Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by ...heartbeat noise and are negatively affected by skewed data. To address these problems, a novel heartbeat recognition method is presented. The aim of this study is to apply a principal component analysis network (PCANet) for feature extraction based on a noisy ECG signal. To improve the classification speed, a linear support vector machine (SVM) was applied. In our experiments, we identified five types of imbalanced original and noise-free ECGs in the MIT-BIH arrhythmia database to verify the effectiveness of our algorithm and achieved 97.77% and 97.08% accuracy, respectively. The results show that our method has high recognition accuracy in the classification of skewed and noisy heartbeats, indicating that our method is a practical ECG recognition method with suitable noise robustness and skewed data applicability.
•Our method is robust to noise.•Our results are better than most other methods.•Our method has only a few hyperparameters and does not require iteration.•Fiducial point detection is no need.•Our approach is applicable for skewed data.
Studies have shown that the combined application of hyaluronic acid (HA) and platelet-rich plasma (PRP) can repair degenerated cartilage and delay the progression of knee osteoarthritis (KOA). The ...purpose of this study was to explore the efficacy and safety of the intra-articular injection of PRP combined with HA compared with the intra-articular injection of PRP or HA alone in the treatment of KOA.
The PubMed, Cochrane Library, EMBASE and China National Knowledge Infrastructure (CNKI) databases were searched from inception to December 2019. Randomized controlled trials and cohort studies of PRP combined with HA for KOA were included. Two orthopaedic surgeons conducted the literature retrieval and extracted the data. Outcome indicators included the Western Ontario and McMaster Universities Arthritis Index (WOMAC), the Lequesne Index, the visual analogue scale (VAS) for pain, and adverse events (AEs). Review Manager 5.3 was used to calculate the relative risk (RR) or standardized mean difference (SMD) of the pooled data. STATA 14.0 was used for quantitative publication bias evaluation.
Seven studies (5 randomized controlled trials, 2 cohort studies) with a total of 941 patients were included. In the VAS comparison after 6 months of follow-up, PRP combined with HA was more likely to reduce knee pain than PRP alone (SMD: - 0.31; 95% confidence interval (CI): - 0.55 to - 0.06; P = 0.01 < 0.05). PRP combined with HA for KOA achieved better improvements in the WOMAC Function Score (SMD: -0.32; 95% CI: - 0.54 to - 0.10; P < 0.05) and WOMAC Total Score (SMD: -0.42; 95% CI: - 0.67 to - 0.17; P < 0.05) at the 12-month follow-up than did the application of PRP alone. In a comparison of Lequesne Index scores at the 6-month follow-up, PRP combined with HA improved knee pain scores more than PRP alone (SMD: -0.42; 95% CI: - 0.67 to - 0.17; P < 0.05). In terms of AEs, PRP combined with HA was not significantly different from PRP or HA alone (P > 0.05).
Compared with intra-articular injection of PRP alone, that of PRP combined with HA can improve the WOMAC Function Scores, WOMAC Total Score, 6-month follow-up VAS ratings, and Lequesne Index scores. However, in terms of the incidence of AEs, PRP combined with HA is not significantly different from PRP or HA alone.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Beta-blocker (BB) is suggested to have anticancer efficacy. However, the potential influence of BB use on overall survival (OS) in patients with lung cancer remains undetermined. We aimed to evaluate ...the above relationship in an updated meta-analysis.
Observational studies comparing OS between users and non-users of BB with lung cancer were identified by search of PubMed, Embase, and Cochrane's Library. A random-effect model was used to pool the results.
Ten retrospective cohort studies with 30870 patients were included. Overall, BB use was not associated with significantly improved OS in lung cancer (hazard ratio HR = 1.02, 95% confidence interval CI: 0.98 to 1.06, p = 0.33) with moderate heterogeneity (I2 = 29%). Stratified analyses showed similar results in patients with non-small cell lung cancer and small cell lung cancer, in studies with BB use before and after the diagnosis of lung cancer, and in studies with or without adjustment of smoking. Use of BB was associated with improved OS in patients with stage III lung cancer (HR = 0.91, 95% CI: 0.85 to 0.98, p = 0.02) and in patients that did not receive surgery resection (HR = 0.78, 95% CI: 0.64 to 0.96, p = 0.02), while use of non-selective BB was associated with worse OS (HR = 1.14, 95% CI: 1.01 to 1.28, p = 0.03).
