifurcation. RESULTS: For carotid anthropomorphic vascular phantom experiments, the SPAs of common carotid arteries (CCAs), external carotid arteries (ECAs) and internal carotid arteries (ICAs) are - 148.53 ± 6.92°, - 153.95 ± 5.11°, and - 238.69 ± 1.72°, respectively. The corresponding SPAs are - 173.47 ± 0.065°, - 115.57 ± 4.83° and - 233.9 ± 8.12° for the polyvinyl alcohol (PVA-c) phantoms. In vivo mouse experiments indicated that the wall shear stress and circumferential strain were out of phase in the ICAs (- 280.08 ± 13.12°) to a greater extent as compared to CCAs (- 141.97 ± 8.03°) and ECAs (- 170.07 ± 9.24°). CONCLUSIONS: The results suggested that SPA may be a useful indicator to locate the atherosclerosis position in carotid bifurcation.

Comparison of central hemodynamic parameters for young basketball athletes and control group.

BACKGROUND: Long-term exercise training may have negative effects on cardiovascular functions. Measurement and calculation of central hemodynamic parameters can comprehensively evaluate the cardiovascular functions. This study aims to compare the central hemodynamics between young basketball athletes and matched controls. METHODS: Total 19 young long-term trained male basketball athletes and 17 matched male recreationally active controls participated. The central hemodynamic parameters such as central blood pressure, pulse pressure, heart rate (HR), augmentation index normalised to 75 bpm (AIx@HR75), augmentation index (AIx), ejection duration (ED), sub-endocardial viability ratio (SEVR) were measured, and total peripheral resistance (TPR), stroke volume (SV) and cardiac output (CO) were calculated. Non-parameter tests and t-test were used to analyse the central hemodynamic parameters between athletes and controls. RESULTS: HR (56 ± 5 bpm versus 79 ± 9 bpm, p < .001), AIx@HR75 (-8 ± 10% versus -1 ± 10%, p < .05), ED (28 ± 2% versus 36 ± 3%, p < .001) and TPR (0.004 ± 0.006 mmHg s/mL versus 0.012 ± 0.006 mmHg s/mL, p < .001) were significantly lower in basketball athletes compared to the controls. SEVR (231 ± 32% versus 159 ± 21%, p < .001) and SV (154 ± 50 mL versus 101 ± 43 mL, p < .01) were significantly higher in basketball athletes than those in the controls. However, there were no significant differences in central blood pressure, pulse pressure, AIx and CO between them. CONCLUSIONS: There is no negative effect on central hemodynamics in young basketball athletes after long-term exercise training. The young basketball athletes have a higher myocardial perfusion, higher efficiency of blood supply, stronger vascular functions and better balance of myocardial oxygen of supply and demand than the controls in this central hemodynamic parameters analysis.

Effect of short-term exercise intervention on cardiovascular functions and quality of life of chronic heart failure patients: A meta-analysis.

OBJECTIVE: The purpose of this study was to comprehensively evaluate the effect of short-term exercise intervention on the cardiovascular functions and quality of life (QoL) of patients with chronic heart failure (CHF). METHODS: This meta-analysis was analyzed using RevMan5.3 and Stata 13.0. The parameters of cardiovascular functions and QoL were assessed. Weighted mean differences and their corresponding 95% confidence intervals (CIs) were computed for continuous variables. RESULTS: Data from 2533 CHF patients enrolled in 28 published studies of randomized controlled trials (RCTs) were collated. There were significant differences in VO2 max prior to and after exercise intervention in CHF patients who are 50-55 years old (5 RCTs; 95% CI, -4.86 to -2.29; I2 = 50.5%), 60-65 years old (10 RCTs; 95% CI, -2.66 to -2.04; I2 = 0%), and 69-75 years old (5 RCTs; 95% CI, -1.88 to -0.34; I2 = 38.5%). VO2 max was significantly increased by aerobic exercise (9 RCTs; 95% CI, -3.45 to -1.92; I2 = 37.7%) and combined aerobic resistance exercise (4 RCTs; 95% CI, -4.41 to -0.26; I2 = 76.6%). There were significant differences in cardiac output (n = 303; 95% CI, -0.25 to -0.02; I2 = 12%) and QoL (n = 299; 95% CI, 3.19 to 9.70; I2 = 17%) prior to and after short-term exercise. CONCLUSION: Aerobic exercise and aerobic with resistance exercise can significantly improve the aerobic capacity of CHF patients, whereas resistance exercise cannot. The improvement in aerobic capacity caused by aerobic exercise and aerobic with resistance exercise decreases with age. Systolic blood pressure and ventricle structures and functions of CHF patients show no significant changes after the short-term exercise intervention.

