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White BK, Gombert A, Nguyen T, Yau B, Ishizumi A, Kirchner L, León A, Wilson H, Jaramillo-Gutierrez G, Cerquides J, D'Agostino M, Salvi C, Sreenath RS, Rambaud K, Samhouri D, Briand S, Purnat TD. Using Machine Learning Technology (Early Artificial Intelligence-Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study. JMIR Infodemiology 2023; 3:e47317. [PMID: 37422854 PMCID: PMC10477919 DOI: 10.2196/47317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND Amid the COVID-19 pandemic, there has been a need for rapid social understanding to inform infodemic management and response. Although social media analysis platforms have traditionally been designed for commercial brands for marketing and sales purposes, they have been underused and adapted for a comprehensive understanding of social dynamics in areas such as public health. Traditional systems have challenges for public health use, and new tools and innovative methods are required. The World Health Organization Early Artificial Intelligence-Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges. OBJECTIVE This paper describes the development of the EARS platform, including data sourcing, development, and validation of a machine learning categorization approach, as well as the results from the pilot study. METHODS Data for EARS are collected daily from web-based conversations in publicly available sources in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives into 5 relevant main categories and 41 subcategories. We developed a semisupervised machine learning algorithm to categorize social media posts into categories and various filters. To validate the results obtained by the machine learning-based approach, we compared it to a search-filter approach, applying Boolean queries with the same amount of information and measured the recall and precision. Hotelling T2 was used to determine the effect of the classification method on the combined variables. RESULTS The EARS platform was developed, validated, and applied to characterize conversations regarding COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the Boolean search filters method for precision and recall in both English and Spanish languages (P<.001). Demographic and other filters provided useful insights on data, and the gender split of users in the platform was largely consistent with population-level data on social media use. CONCLUSIONS The EARS platform was developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of public health taxonomy and artificial intelligence technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed for scalability; iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has the benefit of categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned for continuous improvements, to meet the challenges in the generation of infodemic insights from social media for infodemic managers and public health professionals.
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Affiliation(s)
- Becky K White
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | | | - Tim Nguyen
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Brian Yau
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Atsuyoshi Ishizumi
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | | | | | | | | | - Jesus Cerquides
- Artificial Intelligence Research Institute, Spanish Council for Scientific Research, Cerdanyola, Spain
| | - Marcelo D'Agostino
- Information Systems for Health, Evidence and Intelligence for Action in Health, Pan American Health Organization and World Health Organization Regional Office for the Americas, Washington DC, DC, United States
| | - Cristiana Salvi
- Risk Communication and Community Engagement Unit, Health Emergencies Division, World Health Organization Regional Office for Europe, Copenhagen, Denmark
| | - Ravi Shankar Sreenath
- Risk Communication and Community Engagement Unit, Health Emergencies Division, World Health Organization Regional Office for Europe, Copenhagen, Denmark
| | - Kimberly Rambaud
- Risk Communication and Community Engagement Unit, Health Emergencies Division, World Health Organization Regional Office for Europe, Copenhagen, Denmark
| | - Dalia Samhouri
- Country Health Emergency Preparedness and International Health Regulations (2005), World Health Organization Regional Office for Eastern Mediterranean, Cairo, Egypt
| | - Sylvie Briand
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
| | - Tina D Purnat
- Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Geneva, Switzerland
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Valencia C, Jaramillo-Gutierrez G, Rearte A, Rosin P, Gassino F, Morreale SE, Gobern L, Paredes A, Rondy M, Balsells E, Galindo P, Parra L, Mazariegos O, Young A, Bhavnani D, Miri A, Iken D, James E, Rodriguez A. Adoption of digital tools in the context of the COVID-19 pandemic in the Region of the Americas - the Go.Data experience. Lancet Reg Health Am 2022; 16:100377. [PMID: 36246768 PMCID: PMC9536219 DOI: 10.1016/j.lana.2022.100377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
The COVID-19 pandemic has accelerated the growth of digital health tools. Although a number of different tools exist to support field data collection in the context of outbreak response, they have not been sufficient. This prompted the World Health Organization (WHO) to collaborate with the Global Outbreak Alert and Response Network (GOARN) and GOARN partners to develop a comprehensive system, Go.Data. Go.Data, a digital tool for outbreak response has simplified how countries operationalize and monitor case and contact data. Since the start of the pandemic, WHO and GOARN partners have provided support to Go.Data projects in 65 countries and territories, yet the demand by countries to have documented success cases of Go.Data implementations continues to grow. This viewpoint documents the successful Go.Data implementation frameworks in two countries, Argentina and Guatemala and an academic institution, the University of Texas at Austin.
