A new method for vascular age estimation based on relative risk difference in vascular aging.

OBJECTIVE: The current models of estimating vascular age (VA) primarily rely on the regression label expressed with chronological age (CA), which does not account individual differences in vascular aging (IDVA) that are difficult to describe by CA. This may lead to inaccuracies in assessing the risk of cardiovascular disease based on VA. To address this limitation, this work aims to develop a new method for estimating VA by considering IDVA. This method will provide a more accurate assessment of cardiovascular disease risk. METHODS: Relative risk difference in vascular aging (RRDVA) is proposed to replace IDVA, which is represented as the numerical difference between individual predicted age (PA) and the corresponding mean PA of healthy population. RRDVA and CA are regard as the influence factors to acquire VA. In order to acquire PA of all samples, this work takes CA as the dependent variable, and mines the two most representative indicators from arteriosclerosis data as the independent variables, to establish a regression model for obtaining PA. RESULTS: The proposed VA based on RRDVA is significantly correlated with 27 indirect indicators for vascular aging evaluation. Moreover, VA is better than CA by comparing the correlation coefficients between VA, CA and 27 indirect indicators, and RRDVA greater than zero presents a higher risk of disease. CONCLUSION: The proposed VA overcomes the limitation of CA in characterizing IDVA, which may help young groups with high disease risk to promote healthy behaviors.

Vascular age acquired from the pulse signal: A new index to screen early vascular aging.

BACKGROUND: Chronological age (CA) has been adopted as an important independent risk factor in cardiovascular risk assessment. However, different individuals with same CA may have distinct actual vascular aging due to various lifestyles. Therefore, it is difficult to fully describe the difference of actual vascular aging by CA. OBJECTIVE: This study proposes a new index vascular age (VA) to avoid the limitations of CA. METHOD: In this work, VA refers to the sum of CA and lifestyle impact (AgeLI). Firstly, we take the pulse signal features and CA as independent variables and dependent variable respectively, and adopt cross validation to train Support Vector Regression model. Then we acquire the predicted chronological age (PA) of all subjects with the model. Secondly, we obtain the function model between CA and PA, and calculate the expectation of PA (ePA) for each subject. Simultaneously, we take the difference between PA and ePA as the estimated value of AgeLI to further calculate VA. Finally, in order to evaluate the effectiveness of VA, we compare the correlations between CA, PA, VA and 8 objective indices such as augmentation index, pulse transit time, diastolic augmentation index, etc. RESULTS: In general, VA and PA are closer to these 8 objective indices than CA. Moreover, VA is also superior to PA in vascular aging evaluation. CONCLUSION: The VA suggested in this study emphasizes the difference of vascular aging in same CA group, which can better reflect the actual vascular aging than CA and PA.

Design and evaluation of an exergame system to assist knee disorders patients' rehabilitation based on gesture interaction.

We designed a knee rehabilitation exercise game (Exergame) for home-based rehabilitation of patients with knee disorders. The system includes three functional components: knee exercise plan formulation, exergame, and exercise feedback. The 3D Human Pose Estimation based on images is used as the gesture interaction to capture the patient's primary joint motion data. We recruited 20 knee osteoarthritis (KOA) to evaluate the system's feasibility and user experience. The physician's group formulated the patient's exercise plans. The average accuracy of motion recognition is 95.2%, indicating that the system can effectively guide rehabilitation training for KOA patients. The results of the UEQ-S questionnaire, namely the practical quality value (1.63 ± 0.85), hedonic quality value (1.75 ± 0.86), and the total value (1.69 ± 0.86) of 20 patients, indicate that the system provides an excellent user experience, which improves the willingness and compliance of the patients for the active exercise. The above evidence confirms that the proposed approach is suitable for Knee disorders rehabilitation exercise and has promising application prospects. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-022-00189-5.

Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression.

