The Antimicrobial Mechanism of Action of 3,4-Methylenedioxy-Βββ-Nitropropene
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Retention Indices for Frequently Reported Compounds of Plant Essential Oils
Retention Indices for Frequently Reported Compounds of Plant Essential Oils V. I. Babushok,a) P. J. Linstrom, and I. G. Zenkevichb) National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA (Received 1 August 2011; accepted 27 September 2011; published online 29 November 2011) Gas chromatographic retention indices were evaluated for 505 frequently reported plant essential oil components using a large retention index database. Retention data are presented for three types of commonly used stationary phases: dimethyl silicone (nonpolar), dimethyl sili- cone with 5% phenyl groups (slightly polar), and polyethylene glycol (polar) stationary phases. The evaluations are based on the treatment of multiple measurements with the number of data records ranging from about 5 to 800 per compound. Data analysis was limited to temperature programmed conditions. The data reported include the average and median values of retention index with standard deviations and confidence intervals. VC 2011 by the U.S. Secretary of Commerce on behalf of the United States. All rights reserved. [doi:10.1063/1.3653552] Key words: essential oils; gas chromatography; Kova´ts indices; linear indices; retention indices; identification; flavor; olfaction. CONTENTS 1. Introduction The practical applications of plant essential oils are very 1. Introduction................................ 1 diverse. They are used for the production of food, drugs, per- fumes, aromatherapy, and many other applications.1–4 The 2. Retention Indices ........................... 2 need for identification of essential oil components ranges 3. Retention Data Presentation and Discussion . 2 from product quality control to basic research. The identifi- 4. Summary.................................. 45 cation of unknown compounds remains a complex problem, in spite of great progress made in analytical techniques over 5. -
Precursors and Chemicals Frequently Used in the Illicit Manufacture of Narcotic Drugs and Psychotropic Substances 2017
INTERNATIONAL NARCOTICS CONTROL BOARD Precursors and chemicals frequently used in the illicit manufacture of narcotic drugs and psychotropic substances 2017 EMBARGO Observe release date: Not to be published or broadcast before Thursday, 1 March 2018, at 1100 hours (CET) UNITED NATIONS CAUTION Reports published by the International Narcotics Control Board in 2017 The Report of the International Narcotics Control Board for 2017 (E/INCB/2017/1) is supplemented by the following reports: Narcotic Drugs: Estimated World Requirements for 2018—Statistics for 2016 (E/INCB/2017/2) Psychotropic Substances: Statistics for 2016—Assessments of Annual Medical and Scientific Requirements for Substances in Schedules II, III and IV of the Convention on Psychotropic Substances of 1971 (E/INCB/2017/3) Precursors and Chemicals Frequently Used in the Illicit Manufacture of Narcotic Drugs and Psychotropic Substances: Report of the International Narcotics Control Board for 2017 on the Implementation of Article 12 of the United Nations Convention against Illicit Traffic in Narcotic Drugs and Psychotropic Substances of 1988 (E/INCB/2017/4) The updated lists of substances under international control, comprising narcotic drugs, psychotropic substances and substances frequently used in the illicit manufacture of narcotic drugs and psychotropic substances, are contained in the latest editions of the annexes to the statistical forms (“Yellow List”, “Green List” and “Red List”), which are also issued by the Board. Contacting the International Narcotics Control Board The secretariat of the Board may be reached at the following address: Vienna International Centre Room E-1339 P.O. Box 500 1400 Vienna Austria In addition, the following may be used to contact the secretariat: Telephone: (+43-1) 26060 Fax: (+43-1) 26060-5867 or 26060-5868 Email: [email protected] The text of the present report is also available on the website of the Board (www.incb.org). -
Application of Biomarker Compounds As Tracers for Sources and Fates of Natural and Anthropogenic Organic Matter in Tile Environment
