Search

Gastrointestinal adverse drug reactions (GADRs) of direct-acting antiviral agents (DAAs) in patients with chronic hepatitis C are underestimated.This study aimed to comprehensively evaluate the gastrointestinal safety of DAAs in patients with chronic hepatitis C.The US FDA Adverse Event Reporting System database was searched for GADR cases reported from 01 to 2012 to 30 September 2021. Twelve DAA types used for hepatitis C virus were included. The top 30 GADRs were assessed based on the use of DAAs, number of cases, and clinical features. A case-non-case disproportionality approach was used to confirm pharmacovigilance signals, whereby reporting odds ratios (ROR) with 95% CI were calculated.Nausea (70.01/1000), diarrhoea (39.10/1000), and vomiting (31.68/1000) accounted for the highest number of cases. The pooled median time-to-onset of the top 30 GADRs was 13 days (Q1-Q3: 2-38) and the proportion of drug discontinuation was 19.17%. The highest number of DAA-related cases involved ledipasvir/sofosbuvir (21.86%), sofosbuvir/velpatasvir (21.77%), and sofosbuvir (13.41%). When DAAs were considered as a class drug, after adjusting for age, sex, concomitant diseases and drugs that potentially induced GADRs, significant RORs for specific GADRs were noted, including abdominal discomfort (1.62, 95% CI 1.32-1.99), constipation (1.54, 95% CI 1.26-1.89), dyspepsia (1.25, 95% CI 1.01-1.55), abdominal distension (1.36, 95% CI 1.05-1.75), faeces discoloured (1.77, 95% CI 1.15-2.73), and gastric ulcer (2.37, 95% CI 1.28-4.41).Clinicians should have a deeper understanding of GADRs to improve the gastrointestinal tolerance of patients with chronic hepatitis C.

An association between tumor necrosis factor (TNF)-α inhibitors and hypoglycemia has been detected in a few case reports and small case series; however, no relevant pharmacovigilance data have been published yet.The objective of this study was to detect and characterize relevant safety signals between hypoglycemia and TNF-α inhibitor use.Indication-focused disproportionality analysis was conducted to detect increased reporting of TNF-α-associated hypoglycemia compared with all other reports with the same indication during the same time period. Reporting odds ratios (RORs) with 95% confidence intervals (CIs) were calculated to determine disproportionality. To reduce potential confounding factors, adjusted RORs were further calculated by logistic regression to control for age, sex, diabetes status, and concomitant drugs that potentially affect blood glucose levels.In all, 1086 adverse drug reactions related to TNF-α inhibitors were reported as 'hypoglycemia'. There were no disproportionality signals of hypoglycemia in TNF-α inhibitor users with indication of inflammatory bowel disease. When TNF-α inhibitors were considered as a class, disproportion for hypoglycemia only emerged in indication of psoriasis (n = 267, ROR 1.20, 95% CI 1.02-1.41). In further analyses of specific TNF-α inhibitor type, significant RORs for hypoglycemia were found in indication of rheumatic disease, including adalimumab in ankylosing spondylitis (n = 37, ROR 1.97, 95% CI 1.28-3.04), psoriasis (n = 160, ROR 1.64, 95% CI 1.37-1.97), and rheumatoid arthritis (n = 230, ROR 1.35, 95% CI 1.16-1.56) and infliximab in psoriasis (n = 18, ROR 2.14, 95% CI 1.33-3.42). After adjusting for confounding factors, only the signals of adalimumab were stable.Our study identified some potential pharmacovigilance signals between hypoglycemia and TNF-α inhibitors, which warrants further validation.

Lysophosphatidylserine (LysoPS) is a lipid mediator that induces multiple cellular responses through binding to GPR174. Here, we present the cryo-electron microscopy (cryo-EM) structure of LysoPS-bound human GPR174 in complex with Gs protein. The structure reveals a ligand recognition mode, including the negatively charged head group of LysoPS forms extensive polar interactions with surrounding key residues of the ligand binding pocket, and the L-serine moiety buries deeply into a positive charged cavity in the pocket. In addition, the structure unveils a partially open pocket on transmembrane domain helix (TM) 4 and 5 for a lateral entry of ligand. Finally, the structure reveals a Gs engaging mode featured by a deep insertion of a helix 5 (αH5) and extensive polar interactions between receptor and αH5. Taken together, the information revealed by our structural study provides a framework for understanding LysoPS signaling and a rational basis for designing LysoPS receptor-targeting drugs.