This meta-analysis of retrospective cohort studies does not support a significant association between BB use and improved OS in lung cancer.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs ...is especially important. Currently, deep learning algorithm-based CVD diagnosis methods are extensively employed, however, most such algorithms can only utilize one-lead ECGs. Hence, the potential information in other-lead ECGs was not utilized. To address this issue, we have developed novel methods for diagnosing arrhythmia. In this work, DL-CCANet and TL-CCANet are proposed to extract abstract discriminating features from dual-lead and three-lead ECGs, respectively. Then, the linear support vector machine specializing in high-dimensional features is used as the classifier model. On the MIT-BIH database, a 95.2% overall accuracy is obtained by detecting 15 types of heartbeats using DL-CCANet. On the INCART database, overall accuracies of 94.01% (II and V1 leads), 93.90% (V1 and V5 leads) and 94.07% (II and V5 leads) are achieved by detecting seven types of heartbeat using DL-CCANet, while TL-CCANet yields a higher overall accuracy of 95.52% using the above three leads. In addition, all of the above experiments are implemented using noisy ECG data. The proposed methods have potential to be applied in the clinic and mobile devices.
The direct writing technology was used to create microfluidic three-dimensional terahertz photonic crystal structures (3D-TPCSs) with a glass cement ink, which demonstrated potentials for various ...terahertz technology applications. By a simple injection of liquid alloy into them, metallic 3D-TPCSs could be created easily at a low cost to solve the challenges of their creation by current approaches. These microfluidic 3D-TPCSs also possessed a specific capability of changing their terahertz properties in real time without structural changes by injecting fluidic media with different dielectric properties into their microfluidic channels, which endowed them the easy integration into various terahertz devices that require terahertz modulation for a wide range of applications. Due to their microsized channel structure and subsequent reduction of terahertz irradiation absorption by water in them, they demonstrated the potential as real time, nondestructive biological and chemical sensors to detect changes occurring in them in the fluidic media with the terahertz time-domain spectroscopy (THz-TDS).
The adsorption and activation of CO2 molecules on the surface of photocatalysts are critical steps to realize efficient solar energy-induced CO2 conversion to valuable chemicals. In this work, a ...defect engineering approach of a high-valence cation Nb-doping into TiO2 was developed, which effectively enhanced the adsorption and activation of CO2 molecules on the Nb-doped TiO2 surface. A highly ordered Nb-doped TiO2 nanotube array was prepared by anodization of the Ti–Nb alloy foil and subsequent annealing at 550 °C in air for 2 h for its crystallization. Our sample showed a superior photocatalytic CO2 reduction performance under simulated solar illumination. The main CO2 reduction product was a higher-energy compound of acetaldehyde, which could be easily transported and stored and used to produce various key chemicals as intermediates. The acetaldehyde production rate was over ∼500 μmol·g–1·h–1 with good stability for repeated long-time uses, and it also demonstrated a superior product selectivity to acetaldehyde of over 99%. Our work reveals that the Nb-doped TiO2 nanotube array could be a promising candidate with high efficiency and good product selectivity for the photocatalytic CO2 reduction with solar energy.
Currently, many challenges are faced in simulating ozone(O3), sulfate(SO42−), and nitrate(NO3−) concentrations over East Asia, particularly the overestimation of surface O3 and NO3− concentrations ...and underestimation of the SO42− concentration during haze episodes. In this study, we examined the radiative and heterogeneous chemical effects of aerosols by incorporating recently reported mechanisms, including self-amplifying SO42− formation, dinitrogen pentoxide (N2O5) hydrolysis, and a heterogeneous reaction converting gaseous nitric acid (HNO3) to nitric oxide (NOx), into a Nested Air Quality Prediction Modeling System. Uptakes by aerosols can be computed through a simple parameterization that is dependent on the aerosol core and shell species, shell thickness, and amount of aerosol liquid water. In this study, a 1-year simulation was conducted for 2013. The updated model successfully reproduced the seasonal and daily observations of O3, fine particulate matter, SO42−, and NO3− concentrations in East Asia. Our results revealed that heterogeneous reactions reduced more surface O3 concentrations (10–20 ppbv) in the polluted regions of East China than did perturbations in photolysis frequencies from aerosols, effectively again improving the comparison between simulations and observations. Oxidation of SO2 by NO2 on wet aerosols significantly enhanced SO42− formation, with sulfate covering approximately ~30–60% of total sulfate concentrations in North China Plain during haze days in winter. The uptake of reactive nitrogen species on aerosols effectively reduced NO3− concentrations and successfully balanced the NOx/HNO3 chemistry in the models. We recommended that larger reductions of gaseous precursors should be considered in China to achieve the national air quality objective. The results show that surface O3 concentrations over East China will increase if the emission of aerosols is reduced without corresponding reductions in O3 precursors.
