Myocardial ischemia simulation based on a multi-scale heart electrophysiology model.

BACKGROUND: Myocardial ischemia, caused by insufficient myocardial blood supply, is a leading cause of human death worldwide. Therefore, it is crucial to prioritize the prevention and treatment of this condition. Mathematical modeling is a powerful technique for studying heart diseases. OBJECTIVE: The aim of this study was to discuss the quantitative relationship between extracellular potassium concentration and the degree of myocardial ischemia directly related to it. METHODS: A human cardiac electrophysiological multiscale model was developed to calculate action potentials of all cells simultaneously, enhancing efficiency over traditional reaction-diffusion models. RESULTS: Contrary to the commonly held view that myocardial ischemia is caused by an increase in extracellular potassium concentration, our simulation results indicate that level 1 ischemia is associated with a decrease in extracellular potassium concentration. CONCLUSION: This unusual finding provides a new perspective on the mechanisms underlying myocardial ischemia and has the potential to lead to the development of new diagnostic and treatment strategies.

Prediction of acute kidney injury after cardiac surgery with fibrinogen-to-albumin ratio: a prospective observational study.

Background: The occurrence of acute kidney injury (AKI) following cardiac surgery is common and linked to unfavorable consequences while identifying it in its early stages remains a challenge. The aim of this research was to examine whether the fibrinogen-to-albumin ratio (FAR), an innovative inflammation-related risk indicator, has the ability to predict the development of AKI in individuals after cardiac surgery. Methods: Patients who underwent cardiac surgery from February 2023 to March 2023 and were admitted to the Cardiac Surgery Intensive Care Unit of a tertiary teaching hospital were included in this prospective observational study. AKI was defined according to the KDIGO criteria. To assess the diagnostic value of the FAR in predicting AKI, calculations were performed for the area under the receiver operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Of the 260 enrolled patients, 85 developed AKI with an incidence of 32.7%. Based on the multivariate logistic analyses, FAR at admission [odds ratio (OR), 1.197; 95% confidence interval (CI), 1.064-1.347, p = 0.003] was an independent risk factor for AKI. The receiver operating characteristic (ROC) curve indicated that FAR on admission was a significant predictor of AKI [AUC, 0.685, 95% CI: 0.616-0.754]. Although the AUC-ROC of the prediction model was not substantially improved by adding FAR, continuous NRI and IDI were significantly improved. Conclusions: FAR is independently associated with the occurrence of AKI after cardiac surgery and can significantly improve AKI prediction over the clinical prediction model.

A fast and non-invasive artificial intelligence olfactory-like system that aids diagnosis of Parkinson's disease.

BACKGROUND AND PURPOSE: Several previous studies have shown that skin sebum analysis can be used to diagnose Parkinson's disease (PD). The aim of this study was to develop a portable artificial intelligence olfactory-like (AIO) system based on gas chromatographic analysis of the volatile organic compounds (VOCs) in patient sebum and explore its application value in the diagnosis of PD. METHODS: The skin VOCs from 121 PD patients and 129 healthy controls were analyzed using the AIO system and three classic machine learning models were established, including the gradient boosting decision tree (GBDT), random forest and extreme gradient boosting, to assist the diagnosis of PD and predict its severity. RESULTS: A 20-s time series of AIO system data were collected from each participant. The VOC peaks at a large number of time points roughly concentrated around 5-12 s were significantly higher in PD subjects. The gradient boosting decision tree model showed the best ability to differentiate PD from healthy controls, yielding a sensitivity of 83.33% and a specificity of 84.00%. However, the system failed to predict PD progression scored by Hoehn-Yahr stage. CONCLUSIONS: This study provides a fast, low-cost and non-invasive method to distinguish PD patients from healthy controls. Furthermore, our study also indicates abnormal sebaceous gland secretion in PD patients, providing new evidence for exploring the pathogenesis of PD.

Construction of bimetallic phosphide nanostructures with in situ growth, reduction, and phosphidation of ultra-thin graphene layers as highly efficient catalysts towards the OER.