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Affiliation(s)
- Cristina Valencia
- Global Outbreak Alert and Response Network (GOARN), Geneva, Switzerland,Department of Epidemiology, Mailman School of Public Health, Columbia University, New York City, USA,Corresponding author
| | - Giovanna Jaramillo-Gutierrez
- Global Outbreak Alert and Response Network (GOARN), Geneva, Switzerland,European Programme of Interventional Epidemiology Training Alumni Network, EAN, Brussels, Belgium
| | - Analía Rearte
- Department of Epidemiology, Ministry of Health, Buenos Aires, Argentina,National University of Mar del Plata, Medicine School, Buenos Aires, Argentina
| | - Paula Rosin
- Department of Epidemiology, Ministry of Health, Buenos Aires, Argentina
| | - Fernando Gassino
- Department of Information Systems, Ministry of Health, Buenos Aires, Argentina,Department of Health Emergencies, Pan American Health Organization, Buenos Aires, Argentina
| | | | - Lorena Gobern
- Department of Epidemiology, Ministry of Health, Guatemala City, Guatemala
| | - Antonio Paredes
- Department of Epidemiology, Ministry of Health, Guatemala City, Guatemala
| | - Marc Rondy
- Immunization Unit, Pan American Health Organization, Guatemala City, Guatemala
| | - Evelyn Balsells
- Immunization Unit, Pan American Health Organization, Guatemala City, Guatemala,Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Medical School, Teviot Place, Edinburgh, United Kingdom
| | - Pablo Galindo
- Department of Health Emergencies, Pan American Health Organization, Guatemala City, Guatemala
| | - Liz Parra
- Department of Health Emergencies, Pan American Health Organization, Guatemala City, Guatemala
| | - Oliver Mazariegos
- Department of Health Emergencies, Pan American Health Organization, Guatemala City, Guatemala
| | - Amy Young
- The Dell Medical School at University of Texas at Austin, UT Health Austin, Austin, USA
| | - Darlene Bhavnani
- The Dell Medical School at University of Texas at Austin, UT Health Austin, Austin, USA
| | - Aaron Miri
- The Dell Medical School at University of Texas at Austin, UT Health Austin, Austin, USA
| | - Daniel Iken
- The Dell Medical School at University of Texas at Austin, UT Health Austin, Austin, USA
| | - Emily James
- The Dell Medical School at University of Texas at Austin, UT Health Austin, Austin, USA
| | - Angel Rodriguez
- Department of Health Emergencies, Pan American Health Organization, Washington, United States of America
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Fehr J, Jaramillo-Gutierrez G, Oala L, Gröschel MI, Bierwirth M, Balachandran P, Werneck-Leite A, Lippert C. Piloting a Survey-Based Assessment of Transparency and Trustworthiness with Three Medical AI Tools. Healthcare (Basel) 2022; 10:healthcare10101923. [PMID: 36292369 PMCID: PMC9601535 DOI: 10.3390/healthcare10101923] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/18/2022] [Accepted: 09/21/2022] [Indexed: 11/04/2022] Open
Abstract
Artificial intelligence (AI) offers the potential to support healthcare delivery, but poorly trained or validated algorithms bear risks of harm. Ethical guidelines stated transparency about model development and validation as a requirement for trustworthy AI. Abundant guidance exists to provide transparency through reporting, but poorly reported medical AI tools are common. To close this transparency gap, we developed and piloted a framework to quantify the transparency of medical AI tools with three use cases. Our framework comprises a survey to report on the intended use, training and validation data and processes, ethical considerations, and deployment recommendations. The transparency of each response was scored with either 0, 0.5, or 1 to reflect if the requested information was not, partially, or fully provided. Additionally, we assessed on an analogous three-point scale if the provided responses fulfilled the transparency requirement for a set of trustworthiness criteria from ethical guidelines. The degree of transparency and trustworthiness was calculated on a scale from 0% to 100%. Our assessment of three medical AI use cases pin-pointed reporting gaps and resulted in transparency scores of 67% for two use cases and one with 59%. We report anecdotal evidence that business constraints and limited information from external datasets were major obstacles to providing transparency for the three use cases. The observed transparency gaps also lowered the degree of trustworthiness, indicating compliance gaps with ethical guidelines. All three pilot use cases faced challenges to provide transparency about medical AI tools, but more studies are needed to investigate those in the wider medical AI sector. Applying this framework for an external assessment of transparency may be infeasible if business constraints prevent the disclosure of information. New strategies may be necessary to enable audits of medical AI tools while preserving business secrets.
