Tissue repair and regenerative medicine address the important medical needs to replace damaged tissue with functional tissue. Most regenerative medicine strategies have focused on delivering biomaterials and cells, yet there is the untapped potential for drug-induced regeneration with good specificity and safety profiles. The Hippo pathway is a key regulator of organ size and regeneration by inhibiting cell proliferation and promoting apoptosis. Kinases MST1 and MST2 (MST1/2), the mammalian Hippo orthologs, are central components of this pathway and are, therefore, strong target candidates for pharmacologically induced tissue regeneration. We report the discovery of a reversible and selective MST1/2 inhibitor, 4-((5,10-dimethyl-6-oxo-6,10-dihydro-5H-pyrimido[5,4-b]thieno[3,2-e][1,4]diazepin-2-yl)amino)benzenesulfonamide (XMU-MP-1), using an enzyme-linked immunosorbent assay-based high-throughput biochemical assay. The cocrystal structure and the structure-activity relationship confirmed that XMU-MP-1 is on-target to MST1/2. XMU-MP-1 blocked MST1/2 kinase activities, thereby activating the downstream effector Yes-associated protein and promoting cell growth. XMU-MP-1 displayed excellent in vivo pharmacokinetics and was able to augment mouse intestinal repair, as well as liver repair and regeneration, in both acute and chronic liver injury mouse models at a dose of 1 to 3 mg/kg via intraperitoneal injection. XMU-MP-1 treatment exhibited substantially greater repopulation rate of human hepatocytes in the Fah-deficient mouse model than in the vehicle-treated control, indicating that XMU-MP-1 treatment might facilitate human liver regeneration. Thus, the pharmacological modulation of MST1/2 kinase activities provides a novel approach to potentiate tissue repair and regeneration, with XMU-MP-1 as the first lead for the development of targeted regenerative therapeutics.
Background: Dysbiosis of human gut microbiota is associated with a wide range of metabolic disorders, including gestational diabetes mellitus (GDM). Yet whether gut microbiota dysbiosis participates in the etiology of GDM remains largely unknown.Objectives: Our study was initiated to determine whether the alternations in gut microbial composition during early pregnancy linked to the later development of GDM, and explore the feasibility of microbial biomarkers for the early prediction of GDM. Study design:This nested case-control study was based upon an early pregnancy follow-up cohort (ChiCTR1900020652). Gut microbiota profiles of 98 subjects with GDM and 98 matched healthy controls during the early pregnancy (10-15 weeks) were assessed via 16S rRNA gene amplicon sequencing of V4 region. The data set was randomly split into a discovery set and a validation set, the former was used to analyze the differences between GDM cases and controls in gut microbial composition and functional annotation, and to establish an early identification model of GDM, then the performance of the model was verified by the external validation set.Results: Bioinformatic analyses revealed changes to gut microbial composition with significant differences in relative abundance between the groups. Specifically, Eisenbergiella, Tyzzerella 4, and Lachnospiraceae NK4A136 were enriched in the GDM group, whereas Parabacteroides, Megasphaera, Eubacterium eligens group, etc. remained dominant in the controls. Correlation analysis revealed that GDM-enriched genera Eisenbergiella and Tyzzerella 4 were positively correlated with fasting blood glucose levels, while three control-enriched genera (Parabacteroides, Parasutterella, and Ruminococcaceae UCG 002) were the opposite. Further, GDM functional annotation modules revealed enrichment of modules for sphingolipid metabolism, starch and sucrose metabolism, etc., while lysine biosynthesis and nitrogen metabolism were reduced. Finally, five genera and two clinical indices were included in the linear discriminant analysis model for the prediction of GDM; the areas under receiver operating characteristic curves of the training and validation sets were 0.736 (95% confidence interval: 0.663-0.808) and 0.696 (0.575-0.818), respectively. Ma et al. Gut Bacterial Dysbiosis Before GDMConclusions: Gut bacterial dysbiosis in early pregnancy was found to be associated with the later development of GDM, and gut microbiota-targeted biomarkers might be utilized as potential predictors of GDM.