Purpose: Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal. Methods: Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices. Results: Compared with the CA, the VA is closer to PW (r = 0.539, P < 0.001 to r = 0.589, P < 0.001 in men; r = 0.325, P < 0.001 to r = 0.400, P < 0.001 in women), IPA (r = - 0.446, P < 0.001 to r = - 0.534, P < 0.001 in men; r = - 0.623, P < 0.001 to r = - 0.660, P < 0.001 in women), RBA (r = 0.328, P < 0.001 to r = 0.371, P < 0.001 in women), AIx (r = 0.659, P < 0.001 to r = 0.738, P < 0.001 in men; r = 0.547, P < 0.001 to r = 0.573, P < 0.001 in women), DAI (r = 0.517, P < 0.001 to r = 0.532, P < 0.001 in men; r = 0.507, P < 0.001 to r = 0.570, P < 0.001 in women) and PTT (r = 0.526, P < 0.001 to r = 0.659, P < 0.001 in men; r = 0.577, P < 0.001 to r = 0.814, P < 0.001 in women). Conclusion: The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.

Motion Intent Recognition in Intelligent Lower Limb Prosthesis Using One-Dimensional Dual-Tree Complex Wavelet Transforms.

The motion intent recognition via lower limb prosthesis can be regarded as a kind of short-term action recognition, where the major issue is to explore the gait instantaneous conversion (known as transitional pattern) between each two adjacent different steady states of gait mode. Traditional intent recognition methods usually employ a set of statistical features to classify the transitional patterns. However, the statistical features of the short-term signals via the instantaneous conversion are empirically unstable, which may degrade the classification accuracy. Bearing this in mind, we introduce the one-dimensional dual-tree complex wavelet transform (1D-DTCWT) to address the motion intent recognition via lower limb prosthesis. On the one hand, the local analysis ability of the wavelet transform can amplify the instantaneous variation characteristics of gait information, making the extracted features of instantaneous pattern between two adjacent different steady states more stable. On the other hand, the translation invariance and direction selectivity of 1D-DTCWT can help to explore the continuous features of patterns, which better reflects the inherent continuity of human lower limb movements. In the experiments, we have recruited ten able-bodied subjects and one amputee subject and collected data by performing five steady states and eight transitional states. The experimental results show that the recognition accuracy of the able-bodied subjects has reached 98.91%, 98.92%, and 97.27% for the steady states, transitional states, and total motion states, respectively. Furthermore, the accuracy of the amputee has reached 100%, 91.16%, and 90.27% for the steady states, transitional states, and total motion states, respectively. The above evidence finally indicates that the proposed method can better explore the gait instantaneous conversion (better expressed as motion intent) between each two adjacent different steady states compared with the state-of-the-art.

A semi-dry chemistry hydrogel-based smart biosensing platform for on-site detection of metal ions.

Balancing operability and performance has long been a focus of research in bioanalysis and biosensing. In this work, between the traditional wet chemistry and dry chemistry, we develop a semi-dry smart biosensing platform with favourable operability and performance for metal ions detection. This platform is based on the integration of a stimuli-responsive hydrogel with intelligent image recognition. The hydrogel consists of agarose as a matrix and well-designed fluorescent DNA probes as response elements. Target metal ions in a test sample can diffuse into the hydrogel and activate the DNA probes, outputting fluorescence signals for intelligent imaging. In this way, sensitive and convenient detection of metal ions such as potassium ions (K+) and mercury ions (Hg2+) can be achieved without the assistance of huge instruments and professional workers. The detection limits for K+ and Hg2+ are 0.34 mM and 5.6 nM, respectively. Detection of ions in serum and lake water is also available. Moreover, the hydrogel-based biosensing platform exhibits favorable selectivity, anti-degradation ability, and long-term stability. High-throughput testing can be also achieved by punching multiple test microwells in a single piece of hydrogel. The concept and successful practice of a semi-dry chemistry-based strategy make up for the shortcomings of wet chemistry and dry chemistry, and provide a promising approach for on-site testing.

A Convolutional Neural Network System to Discriminate Drug-Target Interactions.

Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or functions. Exploring potential drug-target interactions (DTIs) are crucial for drug discovery and effective drug development. Computational methods were widely applied in drug-target interactions, since experimental methods are extremely time-consuming and resource-intensive. In this paper, we proposed a novel deep learning-based prediction system, with a new negative instance generation, to identify DTIs. As a result, our method achieved an accuracy of 0.9800 on our created dataset. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded a good performance with accuracy of 0.8814 and AUC value of 0.9527 on the dataset. The outcome of our experimental results indicated that the proposed method, involving the credible negative generation, can be employed to discriminate the interactions between drugs and targets. Website: http://www.dlearningapp.com/web/DrugCNN.htm.

Luminescent europium(III)-organic framework for visual and on-site detection of hydrogen peroxide via a tablet computer.

A luminescent metal-organic framework of type Eu(III)-MOF has been fabricated for visual and on-site fluorometric determination of hydrogen peroxide (H2O2) via a tablet computer. The maximum excitation and emission peaks of type Eu(III)-MOF were found at λex = 290 nm and λem = 615 nm, respectively. The average length of Eu-MOF is 1.21 ± 0.07 µm. In the presence of the target H2O2, Fe2+ is transmitted into Fe3+ via Fenton reaction, leading to a fluorescence quenching of Eu-MOF. Therefore, visible color change occurred from bright red into colorless. Interestingly, by means of tablet computer's digital camera and ImageJ software, fluorescent signals were captured and transduced into digital parameters, resulting in a linear relationship between fluorescence intensity and the concentration of H2O2. As a result, the determination of H2O2 without the aid of complicated instruments is achieved in the range 2.0 µM to 0.2 mM with a detection limit of 1.02 µM. Our approach has been successfully applied to quantify H2O2 in serum, urine, and waste water with good recovery and precision (< 2.5% RSD). Besides, our assay has been exploited for visual detection of H2O2 released from HepG2 cells with the advantages of portability and accuracy. Moreover, the strategy displays acceptable selectivity and stability. Hence, our assay provides an alternative practical method for on-site determination of H2O2 without the need for instruments. Graphical abstract Schematic representation of the synthesis procedure of a luminescent Eu-MOF, which has been successfully applied for on-site detection of H2O2 via Fenton reaction and imaging analysis technique. The method exhibits handheld and accuracy for H2O2 determination, holding the potential for biochemical and clinical applications in remote regions.

A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb Prosthesis.

Powered intelligent lower limb prosthesis can actuate the knee and ankle joints, allowing transfemoral amputees to perform seamless transitions between locomotion states with the help of an intent recognition system. However, prior intent recognition studies often installed multiple sensors on the prosthesis, and they employed machine learning techniques to analyze time-series data with empirical features. We alternatively propose a novel method for training an intent recognition system that provides natural transitions between level walk, stair ascent / descent, and ramp ascent / descent. Since the transition between two neighboring states is driven by motion intent, we aim to explore the mapping between the motion state of a healthy leg and an amputee's motion intent before the upcoming transition of the prosthesis. We use inertial measurement units (IMUs) and put them on the healthy leg of lower limb amputees for monitoring its locomotion state. We analyze IMU data within the early swing phase of the healthy leg, and feed data into a convolutional neural network (CNN) to learn the feature mapping without expert participation. The proposed method can predict the motion intent of both unilateral amputees and the able-bodied, and help to adaptively calibrate the control strategy for actuating powered intelligent prosthesis in advance. The experimental results show that the recognition accuracy can reach a high level (94.15% for the able-bodied, 89.23% for amputees) on 13 classes of motion intent, containing five steady states on different terrains as well as eight transitional states among the steady states.

Sketch distance-based clustering of chromosomes for large genome database compression.