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Affiliation(s)
- Jana Fehr
- Digital Engineering Faculty, University of Potsdam, 14482 Potsdam, Germany
- Digital Health & Machine Learning, Hasso Plattner Institute, 14482 Potsdam, Germany
- Correspondence:
| | | | - Luis Oala
- Department of Artificial Intelligence, Fraunhofer HHI, 10587 Berlin, Germany
| | - Matthias I. Gröschel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Manuel Bierwirth
- ITU/WHO Focus Group AI4H, 1211 Geneva, Switzerland
- Alumnus Goethe Frankfurt University, 60323 Frankfurt am Main, Germany
| | - Pradeep Balachandran
- ITU/WHO Focus Group AI4H, 1211 Geneva, Switzerland
- Technical Consultant (Digital Health), Thiruvananthapuram 695010, India
| | | | - Christoph Lippert
- Digital Engineering Faculty, University of Potsdam, 14482 Potsdam, Germany
- Digital Health & Machine Learning, Hasso Plattner Institute, 14482 Potsdam, Germany
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4
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Vogt F, Kurup KK, Mussleman P, Habrun C, Crowe M, Woodward A, Jaramillo-Gutierrez G, Kaldor J, Vong S, del Rio Vilas V. Contact tracing indicators for COVID-19: Rapid scoping review and conceptual framework. PLoS One 2022; 17:e0264433. [PMID: 35226699 PMCID: PMC8884491 DOI: 10.1371/journal.pone.0264433] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 02/10/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Contact tracing is one of the key interventions in response to the COVID-19 pandemic but its implementation varies widely across countries. There is little guidance on how to monitor contact tracing performance, and no systematic overview of indicators to assess contact tracing systems or conceptual framework for such indicators exists to date. METHODS We conducted a rapid scoping review using a systematic literature search strategy in the peer-reviewed and grey literature as well as open source online documents. We developed a conceptual framework to map indicators by type (input, process, output, outcome, impact) and thematic area (human resources, financial resources, case investigation, contact identification, contact testing, contact follow up, case isolation, contact quarantine, transmission chain interruption, incidence reduction). RESULTS We identified a total of 153 contact tracing indicators from 1,555 peer-reviewed studies, 894 studies from grey literature sources, and 15 sources from internet searches. Two-thirds of indicators were process indicators (102; 67%), while 48 (31%) indicators were output indicators. Only three (2%) indicators were input indicators. Indicators covered seven out of ten conceptualized thematic areas, with more than half being related to either case investigation (37; 24%) or contact identification (44; 29%). There were no indicators for the input area "financial resources", the outcome area "transmission chain interruption", and the impact area "incidence reduction". CONCLUSIONS Almost all identified indicators were either process or output indicators focusing on case investigation, contact identification, case isolation or contact quarantine. We identified important gaps in input, outcome and impact indicators, which constrains evidence-based assessment of contact tracing systems. A universally agreed set of indicators is needed to allow for cross-system comparisons and to improve the performance of contact tracing systems.
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Affiliation(s)
- Florian Vogt
- The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, The Australian Capital Territory, Australia
| | | | - Paul Mussleman
- University Libraries, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Caroline Habrun
- New Mexico Emerging Infections Program, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - Madeleine Crowe
- Health Emergencies Programme, World Health Organization, Geneva, Switzerland
| | - Alexandra Woodward
- Armed Forces Health Surveillance Division, U.S. Department of Defense, Global Emerging Infections Surveillance, Silver Spring, Maryland, United States of America
| | | | - John Kaldor
- The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia
| | - Sirenda Vong
- South East Asia Regional Office, World Health Organization, New Delhi, India
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Jaramillo-Gutierrez G, Wegdam-Blans MC, ter Schegget R, Korbeeck JM, van Aken R, Bijlmer HA, Tjhie JH, Koopmans MP. A dynamic case definition is warranted for adequate notification in an extended epidemic setting: the Dutch Q fever outbreak 2007-2009 as exemplar. ACTA ACUST UNITED AC 2013; 18:20606. [PMID: 24135125 DOI: 10.2807/1560-7917.es2013.18.41.20606] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Q fever is a notifiable disease in the Netherlands:laboratories are obliged to notify possible cases to the Municipal Health Services. These services then try to reconfirm cases with additional clinical and epidemiological data and provide anonymised reports to the national case register of notifiable diseases. Since the start of the 2007–2009 Dutch Q fever outbreak,notification rules remained unchanged, despite new laboratory insights and altered epidemiology. In this study, we retrospectively analysed how these changes influenced the proportion of laboratory-defined acute Q fever cases (confirmed, probable and possible)that were included in the national case register, during(2009) and after the outbreak (2010 and 2011).The number of laboratory-defined cases notified to the Municipal Health Services was 377 in 2009, 96 in 2010 and 50 in 2011. Of these, 186 (49.3%) in 2009, 12(12.5%) in 2010 and 9 (18.0%) in 2011 were confirmed as acute infection by laboratory interpretation. The proportion of laboratory-defined acute Q fever cases that was reconfirmed by the Municipal Health Services and that were included in the national case register decreased from 90% in 2009, to 22% and 24% in 2010 and 2011, respectively. The decrease was observed in all categories of cases, including those considered to be confirmed by laboratory criteria. Continued use ofa pre-outbreak case definition led to over-reporting of cases to the Municipal Health Services in the post-epidemic years. Therefore we recommend dynamic laboratory notification rules, by reviewing case definitions periodically in an ongoing epidemic, as in the Dutch Q fever outbreak.