Contributions of heterogeneous chemical effects of aerosols as a function of hourly ozone (a), sulfate (b) and nitrate (c) concentrations in North China in winter (blue) and summer(green). Display omitted
•A scheme of radiative and heterogeneous effects of aerosols was coupled in NAQPMS.•Heterogeneous reactions largely reduced surface O3 in polluted regions of China.•Oxidation of SO2 by NO2 on wet aerosols enhanced SO42− in North China in haze days.•Uptake of reactive nitrogen species on aerosols effectively reduced NO3−.
Most photocatalysts only function under illumination, while many potential applications require continuous activities in dark. Thus, novel photocatalysts should be developed, which could store part ...of their photoactivity in "memory" under illumination and then be active from this "memory" after the illumination is turned off for an extended period of time. Here a novel composite photocatalyst of SnO2 nanoparticle-decorated Cu2O nanocubes is developed. Their large conduction band potential difference and the inner electrostatic field formed in the p-n heterojunction provide a strong driving force for photogenerated electrons to move from Cu2O to SnO2 under visible light illumination, which could then be released to react with O2 in dark to produce H2O2 for its post-illumination activity. This work demonstrates that the selection of decoration components for photocatalysts with the post-illumination photocatalytic "memory" could be largely expanded to semiconductors with conduction band potentials less positive than the two-electron reduction potential of O2.
Cardiovascular disease is the leading cause of death worldwide. Immediate and accurate diagnoses of cardiovascular disease are essential for saving lives. Although most of the previously reported ...works have tried to classify heartbeats accurately based on the intra-patient paradigm, they suffer from category imbalance issues since abnormal heartbeats appear much less regularly than normal heartbeats. Furthermore, most existing methods rely on data preprocessing steps, such as noise removal and R-peak location. In this study, we present a robust classification system using a multilevel discrete wavelet transform densely network (MDD-Net) for the accurate detection of normal, coronary artery disease (CAD), myocardial infarction (MI) and congestive heart failure (CHF). First, the raw ECG signals from different databases are divided into same-size segments using an original adaptive sample frequency segmentation algorithm (ASFS). Then, the fusion features are extracted from the MDD-Net to achieve great classification performance. We evaluated the proposed method considering the intra-patient and inter-patient paradigms. The average accuracy, positive predictive value, sensitivity and specificity were 99.74%, 99.09%, 98.67% and 99.83%, respectively, under the intra-patient paradigm, and 96.92%, 92.17%, 89.18% and 97.77%, respectively, under the inter-patient paradigm. Moreover, the experimental results demonstrate that our model is robust to noise and class imbalance issues.
An attack of congestive heart failure (CHF) can cause symptoms such as difficulty breathing, dizziness, or fatigue, which can be life-threatening in severe cases. An electrocardiogram (ECG) is a ...simple and economical method for diagnosing CHF. Due to the inherent complexity of ECGs and the subtle differences in the ECG waveform, misdiagnosis happens often. At present, the research on automatic CHF detection methods based on machine learning has become a research hotspot. However, the existing research focuses on an intra-patient experimental scheme and lacks the performance evaluation of working under noise, which cannot meet the application requirements. To solve the above issues, we propose a novel method to identify CHF using the ECG-Convolution-Vision Transformer Network (ECVT-Net). The algorithm combines the characteristics of a Convolutional Neural Network (CNN) and a Vision Transformer, which can automatically extract high-dimensional abstract features of ECGs with simple pre-processing. In this study, the model reached an accuracy of 98.88% for the inter-patient scheme. Furthermore, we added different degrees of noise to the original ECGs to verify the model's noise robustness. The model's performance in the above experiments proved that it could effectively identify CHF ECGs and can work under certain noise.
PDF
