Effect of Danhong injection on heart failure in rats evaluated by metabolomics

BackgroundHeart failure (HF) is characterized by reduced ventricular filling or ejection function due to organic or non-organic cardiovascular diseases. Danhong injection (DHI) is a medicinal material used clinically to treat HF for many years in China. Although prior research has shown that Danhong injection can improve cardiac function and structure, the biological mechanism has yet to be determined.MethodsSerum metabolic analysis was conducted via ultra-high-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry (UHPLC-QE/MS) to explore underlying protective mechanisms of DHI in the transverse aortic constriction (TAC)-induced heart failure. Multivariate statistical techniques were used in the research, such as unsupervised principal component analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). MetaboAnalyst and Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed to pinpoint pertinent metabolic pathways.ResultsAfter DHI treatment, cardiac morphology and function as well as the metabolism in model rats were improved. We identified 17 differential metabolites and six metabolic pathways. Two biomarkers, PC(18:3(6Z,9Z,12Z)/24:0) and L-Phenylalanine, were identified for the first time as strong indicators for the significant effect of DHI.ConclusionThis study revealed that DHI could regulate potential biomarkers and correlated metabolic pathway, which highlighted therapeutic potential of DHI in managing HF

A Survey of Deep Learning-Based Object Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.Comment: 30 pages,12 figure

A Dual-Band Printed End-Fire Antenna with DSPSL Feeding

A novel dual-band printed end-fired antenna with double-sided parallel-strip line (DSPSL) feeding is presented. The DSPSL acts in wideband transition using balanced transmission. Two different modes of the parasitic patches allow the antenna to work in different bands. The printed antenna is designed as a quasi-Yagi structure to achieve directivity in the lower band, and the parallel rectangular patches serve as the parasitic director. These patches act as radiation patches with end-fire direction characteristics in the upper band. The measured bandwidths were 18.3% for the lower frequency band (2.28–2.74 GHz) and 12.6% for the upper frequency band (5.46–6.2 GHz)

Evolution of the class C GPCR Venus flytrap modules involved positive selected functional divergence

<p>Abstract</p> <p>Background</p> <p>Class C G protein-coupled receptors (GPCRs) represent a distinct group of the GPCR family, which structurally possess a characteristically distinct extracellular domain inclusive of the Venus flytrap module (VFTM). The VFTMs of the class C GPCRs is responsible for ligand recognition and binding, and share sequence similarity with bacterial periplasmic amino acid binding proteins (PBPs). An extensive phylogenetic investigation of the VFTMs was conducted by analyzing for functional divergence and testing for positive selection for five typical groups of the class C GPCRs. The altered selective constraints were determined to identify the sites that had undergone functional divergence via positive selection. In order to structurally demonstrate the pattern changes during the evolutionary process, three-dimensional (3D) structures of the GPCR VFTMs were modelled and reconstructed from ancestral VFTMs.</p> <p>Results</p> <p>Our results show that the altered selective constraints in the VFTMs of class C GPCRs are statistically significant. This implies that functional divergence played a key role in characterizing the functions of the VFTMs after gene duplication events. Meanwhile, positive selection is involved in the evolutionary process and drove the functional divergence of the VFTMs. Our results also reveal that three continuous duplication events occurred in order to shape the evolutionary topology of class C GPCRs. The five groups of the class C GPCRs have essentially different sites involved in functional divergence, which would have shaped the specific structures and functions of the VFTMs.</p> <p>Conclusion</p> <p>Taken together, our results show that functional divergence involved positive selection and is partially responsible for the evolutionary patterns of the class C GPCR VFTMs. The sites involved in functional divergence will provide more clues and candidates for further research on structural-function relationships of these modules as well as shedding light on the activation mechanism of the class C GPCRs.</p

High‐Efficiency Graphene‐Oxide/Silicon Solar Cells with an Organic‐Passivated Interface

A breakthrough in graphene-oxide/silicon heterojunction solar cells is presented in which edge-oxidized graphene and an in-plane charge transfer dopant (Nafion) are combined to form a high-quality passivating contact scheme. A graphene oxide (GO):Nafion ink is developed and an advanced back-junction GO:Nafion/n-Si solar cell with a high-power conversion efficiency (18.8%) and large area (5.5 cm2) is reported. This scalable solution-based processing technique has the potential to enable low-cost carbon/silicon heterojunction photovoltaic devices

Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study

The purpose of this study is to evaluate the feasibility and patient acceptability of a novel artificial intelligence (AI)-based diabetic retinopathy (DR) screening model within endocrinology outpatient settings. Adults with diabetes were recruited from two urban endocrinology outpatient clinics and single-field, non-mydriatic fundus photographs were taken and graded for referable DR (&thinsp;&ge;&thinsp;pre-proliferative DR). Each participant underwent; (1) automated screening model; where a deep learning algorithm (DLA) provided real-time reporting of results; and (2) manual model where retinal images were transferred to a retinal grading centre and manual grading outcomes were distributed to the patient within 2 weeks of assessment. Participants completed a questionnaire on the day of examination and 1-month following assessment to determine overall satisfaction and the preferred model of care. In total, 96 participants were screened for DR and the mean assessment time for automated screening was 6.9&thinsp;minutes. Ninety-six percent of participants reported that they were either satisfied or very satisfied with the automated screening model and 78% reported that they preferred the automated model over manual. The sensitivity and specificity of the DLA for correct referral was 92.3% and 93.7%, respectively. AI-based DR screening in endocrinology outpatient settings appears to be feasible and well accepted by patients