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Affiliation(s)
- G Jaramillo-Gutierrez
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
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Jaramillo-Gutierrez G, Benschop KS, Claas EC, de Jong AS, van Loon AM, Pas SD, Pontesilli O, Rossen JW, Swanink CM, Thijsen S, van der Zanden AG, van der Avoort HG, Koopmans MP, Meijer A. September through October 2010 multi-centre study in the Netherlands examining laboratory ability to detect enterovirus 68, an emerging respiratory pathogen. J Virol Methods 2013; 190:53-62. [DOI: 10.1016/j.jviromet.2013.02.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Revised: 12/31/2012] [Accepted: 02/06/2013] [Indexed: 10/27/2022]
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7
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Molina-Cruz A, DeJong RJ, Ortega C, Haile A, Abban E, Rodrigues J, Jaramillo-Gutierrez G, Barillas-Mury C. Some strains of Plasmodium falciparum, a human malaria parasite, evade the complement-like system of Anopheles gambiae mosquitoes. Proc Natl Acad Sci U S A 2012; 109:E1957-62. [PMID: 22623529 PMCID: PMC3396512 DOI: 10.1073/pnas.1121183109] [Citation(s) in RCA: 89] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Plasmodium falciparum lines differ in their ability to infect mosquitoes. The Anopheles gambiae L3-5 refractory (R) line melanizes most Plasmodium species, including the Brazilian P. falciparum 7G8 line, but it is highly susceptible to some African P. falciparum strains such as 3D7, NF54, and GB4. We investigated whether these lines differ in their ability to evade the mosquito immune system. Silencing key components of the mosquito complement-like system [thioester-containing protein 1 (TEP1), leucine-rich repeat protein 1, and Anopheles Plasmodium-responsive leucine-rich repeat protein 1] prevented melanization of 7G8 parasites, reverting the refractory phenotype. In contrast, it had no effect on the intensity of infection with NF54, suggesting that this line is able to evade TEP1-mediated lysis. When R females were coinfected with a line that is melanized (7G8) and a line that survives (3D7), the coinfection resulted in mixed infections with both live and encapsulated parasites on individual midguts. This finding shows that survival of individual parasites is parasite-specific and not systemic in nature, because parasites can evade TEP1-mediated lysis even when other parasites are melanized in the same midgut. When females from an extensive genetic cross between R and susceptible A. gambiae (G3) mosquitoes were infected with P. berghei, encapsulation was strongly correlated with the TEP1-R1 allele. However, P. falciparum 7G8 parasites were no longer encapsulated by females from this cross, indicating that the TEP1-R1 allele is not sufficient to melanize this line. Evasion of the A. gambiae immune system by P. falciparum may be the result of parasite adaptation to sympatric mosquito vectors and may be an important factor driving malaria transmission.
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Affiliation(s)
- Alvaro Molina-Cruz
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852
| | - Randall J. DeJong
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852
| | - Corrie Ortega
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852
| | - Ashley Haile
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852
| | - Ekua Abban
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852
| | - Janneth Rodrigues
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852
| | - Giovanna Jaramillo-Gutierrez
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852
| | - Carolina Barillas-Mury
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852
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Meijer A, van der Sanden S, Snijders BEP, Jaramillo-Gutierrez G, Bont L, van der Ent CK, Overduin P, Jenny SL, Jusic E, van der Avoort HGAM, Smith GJD, Donker GA, Koopmans MPG. Emergence and epidemic occurrence of enterovirus 68 respiratory infections in The Netherlands in 2010. Virology 2011; 423:49-57. [PMID: 22177700 DOI: 10.1016/j.virol.2011.11.021] [Citation(s) in RCA: 128] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2011] [Revised: 11/05/2011] [Accepted: 11/23/2011] [Indexed: 12/20/2022]
Abstract
Following an increase in detection of enterovirus 68 (EV68) in community surveillance of respiratory infections in The Netherlands in 2010, epidemiological and virological analyses were performed to investigate the possible public health impact of EV68 infections. We retrospectively tested specimens collected from acute respiratory infections surveillance and through three children cohort studies conducted in The Netherlands from 1994 through 2010. A total of 71 of 13,310 (0.5%) specimens were positive for EV68, of which 67 (94%) were from symptomatic persons. Twenty-four (34%) of the EV68 positive specimens were collected during 2010. EV68-positive patients with respiratory symptoms showed significantly more dyspnea, cough and bronchitis than EV68-negative patients with respiratory symptoms. Phylogenetic analysis showed an increased VP1 gene diversity in 2010, suggesting that the increased number of EV68 detections in 2010 reflects a real epidemic. Clinical laboratories should consider enterovirus diagnostics in the differential diagnosis of patients presenting with respiratory symptoms.
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Affiliation(s)
- Adam Meijer
- Center for Infectious Disease Control, Laboratory for Infectious Diseases and Perinatal Screening, National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA, Bilthoven, The Netherlands.
