Glucagon-like peptide-1 receptor agonists for the management of diabetic peripheral neuropathy

Diabetes mellitus is a prevalent chronic disease characterized by hyperglycemia. Diabetic peripheral neuropathy (DPN) is one of the complications of diabetes mellitus and is caused by neuron injury induced by hyperglycemic circumstances. The incidence of DPN varies among different countries and regions, ranging from nearly 20% to over 70%. Patients with DPN may encounter symmetric pain or discomfort of the extremes, leading to reduced quality of life and even amputation. The pharmacological management for painful DPN mainly includes antidepressants due to their analgesic effects. Nevertheless, effective therapies to impact the pathogenesis and progression of DPN are lacking. Glucagon-like peptide-1 receptor (GLP-1R) agonists show efficacy in controlling blood glucose and serve as a treatment modality for diabetes mellitus. In recent years, evidence has been proposed that GLP-1R agonists exert neuroprotective effects through modulating inflammation, oxidative stress, and mitochondrial dysfunction. On the other hand, clinical evidence on the potential of GLP-1R agonists for treating DPN is still controversial and limited. This narrative review summarizes the preclinical and clinical studies investigating the capacity of GLP-1R agonists as therapeutic agents for DPN

Univariable and multivariable Mendelian randomization investigating the effects of telomere length on the risk of adverse pregnancy outcomes

BackgroundNumerous observational studies have revealed a correlation between telomere length (TL) and adverse pregnancy outcomes (APOs). However, the impacts of TL on APOs are still unclear.MethodsMendelian randomization (MR) was carried out using summary data from genome-wide association studies (GWAS). Inverse variance weighted (IVW) was employed as the primary analysis to explore the causal relationship between TL and APOs. The exposure data came from a GWAS dataset of IEU analysis of the United Kingdom Biobank phenotypes consisting of 472,174 European participants. Summary-level data for five APOs were obtained from the GWAS datasets of the FinnGen consortium. We also performed multivariate MR (MVMR), adjusting for smoking, alcohol intake, body mass index (BMI), and number of live births. In addition, we conducted a series of rigorous analyses to further examine the validity of our MR findings.ResultsAfter Bonferroni correction and rigorous quality control, univariable MR (UVMR) demonstrated that a shorter TL was significantly associated with an increased risk of spontaneous abortion (SA) (odds ratio [OR]: 0.815; 95% confidence interval [CI]: 0.714–0.930; P = 0.002) and preterm birth (PTB) (OR: 0.758; 95% CI: 0.632-0.908; P = 0.003) in the IVW model. There was a nominally significant relationship between TL and preeclampsia (PE) in the IVW model (OR: 0.799; 95% CI: 0.651-0.979; P = 0.031). However, no significant association was found between TL and gestational diabetes mellitus (GDM) (OR: 0.950; 95% CI: 0.804-1.122; P = 0.543) or fetal growth restriction (FGR) (OR: 1.187; 95% CI: 0.901-1.565; P = 0.223) among the five statistical models. Furthermore, we did not find a significant causal effect of APOs on TL in the reverse MR analysis. MVMR analysis showed that the causal effects of TL on SA remained significant after accounting for smoking, alcohol intake, BMI, and number of live births.ConclusionOur MR study provides robust evidence that shorter telomeres were associated with an increased risk of SA. Further work is necessary to investigate the potential mechanisms. UVMR and MVMR findings showed limited evidence that TL affects the risk of PTB, PE, GDM, and FGR, illustrating that the outcomes of previous observational studies may have been confounded

Directional correlation analysis of local Haar binary pattern for text detection

Two main restrictions exist in state-of-the-art text detection algorithms: 1. Illumination variance; 2. Text-background contrast variance. This paper presents a robust text characterization approach based on local Haar binary pattern (LHBP) to address these problems. Based on LHBP, a coarse-to-fine detection framework is presented to precisely locate text lines in scene images. Firstly, threshold-restricted local binary pattern is extracted from high-frequency coefficients of pyramid Haar wavelet. It preserves and uniforms inconsistent text-background contrasts while filtering gradual illumination variations. Subsequently, we propose a directional correlation analysis (DCA) approach to filter non-directional LHBP regions for locating candidate text regions. Finally, using LHBP histogram, an SVM-based post-classification is presented to refine detection results. Experimental results on ICDAR 03 demonstrate the effectiveness and robustness of our proposed method. Index Terms—Text detection, pyramid wavelet, local binary pattern, directional correlation analysis, SV