We present a novel approach for the in situ growth of bimetallic silicate onto ultrathin graphene, followed by in situ reduction and phosphorization to obtain uniformly dispersed bimetallic phosphides (rGO@FeNiP/rGO@FeCoP) on graphene layers. Unlike the traditional simple composites of single-metallic phosphides and carbon materials, the bimetallic synergy of rGO@FeNiP/rGO@FeCoP obtained through in situ growth, reduction, phosphorization, and alkaline treatment exhibits a large surface area, more nanopores and defects, and more active sites, facilitates electrolyte diffusion and gas release, accelerates electron transfer and enhances electrocatalytic oxygen evolution reaction (OER) performance. Furthermore, the continuous carbon layer architecture surrounding FeNiP/FeCoP provides structural support, improving catalyst stability. We have investigated the effect of different proportions of bimetals on electrocatalytic performance, providing a rational design and synthesis strategy for carbon-based bimetallic phosphides as a promising electrocatalyst for the OER.

Fault Diagnosis of Rotating Machinery Using Kernel Neighborhood Preserving Embedding and a Modified Sparse Bayesian Classification Model.

Fault diagnosis of rotating machinery plays an important role in modern industrial machines. In this paper, a modified sparse Bayesian classification model (i.e., Standard_SBC) is utilized to construct the fault diagnosis system of rotating machinery. The features are extracted and adopted as the input of the SBC-based fault diagnosis system, and the kernel neighborhood preserving embedding (KNPE) is proposed to fuse the features. The effectiveness of the fault diagnosis system of rotating machinery based on KNPE and Standard_SBC is validated by utilizing two case studies: rolling bearing fault diagnosis and rotating shaft fault diagnosis. Experimental results show that base on the proposed KNPE, the feature fusion method shows superior performance. The accuracy of case1 and case2 is improved from 93.96% to 99.92% and 98.67% to 99.64%, respectively. To further prove the superiority of the KNPE feature fusion method, the kernel principal component analysis (KPCA) and relevance vector machine (RVM) are utilized, respectively. This study lays the foundation for the feature fusion and fault diagnosis of rotating machinery.

Ingenious use of autocatalytic hydrodeoxygenation for the separation and recovery of oil and iron from rolling oil sludge.

This paper introduces a transformative hydrodeoxygenation process for the simultaneous recovery of oil and iron from hazardous rolling oil sludge (ROS). Leveraging the inherent catalytic capabilities of iron/iron oxide nanoparticles in the sludge, our process enables the conversion of fatty acids and esters into hydrocarbons under conditions of 4.5 MPa, 330 °C, and 500 rpm. This reaction triggers nanoparticle aggregation and subsequent separation from the oil phase, allowing for effective resource recovery. In contrast to conventional techniques, this method achieves a high recovery rate of 98.3% while dramatically reducing chemical reagent consumption. The reclaimed petroleum and iron-ready for high-value applications-are worth 3910 RMB/ton. Moreover, the process facilitates the retrieval of nanoscale magnetic Fe and Fe0 particles, and the oil, with an impressive hydrocarbon content of 87.8%, can be further refined. This energy-efficient approach offers a greener, more sustainable pathway for ROS valorization.

Editorial to the Special Issue "Molecular Aspects in Catalytic Materials for Pollution Elimination and Green Chemistry".

The Special Issue on "Molecular Aspects in Catalytic Materials for Pollution Elimination and Green Chemistry" encompasses two aims: one is to remove the pollutants produced in the downstream, and the other is to synthesize chemicals by a green route, avoiding the production of pollutants [...].

Membrane Electrode Assembly for Electrocatalytic CO2 Reduction: Principle and Application.

Electrocatalytic CO2 reduction reaction (CO2 RR) in membrane electrode assembly (MEA) systems is a promising technology. Gaseous CO2 can be directly transported to the cathode catalyst layer, leading to enhanced reaction rate. Meanwhile, there is no liquid electrolyte between the cathode and the anode, which can help to improve the energy efficiency of the whole system. The remarkable progress achieved recently points out the way to realize industrially relevant performance. In this review, we focus on the principles in MEA for CO2 RR, focusing on gas diffusion electrodes and ion exchange membranes. Furthermore, anode processes beyond the oxidation of water are considered. Besides, the voltage distribution is scrutinized to identify the specific losses related to the individual components. We also summarize the progress on the generation of different reduced products together with the corresponding catalysts. Finally, the challenges and opportunities are highlighted for future research.