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9
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Francischetti IMB, Oliveira CJ, Ostera GR, Yager SB, Debierre-Grockiego F, Carregaro V, Jaramillo-Gutierrez G, Hume JCC, Jiang L, Moretz SE, Lin CK, Ribeiro JMC, Long CA, Vickers BK, Schwarz RT, Seydel KB, Iacobelli M, Ackerman HC, Srinivasan P, Gomes RB, Wang X, Monteiro RQ, Kotsyfakis M, Sá-Nunes A, Waisberg M. Defibrotide interferes with several steps of the coagulation-inflammation cycle and exhibits therapeutic potential to treat severe malaria. Arterioscler Thromb Vasc Biol 2011; 32:786-98. [PMID: 22116094 DOI: 10.1161/atvbaha.111.240291] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE The coagulation-inflammation cycle has been implicated as a critical component in malaria pathogenesis. Defibrotide (DF), a mixture of DNA aptamers, displays anticoagulant, anti-inflammatory, and endothelial cell (EC)-protective activities and has been successfully used to treat comatose children with veno-occlusive disease. DF was investigated here as a drug to treat cerebral malaria. METHODS AND RESULTS DF blocks tissue factor expression by ECs incubated with parasitized red blood cells and attenuates prothrombinase activity, platelet aggregation, and complement activation. In contrast, it does not affect nitric oxide bioavailability. We also demonstrated that Plasmodium falciparum glycosylphosphatidylinositol (Pf-GPI) induces tissue factor expression in ECs and cytokine production by dendritic cells. Notably, dendritic cells, known to modulate coagulation and inflammation systemically, were identified as a novel target for DF. Accordingly, DF inhibits Toll-like receptor ligand-dependent dendritic cells activation by a mechanism that is blocked by adenosine receptor antagonist (8-p-sulfophenyltheophylline) but not reproduced by synthetic poly-A, -C, -T, and -G. These results imply that aptameric sequences and adenosine receptor mediate dendritic cells responses to the drug. DF also prevents rosetting formation, red blood cells invasion by P. falciparum and abolishes oocysts development in Anopheles gambiae. In a murine model of cerebral malaria, DF affected parasitemia, decreased IFN-γ levels, and ameliorated clinical score (day 5) with a trend for increased survival. CONCLUSION Therapeutic use of DF in malaria is proposed.
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Affiliation(s)
- Ivo M B Francischetti
- Section of Vector Biology, Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
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10
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Van Kerkhove MD, Vandemaele KAH, Shinde V, Jaramillo-Gutierrez G, Koukounari A, Donnelly CA, Carlino LO, Owen R, Paterson B, Pelletier L, Vachon J, Gonzalez C, Hongjie Y, Zijian F, Chuang SK, Au A, Buda S, Krause G, Haas W, Bonmarin I, Taniguichi K, Nakajima K, Shobayashi T, Takayama Y, Sunagawa T, Heraud JM, Orelle A, Palacios E, van der Sande MAB, Wielders CCHL, Hunt D, Cutter J, Lee VJ, Thomas J, Santa-Olalla P, Sierra-Moros MJ, Hanshaoworakul W, Ungchusak K, Pebody R, Jain S, Mounts AW. Risk factors for severe outcomes following 2009 influenza A (H1N1) infection: a global pooled analysis. PLoS Med 2011; 8:e1001053. [PMID: 21750667 PMCID: PMC3130021 DOI: 10.1371/journal.pmed.1001053] [Citation(s) in RCA: 506] [Impact Index Per Article: 38.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2010] [Accepted: 05/18/2011] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Since the start of the 2009 influenza A pandemic (H1N1pdm), the World Health Organization and its member states have gathered information to characterize the clinical severity of H1N1pdm infection and to assist policy makers to determine risk groups for targeted control measures. METHODS AND FINDINGS Data were collected on approximately 70,000 laboratory-confirmed hospitalized H1N1pdm patients, 9,700 patients admitted to intensive care units (ICUs), and 2,500 deaths reported between 1 April 2009 and 1 January 2010 from 19 countries or administrative regions--Argentina, Australia, Canada, Chile, China, France, Germany, Hong Kong SAR, Japan, Madagascar, Mexico, The Netherlands, New Zealand, Singapore, South Africa, Spain, Thailand, the United States, and the United Kingdom--to characterize and compare the distribution of risk factors among H1N1pdm patients at three levels of severity: hospitalizations, ICU admissions, and deaths. The median age of patients increased with severity of disease. The highest per capita risk of hospitalization was among patients <5 y and 5-14 y (relative risk [RR] = 3.3 and 3.2, respectively, compared to the general population), whereas the highest risk of death per capita was in the age groups 50-64 y and ≥65 y (RR = 1.5 and 1.6, respectively, compared to the general population). Similarly, the ratio of H1N1pdm deaths to hospitalizations increased with age and was the highest in the ≥65-y-old age group, indicating that while infection rates have been observed to be very low in the oldest age group, risk of death in those over the age of 64 y who became infected was higher than in younger groups. The proportion of H1N1pdm patients with one or more reported chronic conditions increased with severity (median = 31.1%, 52.3%, and 61.8% of hospitalized, ICU-admitted, and fatal H1N1pdm cases, respectively). With the exception of the risk factors asthma, pregnancy, and obesity, the proportion of patients with each risk factor increased with severity level. For all levels of severity, pregnant women in their third trimester consistently accounted for the majority of the total of pregnant women. Our findings suggest that morbid obesity might be a risk factor for ICU admission and fatal outcome (RR = 36.3). CONCLUSIONS Our results demonstrate that risk factors for severe H1N1pdm infection are similar to those for seasonal influenza, with some notable differences, such as younger age groups and obesity, and reinforce the need to identify and protect groups at highest risk of severe outcomes. Please see later in the article for the Editors' Summary.