AN ABSTRACT OF THE DISSERTATION OF Daniel R. Oros for the degree of Doctor of Philosophy in Environmental Sciences presented on September 24. 1999. Title: Application of Biomarker Compoundsas Tracers for Sources and Fates of Natural and Anthropogenic Organic Matter in the Environment. Redacted for Privacy Abstract approved: Bernd R.T. Simoneit Determination of the source and fate of natural (higher plant lipids, marine lipids, etc.) and anthropogenically (e.g., petroleum, coal emissions) derived hydrocarbons and oxygenated compounds in the environment was accomplished using gas chromatography (GC) and gas chromatography-mass spectrometry (GC- MS) to characterize or identify molecular biomarkers to be utilized as tracers. The distributions and abundances of biomarkers such as straight chain homologous series (e.g., n-alkanes, n-alkanoic acids, n-alkan-2-ones, n-alkanols, etc.) and cyclic terpenoid compounds (e.g., sesquiterpenoids, diterpenoids, steroids, triterpenoids) were identified in epicuticular waxes from conifers of western North America (natural emissions). These biomarkers and their thermal alteration derivativeswere also identified in smoke emissions from known vegetation sources (e.g., conifers, deciduous trees and grasses) and were then applied as tracers in soils, soils that contained wildfire residues and soillriver mud washout after wildfire burning. Where possible, the reaction pathways of transformation from the parentprecursor compounds to intermediate and final alteration products were determined from GC- MS data. In addition, molecular tracer analysis was applied to air, water and sediment samples collected from a lacustrine setting (Crater Lake, OR) in order to determine the identities, levels and fates of anthropogenic (i.e., petroleum hydrocarbon contamination from boating and related activities) hydrocarbons ina pristine organic matter sink. -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
A Proteomic Approach to Uncover Neuroprotective Mechanisms of Oleocanthal Against Oxidative Stress
International Journal of Molecular Sciences Article A Proteomic Approach to Uncover Neuroprotective Mechanisms of Oleocanthal against Oxidative Stress Laura Giusti 1,†, Cristina Angeloni 2,†, Maria Cristina Barbalace 3, Serena Lacerenza 4, Federica Ciregia 5, Maurizio Ronci 6 ID , Andrea Urbani 7, Clementina Manera 4, Maria Digiacomo 4 ID , Marco Macchia 4, Maria Rosa Mazzoni 4, Antonio Lucacchini 1 ID and Silvana Hrelia 3,* ID 1 Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy; [email protected] (L.G.); [email protected] (A.L.) 2 School of Pharmacy, University of Camerino, 62032 Camerino, Italy; [email protected] 3 Department for Life Quality Studies, Alma Mater Studiorum, University of Bologna, 47921 Rimini, Italy; [email protected] 4 Department of Pharmacy, University of Pisa, 56126 Pisa, Italy; [email protected] (S.L.); [email protected] (C.M.); [email protected] (M.D.); [email protected] (M.M.); [email protected] (M.R.M.) 5 Department of Rheumatology, GIGA Research, Centre Hospitalier Universitaire (CHU) de Liège, University of Liège, 4000 Liège, Belgium; [email protected] 6 Department of Medical, Oral and Biotechnological Sciences, University G. d’Annunzio of Chieti-Pescara, 65127 Pescara, Italy; [email protected] 7 Institute of Biochemistry and Clinical Biochemistry, Catholic University, 00198 Rome, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-051-209-1235 † These authors contributed equally to this work. Received: 3 July 2018; Accepted: 1 August 2018; Published: 8 August 2018 Abstract: Neurodegenerative diseases represent a heterogeneous group of disorders that share common features like abnormal protein aggregation, perturbed Ca2+ homeostasis, excitotoxicity, impairment of mitochondrial functions, apoptosis, inflammation, and oxidative stress. -
Congenital Disorders of Glycosylation from a Neurological Perspective