[Effects of antibiotics plus efflux pump inhibitors on mutant selection window of Pseudomonas aeruginosa in vitro].

OBJECTIVE: To explore the effects of antibiotics plus efflux pump (EPI) inhibitors on mutant selection window of Pseudomonas aeruginosa in vitro. METHODS: In standard strains, the minimum inhibitory concentrations (MICs) of meropenem, ceftazidime, amikacin and ciprofloxacin alone were measured. Then the MICs of four antibiotics respectively plus EPI, including reserpine, omeprazole, azithromycin, carbonyl cyanide m-chlorophenyl- hydrazone (CCCP) and Phe-Arg-ß-naphtylamide (PAßN), were measured by checkerboard test. And the mutant prevention concentrations (MPC) of antibiotics alone and antibiotics plus different EPIs were also measured by agar dilution method. Selection indices (SI) were acquired through MPC divided by MIC. Similarly, the SI of meropenem, ceftazidime and ciprofloxacin which respectively plus amikacin and amikacin plus ciprofloxacin were measured. In clinical strains, MIC, MPC and SI of antibiotics alone and four antibiotics plus CCCP were measured. RESULTS: In standard strains, the SI of meropenem, ceftazidime, amikacin and ciprofloxacin alone were >32, >32, 16 and 16. After adding EPI, the SI of four antibiotics were 16, 16, 16, 8 (reserpine), 16, 32, 16, 8 (omeprazole), 8, 16, 16, 8 (azithromycin), 8, 8, 16, 8 (CCCP) and 8, 16, 16, 8 (PAßN). The SI of meropenem, ceftazidime and ciprofloxacin plus amikacin were 4, 4, 8. And the SI of amikacin plus ciprofloxacin was 4. In clinical strains, the SI of ciprofloxacin significantly decreased after adding CCCP. CONCLUSIONS: Both MIC and MPC of meropenem, ceftazidime and ciprofloxacin decreased significantly after adding EPI. The mutant selection window decreased when ciprofloxacin was combined with CCCP.

[Design and implementation of the pulse wave generator with field programmable gate array based on windkessel model].

Pulse waves contain rich physiological and pathological information of the human vascular system. The pulse wave diagnosis systems are very helpful for the clinical diagnosis and treatment of cardiovascular diseases. Accurate pulse waveform is necessary to evaluate the performances of the pulse wave equipment. However, it is difficult to obtain accurate pulse waveform due to several kinds of physiological and pathological conditions for testing and maintaining the pulse wave acquisition devices. A pulse wave generator was designed and implemented in the present study for this application. The blood flow in the vessel was simulated by modeling the cardiovascular system with windkessel model. Pulse waves can be generated based on the vascular systems with four kinds of resistance. Some functional models such as setting up noise types and signal noise ratio (SNR) values were also added in the designed generator. With the need of portability, high speed dynamic response, scalability and low power consumption for the system, field programmable gate array (FPGA) was chosen as hardware platform, and almost all the works, such as developing an algorithm for pulse waveform and interfacing with memory and liquid crystal display (LCD), were implemented under the flow of system on a programmable chip (SOPC) development. When users input in the key parameters through LCD and touch screen, the corresponding pulse wave will be displayed on the LCD and the desired pulse waveform can be accessed from the analog output channel as well. The structure of the designed pulse wave generator is simple and it can provide accurate solutions for studying and teaching pulse waves and the detection of the equipments for acquisition and diagnosis of pulse wave.