BackgroundCuproptosis is a copper-dependent cell death mechanism that is associated with tumor progression, prognosis, and immune response. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of triple-negative breast cancer (TNBC) remains unclear.Patients and methodsIn total, 346 TNBC samples were collected from The Cancer Genome Atlas database and three Gene Expression Omnibus datasets, and were classified using R software packages. The relationships between the different subgroups and clinical pathological characteristics, immune infiltration characteristics, and mutation status of the TME were examined. Finally, a nomogram and calibration curve were constructed to predict patient survival probability to improve the clinical applicability of the CRG_score.ResultsWe identified two CRG clusters with immune cell infiltration characteristics highly consistent with those of the immune-inflamed and immune-desert clusters. Furthermore, we demonstrated that the gene signature can be used to evaluate tumor immune cell infiltration, clinical features, and prognostic status. Low CRG_scores were characterized by high tumor mutation burden and immune activation, good survival probability, and more immunoreactivity to CTLA4, while high CRG_scores were characterized by the activation of stromal pathways and immunosuppression.ConclusionThis study revealed the potential effects of CRGs on the TME, clinicopathological features, and prognosis of TNBC. The CRGs were closely associated with the tumor immunity of TNBC and are a potential tool for predicting patient prognosis. Our data provide new directions for the development of novel drugs in the future.
Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of scene flow benefits many applications, such as autonomous driving and service robot. This paper studies the problem of scene flow estimation from two consecutive 3D point clouds. In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer. The proposed network has a new morefor-less hierarchical architecture. The more-for-less means that the number of input points is greater than the number of output points for scene flow estimation, which brings more input information and balances the precision and resource consumption. In this hierarchical architecture, scene flow of different levels is generated and supervised respectively. A novel attentive embedding module is introduced to aggregate the features of adjacent points using a double attention method in a patch-topatch manner. The proper layers for flow embedding and flow supervision are carefully considered in our network designment. Experiments show that the proposed network outperforms the state-of-the-art performance of 3D scene flow estimation on the FlyingThings3D and KITTI Scene Flow 2015 datasets. We also apply the proposed network to realistic LiDAR odometry task, which is an key problem in autonomous driving. The experiment results demonstrate that our proposed network can outperform the ICP-based method and shows the good practical application ability.
BackgroundHand, foot and mouth disease (HFMD) is one of the highest reported infectious diseases with several outbreaks across the world. This study aimed at describing epidemiological characteristics, investigating spatio-temporal clustering changes, and identifying determinant factors in different clustering areas of HFMD.MethodsDescriptive statistics was used to evaluate the epidemic characteristics of HFMD from 2009 to 2015. Spatial autocorrelation and spatio-temporal cluster analysis were used to explore the spatial temporal patterns. An autologistic regression model was employed to explore determinants of HFMD clustering.ResultsThe incidence rates of HFMD ranged from 54.31/10 million to 318.06/10 million between 2009 and 2015 in Hunan. Cases were mainly prevalent in children aged 5 years and even younger, with an average male-to-female sex ratio of 1.66, and two epidemic periods in each year. Clustering areas gathered in the northern regions in 2009 and in the central regions from 2010 to 2012. They moved to central-southern regions in 2013 and 2014 and central-western regions in 2015. The significant risk factors of HFMD clusters were rainfall (OR = 2.187), temperature (OR = 4.329) and humidity (OR = 2.070). The protect factor was wind speed (OR = 0.258).ConclusionsThe HFMD incidence from 2009 to 2015 in Hunan showed a new spatiotemporal clustering tendency, with the shifting trend of clustering areas toward south and west. Meteorological factors showed a strong association with HFMD clustering, which may assist in predicting future spatial-temporal clusters.Electronic supplementary materialThe online version of this article (10.1186/s12879-017-2742-9) contains supplementary material, which is available to authorized users.
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