Melanocortin Receptor 4 (MC4R) Signaling System in Nile Tilapia

The melanocortin receptor 4 (MC4R) signaling system consists of MC4R, MC4R ligands [melanocyte-stimulating hormone (MSH), adrenocorticotropin (ACTH), agouti-related protein (AgRP)], and melanocortin-2 receptor accessory protein 2 (MRAP2), and it has been proposed to play important roles in feeding and growth in vertebrates. However, the expression and functionality of this system have not been fully characterized in teleosts. Here, we cloned tilapia MC4R, MRAP2b, AgRPs (AgRP, AgRP2), and POMCs (POMCa1, POMCb) genes and characterized the interaction of tilapia MC4R with MRAP2b, AgRP, α-MSH, and ACTH in vitro. The results indicate the following. (1) Tilapia MC4R, MRAP2b, AgRPs, and POMCs share high amino acid identity with their mammalian counterparts. (2) Tilapia MRAP2b could interact with MC4R expressed in CHO cells, as demonstrated by Co-IP assay, and thus decrease MC4R constitutive activity and enhance its sensitivity to ACTH1-40. (3) As in mammals, AgRP can function as an inverse agonist and antagonist of MC4R, either in the presence or absence of MRAP2b. These data, together with the co-expression of MC4R, MRAP2b, AgRPs, and POMCs in tilapia hypothalamus, suggest that as in mammals, ACTH/α-MSH, AgRP, and MRAP2 can interact with MC4R to control energy balance and thus play conserved roles in the feeding and growth of teleosts

Analyzing, Optimizing and Synthesizing Scenes by Reasoning About Relationships Between Objects

3D scene modeling has many applications, including virtual social worlds, massively multiplayer online games, and production of catalog images. However, scene modeling is extremely tedious and challenging, due to the requirement of realism and the involvement of large numbers of objects. Therefore, automated methods for 3D scene modeling are needed. A promising approach is to first analyze the existing 3D scenes, such as the ones in online repositories (e.g. Trimble 3D Warehouse), and then use the knowledge obtained from the analysis step to create new scenes. Although a significant amount of research has focused on developing algorithms for scene analysis and scene modeling, these processes still remain challenging for two reasons. First, the large variability of 3D scenes makes it hard to capture the commonality among scenes for scene analysis. Second, the highly constrained space of realistic 3D scenes makes it challenging to automatically create satisfactory scenes. This dissertation pushes the limits of existing efforts on analyzing, synthesizing, and optimizing 3D scenes by reasoning about relationships between objects. First, it describes an algorithm that segments and annotates 3D scenes by considering relationships between objects in a hierarchical representation. Second, it describes a tool that optimizes a 3D scene to produce compositions by considering relationships between objects in the image space and the scene space. Finally, it focuses on style compatibility between objects, which is a relationship that has never been considered in previous scene modeling tools, and it presents a method for learning to predict the stylistic compatibility between 3D furniture models from different object classes. In this dissertation, we find that relationships between objects are comparatively stable across scenes, and that they can serve as a strong cue for inferring annotation and segmentation of scenes. Furthermore, we also find that modeling relationships between objects helps ensure the realism of synthesized scenes. Therefore, reasoning about relationships between objects greatly facilitates scene analysis and synthesis

Style compatibility for 3D furniture models

This paper presents a method for learning to predict the stylistic compatibility between 3D furniture models from different object classes: e.g., how well does this chair go with that table? To do this, we collect relative assessments of style compatibility using crowdsourcing. We then compute geometric features for each 3D model and learn a mapping of them into a space where Euclidean distances represent style incompatibility. Motivated by the geometric subtleties of style, we introduce part-aware geometric feature vectors that characterize the shapes of different parts of an object separately. Motivated by the need to compute style compatibility between different object classes, we introduce a method to learn object class-specific mappings from geometric features to a shared feature space. During experiments with these methods, we find that they are effective at predicting style compatibility agreed upon by people. We find in user studies that the learned compatibility metric is useful for novel interactive tools that: 1) retrieve stylistically compatible models for a query, 2) suggest a piece of furniture for an existing scene, and 3) help guide an interactive 3D modeler towards scenes with compatible furniture

Characterization of Combined Effects of Urban Built-Up and Vegetated Areas on Long-Term Urban Heat Islands in Beijing

With the development of urbanization and industrialization, megacities have experienced more severe surface urban heat island (SUHI) effects. Land surface temperatures (LSTs) are retrieved; spatial distribution of temperature is characterized, and the relationship among temperatures or SUHIs and land-use and land cover (LULC) in Beijing City are discussed. The changing LSTs in Beijing, from 1990 to 2017, were calculated by a radiative transfer equation and mono-window algorithm. To estimate the effect of SUHI, Landsat-8 Thermal Infrared Sensor (TRIS) and Landsat-5 Thematic Mapper (TM) data were selected. There is an increasing trend toward high LSTs for different LULC types. The connection with building and vegetation density is analyzed. Results indicate that for every 1% increase in the density of buildings, the increase in amplitude of temperature in 2017 was twice as large as it was in 1995 for the study area. In terms of normalized difference vegetation index (NDVI) values, the decrease in amplitude of LST was 10 times that of the year 1995, where there is only a slight increase in the NDVI values of the area