brain sciences Review Congenital Disorders of Glycosylation from a Neurological Perspective Justyna Paprocka 1,* , Aleksandra Jezela-Stanek 2 , Anna Tylki-Szyma´nska 3 and Stephanie Grunewald 4 1 Department of Pediatric Neurology, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland 2 Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland; [email protected] 3 Department of Pediatrics, Nutrition and Metabolic Diseases, The Children’s Memorial Health Institute, W 04-730 Warsaw, Poland; [email protected] 4 NIHR Biomedical Research Center (BRC), Metabolic Unit, Great Ormond Street Hospital and Institute of Child Health, University College London, London SE1 9RT, UK; [email protected] * Correspondence: [email protected]; Tel.: +48-606-415-888 Abstract: Most plasma proteins, cell membrane proteins and other proteins are glycoproteins with sugar chains attached to the polypeptide-glycans. Glycosylation is the main element of the post- translational transformation of most human proteins. Since glycosylation processes are necessary for many different biological processes, patients present a diverse spectrum of phenotypes and severity of symptoms. The most frequently observed neurological symptoms in congenital disorders of glycosylation (CDG) are: epilepsy, intellectual disability, myopathies, neuropathies and stroke-like episodes. Epilepsy is seen in many CDG subtypes and particularly present in the case of mutations -
V45n4a03.Pdf
Montoya JC/et al/Colombia Médica - Vol. 45 Nº4 2014 (Oct-Dec) Colombia Médica colombiamedica.univalle.edu.co Original Article Global differential expression of genes located in the Down Syndrome Critical Region in normal human brain Expresión diferencial global de genes localizados en la Región Crítica del Síndrome de Down en el cerebro humano normal Julio Cesar Montoya1,3, Dianora Fajardo2, Angela Peña2 , Adalberto Sánchez1, Martha C Domínguez1,2, José María Satizábal1, Felipe García-Vallejo1,2 1 Department of Physiological Sciences, School of Basic Sciences, Faculty of Health, Universidad del Valle. 2 Laboratory of Molecular Biology and Pathogenesis LABIOMOL. Universidad del Valle, Cali, Colombia. 3 Faculty of Basic Sciences, Universidad Autónoma de Occidente, Cali, Colombia. Montoya JC , Fajardo D, Peña A , Sánchez A, Domínguez MC, Satizábal JM, García-Vallejo F.. Global differential expression of genes located in the Down Syndrome Critical Region in normal human brain. Colomb Med. 2014; 45(4): 154-61. © 2014 Universidad del Valle. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Article history Abstract Resumen Background: The information of gene expression obtained from Introducción: La información de la expresión de genes consignada Received: 2 July 2014 Revised: 10 November 2014 databases, have made possible the extraction and analysis of data en bases de datos, ha permitido extraer y analizar información acerca Accepted: 19 December 2014 related with several molecular processes involving not only in procesos moleculares implicados tanto en la homeostasis cerebral y su brain homeostasis but its disruption in some neuropathologies; alteración en algunas neuropatologías. -
NICU Gene List Generator.Xlsx
Neonatal Crisis Sequencing Panel Gene List Genes: A2ML1 - B3GLCT A2ML1 ADAMTS9 ALG1 ARHGEF15 AAAS ADAMTSL2 ALG11 ARHGEF9 AARS1 ADAR ALG12 ARID1A AARS2 ADARB1 ALG13 ARID1B ABAT ADCY6 ALG14 ARID2 ABCA12 ADD3 ALG2 ARL13B ABCA3 ADGRG1 ALG3 ARL6 ABCA4 ADGRV1 ALG6 ARMC9 ABCB11 ADK ALG8 ARPC1B ABCB4 ADNP ALG9 ARSA ABCC6 ADPRS ALK ARSL ABCC8 ADSL ALMS1 ARX ABCC9 AEBP1 ALOX12B ASAH1 ABCD1 AFF3 ALOXE3 ASCC1 ABCD3 AFF4 ALPK3 ASH1L ABCD4 AFG3L2 ALPL ASL ABHD5 AGA ALS2 ASNS ACAD8 AGK ALX3 ASPA ACAD9 AGL ALX4 ASPM ACADM AGPS AMELX ASS1 ACADS AGRN AMER1 ASXL1 ACADSB AGT AMH ASXL3 ACADVL AGTPBP1 AMHR2 ATAD1 ACAN AGTR1 AMN ATL1 ACAT1 AGXT AMPD2 ATM ACE AHCY AMT ATP1A1 ACO2 AHDC1 ANK1 ATP1A2 ACOX1 AHI1 ANK2 ATP1A3 ACP5 AIFM1 ANKH ATP2A1 ACSF3 AIMP1 ANKLE2 ATP5F1A ACTA1 AIMP2 ANKRD11 ATP5F1D ACTA2 AIRE ANKRD26 ATP5F1E ACTB AKAP9 ANTXR2 ATP6V0A2 ACTC1 AKR1D1 AP1S2 ATP6V1B1 ACTG1 AKT2 AP2S1 ATP7A ACTG2 AKT3 AP3B1 ATP8A2 ACTL6B ALAS2 AP3B2 ATP8B1 ACTN1 ALB AP4B1 ATPAF2 ACTN2 ALDH18A1 AP4M1 ATR ACTN4 ALDH1A3 AP4S1 ATRX ACVR1 ALDH3A2 APC AUH ACVRL1 ALDH4A1 APTX AVPR2 ACY1 ALDH5A1 AR B3GALNT2 ADA ALDH6A1 ARFGEF2 B3GALT6 ADAMTS13 ALDH7A1 ARG1 B3GAT3 ADAMTS2 ALDOB ARHGAP31 B3GLCT Updated: 03/15/2021; v.3.6 1 Neonatal Crisis Sequencing Panel Gene List Genes: B4GALT1 - COL11A2 B4GALT1 C1QBP CD3G CHKB B4GALT7 C3 CD40LG CHMP1A B4GAT1 CA2 CD59 CHRNA1 B9D1 CA5A CD70 CHRNB1 B9D2 CACNA1A CD96 CHRND BAAT CACNA1C CDAN1 CHRNE BBIP1 CACNA1D CDC42 CHRNG BBS1 CACNA1E CDH1 CHST14 BBS10 CACNA1F CDH2 CHST3 BBS12 CACNA1G CDK10 CHUK BBS2 CACNA2D2 CDK13 CILK1 BBS4 CACNB2 CDK5RAP2 -
Technical Note, Appendix: an Analysis of Blood Processing Methods to Prepare Samples for Genechip® Expression Profiling (Pdf, 1