Synthesis of Nitrogen and Phosphorus/Sulfur Co-Doped Carbon Xerogels for the Efficient Electrocatalytic Reduction of p-Nitrophenol.

Carbon xerogels co-doped with nitrogen (N) and phosphorus (P) or sulfur (S) were synthesized and employed as catalysts for the electrocatalytic reduction of p-nitrophenol (p-NP). The materials were prepared by first synthesizing N-doped carbon xerogels (NDCX) via the pyrolysis of organic gels, and then introducing P or S atoms to the NDCX by a vapor deposition method. The materials were characterized by various measurements including X-ray diffraction, N2 physisorption, Transmission electron microscopy, Fourier Infrared spectrometer, and X-ray photoelectron spectra, which showed that N atoms were successfully doped to the carbon xerogels, and the co-doping of P or S atoms affected the existing status of N atoms. Cyclic voltammetry (CV) scanning manifested that the N and P co-doped materials, i.e., P-NDCX-1.0, was the most suitable catalyst for the reaction, showing an overpotential of -0.569 V (vs. Ag/AgCl) and a peak slop of 695.90 µA/V. The material was also stable in the reaction and only a 14 mV shift in the reduction peak overpotential was observed after running for 100 cycles.

Enhanced Exciton Effect and Singlet Oxygen Generation Triggered by Tunable Oxygen Vacancies on Bi2MoO6 for Efficient Photocatalytic Degradation of Sodium Pentachlorophenol.

Construction of the tunable oxygen vacancies (OVs) is widely utilized to accelerate molecular oxygen activation for boosting photocatalytic performance. Herein, the in-situ introduction of OVs on Bi2MoO6 was accomplished using a calcination treatment in an H2/Ar atmosphere. The introduced OVs can not only facilitate carrier separation, but also strengthen the exciton effect, which accelerates singlet oxygen generation through the energy transfer process. Superior carrier separation and abundant singlet oxygen played a crucial role in favoring photocatalytic NaPCP degradation. The optimal BMO-001-300 sample exhibited the fastest NaPCP degradation rate of 0.033 min-1, about 3.8 times higher than that of the pristine Bi2MoO6. NaPCP was effectively degraded and mineralized mainly through dechlorination, dehydroxylation and benzene ring opening. The present work will shed light on the construction and roles of OVs in semiconductor-based photocatalysis and provide a novel insight into ROS-mediated photocatalytic degradation.

Research Progress on Graphitic Carbon Nitride/Metal Oxide Composites: Synthesis and Photocatalytic Applications.

Although graphitic carbon nitride (g-C3N4) has been reported for several decades, it is still an active material at the present time owing to its amazing properties exhibited in many applications, including photocatalysis. With the rapid development of characterization techniques, in-depth exploration has been conducted to reveal and utilize the natural properties of g-C3N4 through modifications. Among these, the assembly of g-C3N4 with metal oxides is an effective strategy which can not only improve electron-hole separation efficiency by forming a polymer-inorganic heterojunction, but also compensate for the redox capabilities of g-C3N4 owing to the varied oxidation states of metal ions, enhancing its photocatalytic performance. Herein, we summarized the research progress on the synthesis of g-C3N4 and its coupling with single- or multiple-metal oxides, and its photocatalytic applications in energy production and environmental protection, including the splitting of water to hydrogen, the reduction of CO2 to valuable fuels, the degradation of organic pollutants and the disinfection of bacteria. At the end, challenges and prospects in the synthesis and photocatalytic application of g-C3N4-based composites are proposed and an outlook is given.

F-Wave Extraction from Single-Lead Electrocardiogram Signals with Atrial Fibrillation by Utilizing an Optimized Resonance-Based Signal Decomposition Method.

