Brown University

About: Brown University is a education organization based out in Providence, Rhode Island, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 35778 authors who have published 90896 publications receiving 4471489 citations. The organization is also known as: brown.edu & Brown.

Suppression of Raf-1 kinase activity and MAP kinase signalling by RKIP

Abstract: Raf-1 phosphorylates and activates MEK-1, a kinase that activates the extracellular signal regulated kinases (ERK). This kinase cascade controls the proliferation and differentiation of different cell types. Here we describe a Raf-1-interacting protein, isolated using a yeast two-hybrid screen. This protein inhibits the phosphorylation and activation of MEK by Raf-1 and is designated RKIP (Raf kinase inhibitor protein). In vitro, RKIP binds to Raf-1, MEK and ERK, but not to Ras. RKIP co-immunoprecipitates with Raf-1 and MEK from cell lysates and colocalizes with Raf-1 when examined by confocal microscopy. RKIP is not a substrate for Raf-1 or MEK, but competitively disrupts the interaction between these kinases. RKIP overexpression interferes with the activation of MEK and ERK, induction of AP-1-dependent reporter genes and transformation elicited by an oncogenically activated Raf-1 kinase. Downregulation of endogenous RKIP by expression of antisense RNA or antibody microinjection induces the activation of MEK-, ERK- and AP-1-dependent transcription. RKIP represents a new class of protein-kinase-inhibitor protein that regulates the activity of the Raf/MEK/ERK module.

Surviving Sepsis Campaign: Guidelines on the Management of Critically Ill Adults with Coronavirus Disease 2019 (COVID-19).

Abstract: BACKGROUND: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, Coronavirus Disease 2019 (COVID-19), affecting thousands of people around the world. Urgent guidance for clinicians caring for the sickest of these patients is needed. METHODS: We formed a panel of 36 experts from 12 countries. All panel members completed the World Health Organization conflict of interest disclosure form. The panel proposed 53 questions that are relevant to the management of COVID-19 in the ICU. We searched the literature for direct and indirect evidence on the management of COVID-19 in critically ill patients in the ICU. We identified relevant and recent systematic reviews on most questions relating to supportive care. We assessed the certainty in the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach, then generated recommendations based on the balance between benefit and harm, resource and cost implications, equity, and feasibility. Recommendations were either strong or weak, or in the form of best practice recommendations. RESULTS: The Surviving Sepsis Campaign COVID-19 panel issued 54 statements, of which four are best practice statements, nine are strong recommendations, and 35 are weak recommendations. No recommendation was provided for six questions. The topics were: 1) infection control, 2) laboratory diagnosis and specimens, 3) hemodynamic support, 4) ventilatory support, and 5) COVID-19 therapy. CONCLUSION: The Surviving Sepsis Campaign COVID-19 panel issued several recommendations to help support healthcare workers caring for critically ill ICU patients with COVID-19. When available, we will provide new evidence in further releases of these guidelines.

Estrogen Action Via the G Protein-Coupled Receptor, GPR30: Stimulation of Adenylyl Cyclase and cAMP-Mediated Attenuation of the Epidermal Growth Factor Receptor-to-MAPK Signaling Axis

Abstract: Estrogen triggers rapid yet transient activation of the MAPKs, extracellular signal-regulated kinase (Erk)-1 and Erk-2. We have reported that this estrogen action requires the G protein-coupled receptor, GPR30, and occurs via Gbetagamma-subunit protein-dependent transactivation of the epidermal growth factor (EGF) receptor through the release of pro-heparan-bound EGF from the cell surface. Here we investigate the mechanism by which Erk-1/-2 activity is rapidly restored to basal levels after estrogen stimulation. Evidence is provided that attenuation of Erk-1/-2 activity by estrogen occurs via GPR30-dependent stimulation of adenylyl cyclase and cAMP-dependent signaling that results in Raf-1 inactivation. We show that 17beta-E2 represses EGF-induced activation of the Raf-to-Erk pathway in human breast carcinoma cells that express GPR30, including MCF-7 and SKBR3 cells which express both or neither, ER, respectively. MDA-MB-231 cells, which express ERbeta, but not ERalpha, and low levels of GPR30 protein, are unable to stimulate adenylyl cyclase or promote estrogen-mediated blockade of EGF-induced activation of Erk-1/-2. Pretreatment of MDA-MB-231 cells with cholera toxin, which ADP-ribosylates and activates Galphas subunit proteins, results in G protein-coupled receptor (GPCR)-independent adenylyl cyclase activity and suppression of EGF-induced Erk-1/-2 activity. Transfection of GPR30 into MDA-MB-231 cells restores their ability to stimulate adenylyl cyclase and attenuate EGF-induced activation of Erk-1/-2 by estrogen. Moreover, GPR30-dependent, cAMP-mediated attenuation of EGF-induced Erk-1/-2 activity was achieved by ER antagonists such as tamoxifen or ICI 182, 780; yet not by 17alpha-E2 or progesterone. Thus, our data delineate a novel mechanism, requiring GPR30 and estrogen, that acts to regulate Erk-1/-2 activity via an inhibitory signal mediated by cAMP. Coupled with our prior findings, these current data imply that estrogen balances Erk-1/-2 activity through a single GPCR via two distinct G protein-dependent signaling pathways that have opposing effects on the EGF receptor-to-MAPK pathway.

SUN attribute database: Discovering, annotating, and recognizing scene attributes

Abstract: In this paper we present the first large-scale scene attribute database. First, we perform crowd-sourced human studies to find a taxonomy of 102 discriminative attributes. Next, we build the “SUN attribute database” on top of the diverse SUN categorical database. Our attribute database spans more than 700 categories and 14,000 images and has potential for use in high-level scene understanding and fine-grained scene recognition. We use our dataset to train attribute classifiers and evaluate how well these relatively simple classifiers can recognize a variety of attributes related to materials, surface properties, lighting, functions and affordances, and spatial envelope properties.