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Affiliation(s)
- Maria D. Van Kerkhove
- Global Influenza Programme, World Health Organization
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | | | - Vivek Shinde
- Global Influenza Programme, World Health Organization
| | | | - Artemis Koukounari
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Christl A. Donnelly
- Medical Research Council Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | | | - Rhonda Owen
- Influenza Surveillance Section, Surveillance Branch, Office of Health Protection, Department of Health and Ageing, Woden, Australia
| | - Beverly Paterson
- Influenza Surveillance Section, Surveillance Branch, Office of Health Protection, Department of Health and Ageing, Woden, Australia
| | - Louise Pelletier
- Influenza Surveillance Section, Public Health Agency of Canada, Ontario, Canada
| | - Julie Vachon
- Influenza Surveillance Section, Public Health Agency of Canada, Ontario, Canada
| | - Claudia Gonzalez
- Departamento de Epidemiología, División de Planificación Sanitaria, Ministerio de Salud de Chile, Santiago, Chile
| | - Yu Hongjie
- Office for Disease Control and Emergency Response, Chinese Center for Disease Control and Prevention Beijing, China
| | - Feng Zijian
- Office for Disease Control and Emergency Response, Chinese Center for Disease Control and Prevention Beijing, China
| | - Shuk Kwan Chuang
- Surveillance and Epidemiology Branch, Centre for Health Protection, Centre for Health Protection of Department of Health, Hong Kong
| | - Albert Au
- Surveillance and Epidemiology Branch, Centre for Health Protection, Centre for Health Protection of Department of Health, Hong Kong
| | - Silke Buda
- Department for Infectious Disease Epidemiology, Robert Koch Institute, Berlin, Germany
| | - Gerard Krause
- Department for Infectious Disease Epidemiology, Robert Koch Institute, Berlin, Germany
| | - Walter Haas
- Department for Infectious Disease Epidemiology, Robert Koch Institute, Berlin, Germany
| | - Isabelle Bonmarin
- Département des Maladies Infectieuses, Institut de Veille, Sanitaire, Saint-Maurice Cedex, France
| | - Kiyosu Taniguichi
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan
| | | | | | | | - Tomi Sunagawa
- Infectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan
| | - Jean Michel Heraud
- Virology Unit, Institut Pasteur from Madagascar, Antananarivo, Madagascar
| | - Arnaud Orelle
- Virology Unit, Institut Pasteur from Madagascar, Antananarivo, Madagascar
| | - Ethel Palacios
- Directorate General of Epidemiology, Mexico City, Mexico
| | - Marianne A. B. van der Sande
- Epidemiology and Surveillance Unit, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - C. C. H. Lieke Wielders
- Epidemiology and Surveillance Unit, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Darren Hunt
- New Zealand Ministry of Health, Wellington, New Zealand
| | - Jeffrey Cutter
- Communicable Diseases Division at the Ministry of Health, Singapore
| | - Vernon J. Lee
- Biodefence Centre, Ministry of Defence, Singapore
- Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Juno Thomas
- Epidemiology and Surveillance Unit, Respiratory Virus Unit, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg, South Africa
| | - Patricia Santa-Olalla
- Coordinating Centre for Health Alerts and Emergencies, Dirección General de Salud Pública y Sanidad Exterior Ministerio de Sanidad y Política Social, Madrid, Spain
| | - Maria J. Sierra-Moros
- Coordinating Centre for Health Alerts and Emergencies, Dirección General de Salud Pública y Sanidad Exterior Ministerio de Sanidad y Política Social, Madrid, Spain
| | | | - Kumnuan Ungchusak
- Department of Disease Control, Ministry of Public Health, Nonthaburi, Thailand
| | | | - Seema Jain
- Epidemiology and Prevention Branch, Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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11
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Anderson JM, Samake S, Jaramillo-Gutierrez G, Sissoko I, Coulibaly CA, Traoré B, Soucko C, Guindo B, Diarra D, Fay MP, Lawyer PG, Doumbia S, Valenzuela JG, Kamhawi S. Seasonality and prevalence of Leishmania major infection in Phlebotomus duboscqi Neveu-Lemaire from two neighboring villages in central Mali. PLoS Negl Trop Dis 2011; 5:e1139. [PMID: 21572984 PMCID: PMC3091838 DOI: 10.1371/journal.pntd.0001139] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2010] [Accepted: 02/20/2011] [Indexed: 11/24/2022] Open
Abstract
Phlebotomus duboscqi is the principle vector of Leishmania major, the causative agent of cutaneous leishmaniasis (CL), in West Africa and is the suspected vector in Mali. Although found throughout the country the seasonality and infection prevalence of P. duboscqi has not been established in Mali. We conducted a three year study in two neighboring villages, Kemena and Sougoula, in Central Mali, an area with a leishmanin skin test positivity of up to 45%. During the first year, we evaluated the overall diversity of sand flies. Of 18,595 flies collected, 12,952 (69%) belonged to 12 species of Sergentomyia and 5,643 (31%) to two species of the genus Phlebotomus, P. duboscqi and P. rodhaini. Of those, P. duboscqi was the most abundant, representing 99% of the collected Phlebotomus species. P. duboscqi was the primary sand fly collected inside dwellings, mostly by resting site collection. The seasonality and infection prevalence of P. duboscqi was monitored over two consecutive years. P. dubsocqi were collected throughout the year. Using a quasi-Poisson model we observed a significant annual (year 1 to year 2), seasonal (monthly) and village effect (Kemena