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Non-precious metal catalysts are well investigated in electrocatalysis. Fe and N co-doped carbon (Fe-N-C) catalysts have drawn great attention due to their low cost and good performance in the oxygen reduction reaction (ORR) and recently in the oxygen evolution reaction (OER). Based on the recent advances of a variety of physical characterization techniques, more information about the chemical environment of the catalytic active sites has been acquired. However, due to the complexity of the catalytic material and process, the real active structures are still controversial. In this work, several active sites are proposed for Fe-N-C catalysts (L-FeNx, x = 2 or 4, L = nothing, O or OH) and their performance in electrochemical ORR and the OER are investigated. The computations are based on density functional theory (DFT), including Van der Waals interaction and solvation effect with an implicit electrolyte model. Calculations indicate that the catalytic activity of the Fe centers depends strongly on the N coordination number and on the presence of extra ligands like OH group. In particular, HO-FeN2, but not FeN4, appears as the most active site. Scaling relations are obtained by connecting the free energy of potential-determining steps with the adsorption free energy of intermediates. Furthermore, three promising active sites suggested from scaling relations are studied by the more elaborate surface charging approach, which includes the influence of the applied potential and the electrolyte. The results show that the specific treatment of the influence of the applied potential has a minor influence at low potential, which is the case for the ORR, but a major influence at higher potential, as for the OER, changing the calculated overpotential by up to 0.34 V.

In addition to controlling smooth muscle tone in coronary vessels, endothelial cells also influence subjacent cardiomyocyte growth. Because heparanase, with exclusive expression in endothelial cells, enables extracellular matrix remodeling, angiogenesis, metabolic reprogramming, and cell survival, it is conceivable that it could also encourage development of cardiac hypertrophy. Global heparanase overexpression resulted in physiologic cardiac hypertrophy, likely an outcome of HSPG clustering and activation of hypertrophic signaling. The heparanase autocrine effect of releasing neuregulin-1 could have also contributed to this overexpression. Hyperglycemia induced by streptozotocin-induced diabetes sensitized the heart to flow-induced release of heparanase and neuregulin-1. Despite this excess secretion, progression of diabetes caused significant gene expression changes related to mitochondrial metabolism and cell death that led to development of pathologic hypertrophy and heart dysfunction. Physiologic cardiac hypertrophy was also observed in rats with cardiomyocyte-specific vascular endothelial growth factor B overexpression. When perfused, hearts from these animals released significantly higher amounts of both heparanase and neuregulin-1. However, subjecting these animals to diabetes triggered robust transcriptome changes related to metabolism and a transition to pathologic hypertrophy. Our data suggest that in the absence of mechanisms that support cardiac energy generation and prevention of cell death, as seen after diabetes, there is a transition from physiologic to pathologic cardiac hypertrophy and a decline in cardiac function. Article Highlights: Endothelial cells have been implicated in physiologic cardiac hypertrophy. We examined the role of endothelial heparanase in this process and how diabetes affects heparanase function. Heparanase overexpression resulted in physiologic cardiac hypertrophy, an outcome of HSPG clustering and direct and indirect activation of hypertrophic signaling. Hyperglycemia sensitized the heart to flow-induced heparanase release, and progression of diabetes caused gene expression changes related to mitochondrial metabolism and apoptosis. With diabetes and its associated metabolic inflexibility and cell death, actions of heparanase are negated, leading to pathologic cardiac hypertrophy and heart dysfunction. These results can contribute to mechanism-directed therapeutics for diabetic cardiomyopathy. [ABSTRACT FROM AUTHOR]

The preparation of nanozymes with high specific activity is highly important for various applications. However, only a few nanozymes have specific activities comparable to natural enzymes. Herein, novel Pt-on-Rh hollow nanorods (PtRh HNRs) were developed, in which surface Pt exhibited adjustable dispersity and interior Rh served as the support. The optimized PtRh HNRs demonstrated high-performance peroxidase (POD)-like activity, with a specific activity as high as 1352 U mg−1, which was 3.86 times that of their monometallic Pt counterparts. Density functional theory (DFT) calculations illustrated that the presence of Rh decreased the energy barrier of the rate-determining step. When PtRh HNRs were used as nanozymes in the colorimetric detection of hydrogen peroxide (H2O2) and ascorbic acid (AA), the limits of detection (LODs) were as low as 9.97 μM and 0.039 μM, respectively. The current work highlights a facile and powerful strategy for manufacturing nanozymes with high specific activity and demonstrates that the prepared PtRh HNRs have the potential for analysis and determination. [ABSTRACT FROM AUTHOR]

The BeiDou Global Navigation Satellite System (BDS-3) provides real-time precise point positioning (PPP) service via B2b signals, offering real-time decimeter-level positioning for users in China and surrounding areas. However, common interruptions and outliers in PPP-B2b services arise due to factors such as the Geostationary Orbit (GEO) satellite "south wall effect", Issue of Data (IOD) matching errors, and PPP-B2b signal broadcast priorities, posing challenges to continuous high-precision positioning. This study meticulously examines the completeness, continuity, and jumps in PPP-B2b orbit and clock correction using extensive observational data. Based on this analysis, a two-step method for detecting outliers in PPP-B2b orbit and clock corrections is devised, leveraging epoch differences and median absolute deviation. Subsequently, distinct prediction methods are developed for BDS-3 and GPS orbit and clock corrections. Results from simulated and real-time dynamic positioning experiments indicate that predicted corrections can maintain the same accuracy as normal correction values for up to 10 min and sustain decimeter-level positioning accuracy within 30 min. The adoption of predicted correction values significantly enhances the duration of sustaining real-time PPP during signal interruptions. [ABSTRACT FROM AUTHOR]

BackgroundEarly gastric cancer (EGC) has a high survival rate, but it is difficult to diagnosis. Recently, artificial intelligence (AI) based on deep convolutional neural network (DCNN) has made significant progress in the field of gastroenterology. The purpose of this study was to establish a DCNN assist system to improve the detection of EGC.Methods3400 EGC and 8600 benign images were collected to train the DCNN to detect EGC. Subsequently, its diagnostic ability was compared to that of endoscopists using an independent internal test set (ITS, including 1289 images) and an external test set (ETS, including 542 images) come from three digestive center.ResultsThe diagnostic time of DCNN and endoscopists were 0.028s, 8.05 ± 0.21s, 7.69 ± 0.25s in ITS, and 0.028s, 7.98 ± 0.19s, 7.50 ± 0.23s in ETS, respectively. In ITS, the diagnostic sensitivity and accuracy of DCNN are 88.08%(95% confidence interval,95%CI,85.24%-90.44%), 88.60% (95%CI,86.74%-90.22%), respectively. In ETS, the diagnostic sensitivity and accuracy are 92.08% (95%CI, 87.91%- 94.94%),92.07%(95%CI, 89.46%-94.08%),respectively. DCNN outperformed all endoscopists in ETS, and had a significantly higher sensitivity than the junior endoscopists(JE)(by18.54% (95%CI, 15.64%-21.84%) in ITS, also higher than JE (by21.67%,95%CI, 16.90%-27.32%) and senior endoscopists (SE) (by2.08%, 95%CI, 0.75%-4.92%)in ETS. The accuracy of DCNN model was higher (by10.47%,95%CI, 8.91%-12.27%) than that of JE in ITS, and also higher (by14.58%,95%CI, 11.84%-17.81%; by 1.94%,95%CI,1.25%-2.96%, respectively) than JE and SE in ETS.ConclusionThe DCNN can detected more EGC images in a shorter time than the endoscopists. It will become an effective tool to assist in the detection of EGC in the near future.