Appendix 1: Signature genes for different blood cell types. Blood Cell Type Source Probe Set Description Symbol Blood Cell Type Source Probe Set Description Symbol Fraction ID Fraction ID Mono- Lympho- GSK 203547_at CD4 antigen (p55) CD4 Whitney et al. 209813_x_at T cell receptor TRG nuclear cytes gamma locus cells Whitney et al. 209995_s_at T-cell leukemia/ TCL1A Whitney et al. 203104_at colony stimulating CSF1R lymphoma 1A factor 1 receptor, Whitney et al. 210164_at granzyme B GZMB formerly McDonough (granzyme 2, feline sarcoma viral cytotoxic T-lymphocyte- (v-fms) oncogene associated serine homolog esterase 1) Whitney et al. 203290_at major histocompatibility HLA-DQA1 Whitney et al. 210321_at similar to granzyme B CTLA1 complex, class II, (granzyme 2, cytotoxic DQ alpha 1 T-lymphocyte-associated Whitney et al. 203413_at NEL-like 2 (chicken) NELL2 serine esterase 1) Whitney et al. 203828_s_at natural killer cell NK4 (H. sapiens) transcript 4 Whitney et al. 212827_at immunoglobulin heavy IGHM Whitney et al. 203932_at major histocompatibility HLA-DMB constant mu complex, class II, Whitney et al. 212998_x_at major histocompatibility HLA-DQB1 DM beta complex, class II, Whitney et al. 204655_at chemokine (C-C motif) CCL5 DQ beta 1 ligand 5 Whitney et al. 212999_x_at major histocompatibility HLA-DQB Whitney et al. 204661_at CDW52 antigen CDW52 complex, class II, (CAMPATH-1 antigen) DQ beta 1 Whitney et al. 205049_s_at CD79A antigen CD79A Whitney et al. 213193_x_at T cell receptor beta locus TRB (immunoglobulin- Whitney et al. 213425_at Homo sapiens cDNA associated alpha) FLJ11441 fis, clone Whitney et al. 205291_at interleukin 2 receptor, IL2RB HEMBA1001323, beta mRNA sequence Whitney et al. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Aneuploidy: Using Genetic Instability to Preserve a Haploid Genome?
Health Science Campus FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Science (Cancer Biology) Aneuploidy: Using genetic instability to preserve a haploid genome? Submitted by: Ramona Ramdath In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Science Examination Committee Signature/Date Major Advisor: David Allison, M.D., Ph.D. Academic James Trempe, Ph.D. Advisory Committee: David Giovanucci, Ph.D. Randall Ruch, Ph.D. Ronald Mellgren, Ph.D. Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D. Date of Defense: April 10, 2009 Aneuploidy: Using genetic instability to preserve a haploid genome? Ramona Ramdath University of Toledo, Health Science Campus 2009 Dedication I dedicate this dissertation to my grandfather who died of lung cancer two years ago, but who always instilled in us the value and importance of education. And to my mom and sister, both of whom have been pillars of support and stimulating conversations. To my sister, Rehanna, especially- I hope this inspires you to achieve all that you want to in life, academically and otherwise. ii Acknowledgements As we go through these academic journeys, there are so many along the way that make an impact not only on our work, but on our lives as well, and I would like to say a heartfelt thank you to all of those people: My Committee members- Dr. James Trempe, Dr. David Giovanucchi, Dr. Ronald Mellgren and Dr. Randall Ruch for their guidance, suggestions, support and confidence in me. My major advisor- Dr. David Allison, for his constructive criticism and positive reinforcement. -
Literature Mining Sustains and Enhances Knowledge Discovery from Omic Studies
LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES by Rick Matthew Jordan B.S. Biology, University of Pittsburgh, 1996 M.S. Molecular Biology/Biotechnology, East Carolina University, 2001 M.S. Biomedical Informatics, University of Pittsburgh, 2005 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2016 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Rick Matthew Jordan It was defended on December 2, 2015 and approved by Shyam Visweswaran, M.D., Ph.D., Associate Professor Rebecca Jacobson, M.D., M.S., Professor Songjian Lu, Ph.D., Assistant Professor Dissertation Advisor: Vanathi Gopalakrishnan, Ph.D., Associate Professor ii Copyright © by Rick Matthew Jordan 2016 iii LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES Rick Matthew Jordan, M.S. University of Pittsburgh, 2016 Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature- mining evidence with pathway analysis evidence is an open problem in biomedical informatics research.