versus Sougoula) on the number of collected P. duboscqi. The significant seasonal effect of the quasi-Poisson model reflects two seasonal collection peaks in May-July and October-November. The infection status of pooled P. duboscqi females was determined by PCR. The infection prevalence of pooled females, estimated using the maximum likelihood estimate of prevalence, was 2.7% in Kemena and Sougoula. Based on the PCR product size, L. major was identified as the only species found in flies from the two villages. This was confirmed by sequence alignment of a subset of PCR products from infected flies to known Leishmania species, incriminating P. duboscqi as the vector of CL in Mali. Female sand flies transmit a parasite called Leishmania that causes a disease called cutaneous leishmaniasis (CL). Several species of sand flies are found in West Africa, but only one species, Phlebotomus duboscqi, has been proven to transmit the parasite. Cutaneous Leishmaniasis has also been reported from Mali, Central West Africa, but the sand fly transmitting the parasite and its annual abundance has not been established, until now. Sand flies were collected during three consecutive years from two neighboring villages in Central Mali, Kemena and Sougoula, where CL is present. P. duboscqi was collected year-round and was the dominant sand fly inside of and surrounding human dwellings. Other sand fly species, known not to be vectors of CL, were primarily found outside the village. Additionally, P. duboscqi females were found infected with L. major, the same Leishmania species identified from human CL cases in Mali. The estimated infection prevalence of P. duboscqi females was 2.7%. Interestingly, the sand fly abundance and infection prevalence was similar in the two villages despite a previous report indicating a disparate L. major exposure rate in humans. This study greatly enhances our knowledge of CL transmission in Mali, poorly studied in this country to date.
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Affiliation(s)
- Jennifer M. Anderson
- Laboratory of Malaria and Vector Research, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Sibiry Samake
- Faculty of Medicine, Pharmacy and Odontostomatology, University of Bamako, Bamako, Mali
| | - Giovanna Jaramillo-Gutierrez
- Laboratory of Malaria and Vector Research, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Ibrahim Sissoko
- Faculty of Medicine, Pharmacy and Odontostomatology, University of Bamako, Bamako, Mali
| | - Cheick A. Coulibaly
- Faculty of Medicine, Pharmacy and Odontostomatology, University of Bamako, Bamako, Mali
| | - Bourama Traoré
- Faculty of Medicine, Pharmacy and Odontostomatology, University of Bamako, Bamako, Mali
| | - Constance Soucko
- Faculty of Science and Technology, University of Bamako, Bamako, Mali
| | - Boubacar Guindo
- Faculty of Medicine, Pharmacy and Odontostomatology, University of Bamako, Bamako, Mali
| | - Dansine Diarra
- Faculty of Medicine, Pharmacy and Odontostomatology, University of Bamako, Bamako, Mali
| | - Michael P. Fay
- Biostatistics Research Branch, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Phillip G. Lawyer
- Intracellular Parasite Biology Section, Laboratory of Parasitic Diseases, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Seydou Doumbia
- Faculty of Medicine, Pharmacy and Odontostomatology, University of Bamako, Bamako, Mali
| | - Jesus G. Valenzuela
- Laboratory of Malaria and Vector Research, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Shaden Kamhawi
- Laboratory of Malaria and Vector Research, National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
- * E-mail:
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12
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Jaramillo-Gutierrez G, Molina-Cruz A, Kumar S, Barillas-Mury C. The Anopheles gambiae oxidation resistance 1 (OXR1) gene regulates expression of enzymes that detoxify reactive oxygen species. PLoS One 2010; 5:e11168. [PMID: 20567517 PMCID: PMC2887368 DOI: 10.1371/journal.pone.0011168] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2009] [Accepted: 05/13/2010] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND OXR1 is an ancient gene, present in all eukaryotes examined so far that confers protection from oxidative stress by an unknown mechanism. The most highly conserved region of the gene is the carboxyl-terminal TLDc domain, which has been shown to be sufficient to prevent oxidative damage. METHODOLOGY/PRINCIPAL FINDINGS OXR1 has a complex genomic structure in the mosquito A. gambiae, and we confirm that multiple splice forms are expressed in adult females. Our studies revealed that OXR1 regulates the basal levels of catalase (CAT) and glutathione peroxidase (Gpx) expression, two enzymes involved in detoxification of hydrogen peroxide, giving new insight into the mechanism of action of OXR1. Gene silencing experiments indicate that the Jun Kinase (JNK) gene acts upstream of OXR1 and also regulates expression of CAT and GPx. Both OXR1 and JNK genes are required for adult female mosquitoes to survive chronic oxidative stress. OXR1 silencing decreases P. berghei oocyst formation. Unexpectedly, JNK silencing has the opposite effect and enhances Plasmodium infection in the mosquito, suggesting that JNK may also mediate some, yet to be defined, antiparasitic response. CONCLUSION The JNK pathway regulates OXR1 expression and OXR1, in turn, regulates expression of enzymes that detoxify reactive oxygen species (ROS) in Anopheles gambiae. OXR1 silencing decreases Plasmodium infection in the mosquito, while JNK silencing has the opposite effect and enhances infection.