(1) Background: A typical cardiac cycle consists of a P-wave, a QRS complex, and a T-wave, and these waves are perfectly shown in electrocardiogram signals (ECG). When atrial fibrillation (AF) occurs, P-waves disappear, and F-waves emerge. F-waves contain information on the cause of atrial fibrillation. Therefore it is essential to extract F-waves from the ECG signal. However, F-waves overlap the QRS complex and T-waves in both the time and frequency domain, causing this matter to be a difficult one. (2) Methods: This paper presents an optimized resonance-based signal decomposition method for detecting F-waves in single-lead ECG signals with atrial fibrillation (AF). It represents the ECG signal utilizing morphological component analysis as a linear combination of a finite number of components selected from the high-resonance and low-resonance dictionaries, respectively. The linear combination of components in the low-resonance dictionary reconstructs the oscillatory part (F-wave) of the ECG signal. In contrast, the linear combination of components in the high-resonance dictionary reconstructs the transient components part (QRST wave). The tunable Q-factor wavelet transform generates the high and low resonance dictionaries, with a high Q-factor producing a high resonance dictionary and a low Q-factor producing a low resonance dictionary. The different Q-factor settings affect the dictionaries' characteristics, hence the F-wave extraction. A genetic algorithm was used to optimize the Q-factor selection to select the optimal Q-factor. (3) Results: The presented method helps reduce RMSE between the extracted and the simulated F-waves compared to average beat subtraction (ABS) and principal component analysis (PCA). According to the amplitude of the F-wave, RMSE is reduced by 0.24-0.32. Moreover, the dominant frequency of F-waves extracted by the presented method is clearer and more resistant to interference. The presented method outperforms the other two methods, ABS and PCA, in F-wave extraction from AF-ECG signals with the ventricular premature heartbeat. (4) Conclusion: The proposed method can potentially improve the accuracy of F-wave extraction for mobile ECG monitoring equipment, especially those with fewer leads.

CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive.

(1) Background and objective: Cardiovascular disease is one of the most common causes of death in today's world. ECG is crucial in the early detection and prevention of cardiovascular disease. In this study, an improved deep learning method is proposed to diagnose abnormal and normal ECG accurately. (2) Methods: This paper proposes a CNN-FWS that combines three convolutional neural networks (CNN) and recursive feature elimination based on feature weights (FW-RFE), which diagnoses abnormal and normal ECG. F1 score and Recall are used to evaluate the performance. (3) Results: A total of 17,259 records were used in this study, which validated the diagnostic performance of CNN-FWS for normal and abnormal ECG signals in 12 leads. The experimental results show that the F1 score of CNN-FWS is 0.902, and the Recall of CNN-FWS is 0.889. (4) Conclusion: CNN-FWS absorbs the advantages of convolutional neural networks (CNN) to obtain three parts of different spatial information and enrich the learned features. CNN-FWS can select the most relevant features while eliminating unrelated and redundant features by FW-RFE, making the residual features more representative and effective. The method is an end-to-end modeling approach that enables an adaptive feature selection process without human intervention.

Unique insights into photocatalytic VOCs oxidation over WO3/carbon dots nanohybrids assisted by water activation and electron transfer at interfaces.

Internal electric field (IEF) at heterojunction interfaces can separate photoexcited charge carriers and promote photocatalytic performance. Here we have modified WO3 nanoplates with carbon dots (CDs) and constructed an interfacial IEF directing from CDs to WO3 with assistance of their remarkably different work functions. Such electric field drove photoexcited electrons to transport towards CDs and retained photoexcited holes to stay at WO3, achieving electron/hole spatial separation. H2O preferred chemisorption on the five-coordinated W atoms of WO3 with an elongated H-O bond and bent H-O-H angle, which allowed the activation of H2O and favorable production of ·OH radicals. The WO3/CDs (WC1) showed a superior photocatalytic activity for visible-light photooxidation of HCHO and CH3COCH3 with CO2 production rate of 411 and 188 µmol g-1 h-1, respectively, outperforming most of WO3-based photocatalysts. The enhanced photocatalytic performance correlated with the IEF-induced charge separation, favorable ·OH production and VOCs chemisorption. Our work confirms the role of CDs cocatalyst in the photocatalytic oxidation of VOCs, which will inspire enthusiasm to develop more advanced heterojunction photocatalysts involving carbon nanomaterials.