Adhesion G protein-coupled receptors (aGPCRs) are keys of many physiological events and attractive targets for various diseases. aGPCRs are also known to be capable of self-activation via an autoproteolysis process that removes the inhibitory GAIN domain on the extracellular side of receptor and releases a stalk peptide to bind and activate the transmembrane side of receptor. However, the detailed mechanism of aGPCR activation remains elusive. Here, we report the cryo-electron microscopy structures of GPR110 (ADGRF1), a member of aGPCR, in complex with Gq, Gs, Gi, G12 and G13. The structures reveal distinctive ligand engaging model and activation conformations of GPR110. The structures also unveil the rarely explored GPCR/G12 and GPCR/G13 engagements. A comparison of Gq, Gs, Gi, G12 and G13 engagements with GPR110 reveals details of G-protein engagement, including a dividing point at the far end of the alpha helix 5 (αH5) of Gα subunit that separates Gq/Gs engagements from Gi/G12/G13 engagements. This is also where Gq/Gs bind the receptor through both hydrophobic and polar interaction, while Gi/G12/G13 engage receptor mainly through hydrophobic interaction. We further provide physiological evidence of GPR110 activation via stalk peptide. Taken together, our study fills the missing information of GPCR/G-protein engagement and provides a framework for understanding aGPCR activation and GPR110 signaling.

Depth sensor is widely used today and has great impact in object pose estimation, camera tracking, human actions, and scene reconstruction. This paper presents a novel method for human interaction recognition based on 3D skeleton data captured by Kinect sensor using hierarchical spatial-temporal saliency-based representation method. Hierarchical saliency can be conceptualized as Salient Actions at the highest level, determined by the initial movement in an interaction; Salient Points at middle level, determined by a single time point uniquely identified for all instances of Salient Action; Salient Joints at the lowest level, determined by the greatest positional changes of human joints in a Salient Action sequence. Given the interaction saliency at different levels, several types of features, such as spatial displacement, direction relations, and etc., are introduced based on action characteristics. Since there are few publicly accessible test datasets, we created a new dataset with eight types of interactions named K3HI, using the Microsoft Kinect. The method was tested based on Support Vector Machine (SVM) multi-class classifier. In the experiment, the results demonstrate that the average recognition accuracy of hierarchical saliency-based representation is 90.29%, outperforming methods using other features.

Mesh morphing is a technique which gradually deforms a mesh into another one. Mesh parameterization, a powerful tool adopted to establish the one-to-one correspondence map between different meshes, is of great importance in 3D mesh morphing. However, current parameterization methods used in mesh morphing induce large area distortion, resulting in geometric information loss. In this paper, we propose a new morphing approach for topological disk meshes based on area-preserving parameterization. Conformal mapping and Mobius transformation are computed firstly as rough alignment. Then area preserving parameterization is computed via the discrete optimal mass transport map. Features are exactly aligned through radial basis functions. A surface remeshing scheme via Delaunay refinement algorithm is developed to create a new mesh connectivity. Experimental results demonstrate that the proposed method performs well and generates high-quality morphs.

Like outdoors, indoor security is also a critical problem and human action recognition in indoor area is still a hot topic. Most studies on human action recognition ignored the semantic information of a scene, whereas indoors contains varieties of semantics. Meanwhile, the depth sensor with color and depth data is more suitable for extracting the semantics context in human actions. Hence, this paper proposed an indoor action recognition method using Kinect based on the semantics of a scene. First, we proposed a trajectory clustering algorithm for a three-dimensional (3D) scene by combining the different characteristics of people such as the spatial location, movement direction, and speed. Based on the clustering results and scene context, it concludes a region of interest (ROI) extraction method for indoors, and dynamic time warping (DTW) is used to study the abnormal action sequences. Finally, the color and depth-data-based 3D motion history image (3D–MHI) features and the semantics context of the scene were combined to recognize human action. In the experiment, two datasets were tested and the results demonstrate that our semantics-based method performs better than other methods.