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Affiliation(s)
- Giovanna Jaramillo-Gutierrez
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Alvaro Molina-Cruz
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Sanjeev Kumar
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Carolina Barillas-Mury
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
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13
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Jaramillo-Gutierrez G, Rodrigues J, Ndikuyeze G, Povelones M, Molina-Cruz A, Barillas-Mury C. Mosquito immune responses and compatibility between Plasmodium parasites and anopheline mosquitoes. BMC Microbiol 2009; 9:154. [PMID: 19643026 PMCID: PMC2782267 DOI: 10.1186/1471-2180-9-154] [Citation(s) in RCA: 66] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2008] [Accepted: 07/30/2009] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Functional screens based on dsRNA-mediated gene silencing identified several Anopheles gambiae genes that limit Plasmodium berghei infection. However, some of the genes identified in these screens have no effect on the human malaria parasite Plasmodium falciparum; raising the question of whether different mosquito effector genes mediate anti-parasitic responses to different Plasmodium species. RESULTS Four new An. gambiae (G3) genes were identified that, when silenced, have a different effect on P. berghei (Anka 2.34) and P. falciparum (3D7) infections. Orthologs of these genes, as well as LRIM1 and CTL4, were also silenced in An. stephensi (Nijmegen Sda500) females infected with P. yoelii (17XNL). For five of the six genes tested, silencing had the same effect on infection in the P. falciparum-An. gambiae and P. yoelii-An. stephensi parasite-vector combinations. Although silencing LRIM1 or CTL4 has no effect in An. stephensi females infected with P. yoelii, when An. gambiae is infected with the same parasite, silencing these genes has a dramatic effect. In An. gambiae (G3), TEP1, LRIM1 or LRIM2 silencing reverts lysis and melanization of P. yoelii, while CTL4 silencing enhances melanization. CONCLUSION There is a broad spectrum of compatibility, the extent to which the mosquito immune system limits infection, between different Plasmodium strains and particular mosquito strains that is mediated by TEP1/LRIM1 activation. The interactions between highly compatible animal models of malaria, such as P. yoelii (17XNL)-An. stephensi (Nijmegen Sda500), is more similar to that of P. falciparum (3D7)-An. gambiae (G3).
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Affiliation(s)
- Giovanna Jaramillo-Gutierrez
- Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 29892, USA.
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14
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Molina-Cruz A, DeJong RJ, Charles B, Gupta L, Kumar S, Jaramillo-Gutierrez G, Barillas-Mury C. Reactive oxygen species modulate Anopheles gambiae immunity against bacteria and Plasmodium. J Biol Chem 2007; 283:3217-3223. [PMID: 18065421 DOI: 10.1074/jbc.m705873200] [Citation(s) in RCA: 205] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The involvement of reactive oxygen species (ROS) in mosquito immunity against bacteria and Plasmodium was investigated in the malaria vector Anopheles gambiae. Strains of An. gambiae with higher systemic levels of ROS survive a bacterial challenge better, whereas reduction of ROS by dietary administration of antioxidants significantly decreases survival, indicating that ROS are required to mount effective antibacterial responses. Expression of several ROS detoxification enzymes increases in the midgut and fat body after a blood meal. Furthermore, expression of several of these enzymes increases to even higher levels when mosquitoes are fed a Plasmodium berghei-infected meal, indicating that the oxidative stress after a blood meal is exacerbated by Plasmodium infection. Paradoxically, a complete lack of induction of catalase mRNA and lower catalase activity were observed in P. berghei-infected midguts. This suppression of midgut catalase expression is a specific response to ookinete midgut invasion and is expected to lead to higher local levels of hydrogen peroxide. Further reduction of catalase expression by double-stranded RNA-mediated gene silencing promoted parasite clearance by a lytic mechanism and reduced infection significantly. High mosquito mortality is often observed after P. berghei infection. Death appears to result in part from excess production of ROS, as mortality can be decreased by oral administration of uric acid, a strong antioxidant. We conclude that ROS modulate An. gambiae immunity and that the mosquito response to P. berghei involves a local reduction of detoxification of hydrogen peroxide in the midgut that contributes to limit Plasmodium infection through a lytic mechanism.
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Affiliation(s)
- Alvaro Molina-Cruz
- Laboratory of Malaria and Vector Research, NIAID, National Institutes of Health, Rockville, Maryland 20892-8130.
| | - Randall J DeJong
- Laboratory of Malaria and Vector Research, NIAID, National Institutes of Health, Rockville, Maryland 20892-8130
| | - Bradley Charles
- Department of Microbiology, Immunology, and Pathology, Colorado State University, Ft. Collins, Colorado 80523
| | - Lalita Gupta
- Laboratory of Malaria and Vector Research, NIAID, National Institutes of Health, Rockville, Maryland 20892-8130
| | - Sanjeev Kumar
- Laboratory of Malaria and Vector Research, NIAID, National Institutes of Health, Rockville, Maryland 20892-8130
| | | | - Carolina Barillas-Mury
- Laboratory of Malaria and Vector Research, NIAID, National Institutes of Health, Rockville, Maryland 20892-8130
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