Atomic layer deposition of silica to improve the high-temperature hydrothermal stability of Cu-SSZ-13 for NH3 SCR of NOx.

The improvement of stability is a crucial and challenging issue for industrial catalyst, which affects not only the service time but also the cost of catalyst. This is especially prominent for that applied in harsh environment atmospheres, such as the exhaust of diesel vehicles. Herein, we reported a new strategy to improve the high-temperature hydrothermal stability of Cu-SSZ-13, which is a promising catalyst for the treatment of exhaust emitted from diesel vehicles through the NH3-SCR NOx route. Different from that reported in literature, we managed to improve the high-temperature hydrothermal stability of Cu-SSZ-13 by coating the surface with a nanolayer of stable SiO2 material using the atomic layer deposition (ALD) method. The coating of SiO2 layers effectively suppressed the leaching of alumina from the SSZ-13 molecular sieve even after the hydrothermal aging at 800 °C for 16 h with 12.5% water in air. Meanwhile, the ultra-thin SiO2 nanolayer does not block the pores of zeolites and affect the catalytic activity of Cu-SSZ-13 contribute to the superiority of the ALD technology.

[Atrial fibrillation diagnosis algorithm based on improved convolutional neural network].

Atrial fibrillation (AF) is a common arrhythmia, which can lead to thrombosis and increase the risk of a stroke or even death. In order to meet the need for a low false-negative rate (FNR) of the screening test in clinical application, a convolutional neural network with a low false-negative rate (LFNR-CNN) was proposed. Regularization coefficients were added to the cross-entropy loss function which could make the cost of positive and negative samples different, and the penalty for false negatives could be increased during network training. The inter-patient clinical database of 21 077 patients (CD-21077) collected from the large general hospital was used to verify the effectiveness of the proposed method. For the convolutional neural network (CNN) with the same structure, the improved loss function could reduce the FNR from 2.22% to 0.97% compared with the traditional cross-entropy loss function. The selected regularization coefficient could increase the sensitivity (SE) from 97.78% to 98.35%, and the accuracy (ACC) was 96.62%, which was an increase from 96.49%. The proposed algorithm can reduce the FNR without losing ACC, and reduce the possibility of missed diagnosis to avoid missing the best treatment period. Meanwhile, it provides a universal loss function for the clinical auxiliary diagnosis of other diseases.

Versatile 3D reduced graphene oxide/poly(amino-phosphonic acid) aerogel derived from waste acrylic fibers as an efficient adsorbent for water purification.

The fabrication of multifunctional materials to remove soluble heavy metal ions and dyes, as well as insoluble oils from waste water is urgently required, yet remains a daunting challenge because of difficulty in controlling their structure and property to satisfy various demands. Herein, for the first time, novel 3D reduced graphene oxide/poly(amino-phosphonic acid) (PAPA) aerogels (rGO/PAPAs) with different PAPA content were developed by solvothermal reduction of the graphene oxide and cross-linking with PAPA chain, and subsequently employed as versatile adsorbent for the removal of complex pollutants such as Cr(III) ion, methylene blue (MB) dye and various kinds of organic solvents from water. Benefiting from the synergistic effect of the reduced graphene oxide (rGO) sheet and PAPA component, as well as its unique 3D structure, the resultant aerogel (rGO/PAPA-2) gained amphiphilic, ultralight, and multifunctional properties. Thus, it showed a fast adsorption rate (within 15 min) and high adsorption capacity (up to 327.1 mg/g) for Cr(III) ion at an optimal pH of 5.5 due to its unique 3D network structure with abundant amino-phosphonic acid functional groups. The uptake of Cr(III) by rGO/PAPA-2 was fitted well with the Langmuir isotherm and pseudo-second-order kinetic model. The adsorption mechanism of Cr(III) onto rGO/PAPA-2 can be attributed to electrostatic attraction and surface complexation with APA groups. In addition, the rGO/PAPA-2 displayed an excellent adsorption performance for MB (694.5 mg/g) and several organic solvents (83.2 to 254.3 g/g). Moreover, the rGO/PAPA-2 exhibited a good regeneration (around 99%) and satisfactory recovery abilities for the tested adsorbates. Notably, PAPA chains can be easily prepared from waste acrylic fibers, making it become a cost effective but versatile candidate to prepare new material. Therefore, this work provides a new design strategy to fabricate the rGO/PAPA-2 aerogel with great prospect for sophisticated industrial wastewater cleanup.

Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings.

As one of the key parts of rotary machine, the fault diagnosis and running condition monitoring of rolling bearings are of great importance for normal working and safe production of rotary machine. However, the traditional diagnosis approaches merely count on artificial feature extraction and domain expertise. Meanwhile, the existing convolutional neural networks (CNNs) have the problem of low fault recognition rates. This paper proposes a novel convolutional neural network with one-dimensional structure (ODCNN) for the automatical fault diagnosis of rolling bearings, which adopts six sets of convolutional and max-pooling layers to extract signal features and applies a flattening convolutional layer followed by two fully-connected layers for feature classification. The architectures of one-dimensional LeNet-5, AlexNet, and the proposed ODCNN are illustrated in detail, followed by the obtaining of training and testing samples, which is pre-processed by overlapping the vibration signals of rolling bearings. Finally, the classification experiment is carried out. The experimental results show that the ODCNN has higher fault diagnosis rates and can achieve high accuracy with load variant. Additionally, the extracted features of three CNNs are visualized, which illustrate that the new CNN has a better classification capacity.

In Situ Construction of Pt-Ni NF@Ni-MOF-74 for Selective Hydrogenation of p-Nitrostyrene by Ammonia Borane.

Pt-Ni nanoframes (Pt-Ni NFs) exhibit outstanding catalytic properties for several reactions owing to the large numbers of exposed surface active sites, but its stability and selectivity need to be improved. Herein, an in situ method for construction of a core-shell structured Pt-Ni NF@Ni-MOF-74 is reported using Pt-Ni rhombic dodecahedral as self-sacrificial template. The obtained sample exhibits not only 100 % conversion for the selective hydrogenation of p-nitrostyrene to p-aminostyrene conducted at room temperature, but also good selectivity (92 %) and high stability (no activity loss after fifteen runs) during the reaction. This is attributed to the Ni-MOF-74 shell in situ formed in the preparation process, which can stabilize the evolved Pt-Ni NF and donate electrons to the Pt metals that facilitate the preferential adsorption of electrophilic NO2 group. This study opens up new vistas for the design of highly active, selective, and stable noble-metal-containing materials for selective hydrogenation reactions.

VR motion sickness recognition by using EEG rhythm energy ratio based on wavelet packet transform.

BACKGROUND AND OBJECTIVES: Virtual reality motion sickness (VRMS) is one of the main factors hindering the development of VR technology. At present, the VRMS recognition methods using electroencephalogram (EEG) signals have poor applicability to multiple subjects. METHODS: Aiming at this dilemma, the wavelet packet transform (WPT), was used to propose a feature extraction method for EEG rhythm energy ratios of delta (δ), theta (θ), alpha (α), and beta (ß) in this research. Moreover, VRMS was recognized by combining k-Nearest Neighbor classifier (k-NN), support vector machine (SVM) with polynomial kernel (polynomial-SVM) and radial basis function kernel (RBF-SVM), respectively. The method is that the raw EEG signals were de-noised by an elliptical band-pass filter and segmented by a fixed window, 7-level db4 WPT was performed on each EEG segment, and the wavelet packet energy ratios of delta, theta, alpha and beta rhythms from FP1, FP2, C3, C4, P3, P4, O1 and O2 channels were calculated and combined to form feature vectors for recognizing VRMS. RESULTS: Under the condition of 4-s window size, the average VRMS recognition accuracy of polynomial-SVM for the single subject was 92.85%, and the VRMS recognition accuracy of 18 subjects was about 79.25%. CONCLUSIONS: Compared with other VRMS recognition methods, this method does not only have a higher recognition accuracy to a single subject, but also have better applicability to multiple subjects. Meanwhile, when using the EEG four rhythm energy ratios of FP1, FP2, C3, C4, P3, P4, O1 and O2 channels as feature vectors, the polynomial-SVM achieved better VRMS recognition performance than the k-NN and RBF-SVM.