BACKGROUND: The rapid development of Next-Generation Sequencing technologies enables sequencing genomes with low cost. The dramatically increasing amount of sequencing data raised crucial needs for efficient compression algorithms. Reference-based compression algorithms have exhibited outstanding performance on compressing single genomes. However, for the more challenging and more useful problem of compressing a large collection of n genomes, straightforward application of these reference-based algorithms suffers a series of issues such as difficult reference selection and remarkable performance variation. RESULTS: We propose an efficient clustering-based reference selection algorithm for reference-based compression within separate clusters of the n genomes. This method clusters the genomes into subsets of highly similar genomes using MinHash sketch distance, and uses the centroid sequence of each cluster as the reference genome for an outstanding reference-based compression of the remaining genomes in each cluster. A final reference is then selected from these reference genomes for the compression of the remaining reference genomes. Our method significantly improved the performance of the-state-of-art compression algorithms on large-scale human and rice genome databases containing thousands of genome sequences. The compression ratio gain can reach up to 20-30% in most cases for the datasets from NCBI, the 1000 Human Genomes Project and the 3000 Rice Genomes Project. The best improvement boosts the performance from 351.74 compression folds to 443.51 folds. CONCLUSIONS: The compression ratio of reference-based compression on large scale genome datasets can be improved via reference selection by applying appropriate data preprocessing and clustering methods. Our algorithm provides an efficient way to compress large genome database.

Relative validity of an indirect calorimetry device for measuring resting energy expenditure and respiratory quotient.

BACKGROUND AND OBJECTIVES: Resting energy expenditure (REE) and respiratory quotient (RQ) are important for optimal nutritional care. The purpose of this study was to assess the accuracy and repeatability of an indirect calorimetry device (IIM-IC-100) in the measurement of REE and RQ in healthy Chinese adults. METHODS AND STUDY DESIGN: A total of 38 healthy adults (19 male and 19 female) aged 18-52 years (25±6 years) were enrolled in this study. REE and RQ were measured by IIM-IC-100 and by VO2000, alternately and in duplicate. RESULTS: There was a highly significant correlation between IIM-IC-100 REE and VO2000 REE (r=0.906, p<0.001), with mean IIM-IC-100 REE significantly higher than that of VO2000 (1475±269 vs 1394±313 kcal/d, p=0.002). Bland- Altman analysis revealed that the mean difference between IIM-IC-100 REE and VO2000 REE was 81.3 kcal/d, with limits of agreement of -185 to +347 kcal/d. There was no significant difference in RQ between the two devices. No significant differences were observed between the repeated measurements for both devices. Intrasubject coefficients of variation (CVs) of REE were smaller for IIM-IC-100 (5.8%) than for VO2000 (10.5%), while CVs of RQ were similar for IIM-IC-100 (7.2%) and VO2000 (6.9%). CONCLUSIONS: These preliminary data indicated that the IIM-IC-100 showed promise as an accurate and precise tool in the assessment of REE and RQ in healthy Chinese adults.

Correlation between ultrasonic power spectrum and bone density on the heel.

The purposes of this paper were to evaluate the correlation between ultrasonic power spectrum and bone density and to extract the effectiveness of parameters from power spectrum for evaluating bone density. A total of 50 persons 24-72years of age were recruited. All study participants underwent bone mineral density (BMD) measurements of the lumbar spine (vertebral levels L1-L4). The participants also underwent calcaneal measurements to determine ultrasonic power spectrum with central frequencies of 0.5MHz. Three parameters from normalized power spectrum, called principle frequency (PF), frequency band (FB), and amplitude for principle frequency (APF), were chosen and be evaluated the correlation with the lumbar spine BMD. The correlation coefficient of PF, FB and APF with BMD was r=-0.48 (p<0.001), r=0.48 (p<0.001), and r=-0.71 (p<0.001), respectively. The results showed that the correlation between APF and BMD was better than the correlation among PF, FB and BMD, and APF have a significant correlation with BMD. In conclusion, the correlations among the parameters of ultrasonic power spectrum and BMD are significant, and especially APF performs better than PF and FB in evaluating bone density of participants. These results suggest that ultrasonic power spectrum may contain substantial information not already contained in BUA and SOS. A multiple regression model including all three QUS variables was somewhat more predictive of BMD than a model including only BUA and SOS.