The role of serum magnesium in the prediction of acute kidney injury after total aortic arch replacement: A prospective observational study.

Background: Considerable morbidity and death are associated with acute kidney damage (AKI) following total aortic arch replacement (TAAR). The relationship between AKI following TAAR and serum magnesium levels remains unknown. The intention of this research was to access the predictive value of serum magnesium levels on admission to the Cardiovascular Surgical Intensive Care Unit (CSICU) for AKI in patients receiving TAAR.

Predictive Value of Serum Soluble ST2 in Adult Patients Undergoing Cardiac Surgery for Acute Kidney Injury.

Introduction: Cardiac surgery is related to an increased risk of postoperative acute kidney injury (AKI). Serum soluble ST2 (sST2) is highly predictive of several cardiovascular diseases and may also be involved in renal injury. This study explored the relationship between serum sST2 levels measured at intensive care unit (ICU) admission and the development of AKI after cardiac surgery.

Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type A aortic dissection.

Background: Severe acute kidney injury (AKI) after total aortic arch replacement (TAAR) is related to adverse outcomes in patients with acute type A aortic dissection (ATAAD). However, the early prediction of severe AKI remains a challenge. This study aimed to develop a novel model to predict severe AKI after TAAR in ATAAD patients using machine learning algorithms.

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.

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.

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

We present a novel approach for the growth of bimetallic silicate onto ultrathin graphene, followed by 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 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.

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.

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.

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 CO Reduction: Principle and Application.

Electrocatalytic CO reduction reaction (CO RR) in membrane electrode assembly (MEA) systems is a promising technology. Gaseous CO 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.

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, N 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.

Enhanced Exciton Effect and Singlet Oxygen Generation Triggered by Tunable Oxygen Vacancies on BiMoO 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 BiMoO was accomplished using a calcination treatment in an H/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.

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

Although graphitic carbon nitride (g-CN) 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-CN through modifications. Among these, the assembly of g-CN 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-CN owing to the varied oxidation states of metal ions, enhancing its photocatalytic performance.

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.

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.

Unique insights into photocatalytic VOCs oxidation over WO/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 WO nanoplates with carbon dots (CDs) and constructed an interfacial IEF directing from CDs to WO 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 WO, achieving electron/hole spatial separation.

Atomic layer deposition of silica to improve the high-temperature hydrothermal stability of Cu-SSZ-13 for NH SCR of NO.

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 NH-SCR NO route.

[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.

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.

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.

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.

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 (β) in this research.

A novel IRBF-RVM model for diagnosis of atrial fibrillation.

Background And Objective: Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learning method is proposed for rapid modeling and accurate diagnosis of AF.

Methods: This paper presents a novel IRBF-RVM model that combines the integrated radial basis function (IRBF) and relevance vector machine (RVM), which is utilized for the diagnosis of AF.

Correction: CoO-nanoparticle-entrapped nitrogen and boron codoped mesoporous carbon as an efficient electrocatalyst for hydrogen evolution.

Correction for 'CoO-nanoparticle-entrapped nitrogen and boron codoped mesoporous carbon as an efficient electrocatalyst for hydrogen evolution' by Duihai Tang et al., Dalton Trans., 2019, DOI: 10.