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Abstract
Much environmental enrichment for laboratory animals is intended to enhance animal welfare and normalcy by providing stimulation to reduce 'boredom'. Behavioural manifestations of boredom include restless sensation-seeking behaviours combined with indicators of sub-optimal arousal. Here we explored whether these signs could be reduced by extra daily play opportunity in laboratory ferrets. Specifically, we hypothesised that playtime would reduce restlessness, aggression, sensation-seeking and awake drowsiness, even 24h later in the homecage. Female ferrets (n = 14) were group housed in enriched multi-level cages. Playtime involved exploring a room containing a ball pool, paper bags, balls containing bells, and a familiar interactive human for 1h. This was repeated on three consecutive mornings, and on the fourth morning, homecage behaviour was compared between ferrets who had experienced the playtime treatment versus control cagemates who had not. Their investigation of stimuli (positive = mouse odour or ball; ambiguous = empty bottle or tea-strainer; and negative = peppermint or bitter apple odour) was also recorded. We then swapped treatments, creating a paired experimental design. Ferrets under control conditions lay awake with their eyes open and screeched significantly more, but slept and sat/stood less, than following playtime. They also contacted negative and ambiguous stimuli significantly more under control conditions than they did following playtime; contact with positive stimuli showed no effects. Attempts to blind the observer to treatments were unsuccessful, so replication is required, but the findings suggest that playtime may have reduced both sub-optimal arousal and restless sensation seeking behaviour, consistent with reducing boredom.
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Affiliation(s)
- Charlotte C Burn
- Animal Welfare Science and Ethics, The Royal Veterinary College, Hertfordshire, UK
| | - Jade Raffle
- Animal Welfare Science and Ethics, The Royal Veterinary College, Hertfordshire, UK
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2
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Kostaki EG, Frampton D, Paraskevis D, Pantavou K, Ferns B, Raffle J, Grant P, Kozlakidis Z, Hadjikou A, Pavlitina E, Williams LD, Hatzakis A, Friedman SR, Nastouli E, Nikolopoulos GK. Near Full-length Genomic Sequencing and Molecular Analysis of HIV-Infected Individuals in a Network-based Intervention (TRIP) in Athens, Greece: Evidence that Transmissions Occur More Frequently from those with High HIV-RNA. Curr HIV Res 2019; 16:345-353. [PMID: 30706819 PMCID: PMC6446520 DOI: 10.2174/1570162x17666190130120757] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 01/21/2019] [Accepted: 01/27/2019] [Indexed: 11/25/2022]
Abstract
Background: TRIP (Transmission Reduction Intervention Project) was a network-based, contact tracing approach to locate and link to care, mostly people who inject drugs (PWID) with recent HIV infection. Objective: We investigated whether sequences from HIV-infected participants with high viral load cluster together more frequently than what is expected by chance. Methods: Paired end reads were generated for 104 samples using Illumina MiSeq next-generation se-quencing. Results: 63 sequences belonged to previously identified local transmission networks of PWID (LTNs) of an HIV outbreak in Athens, Greece. For two HIV-RNA cut-offs (105 and 106 IU/mL), HIV transmissions were more likely between PWID with similar levels of HIV-RNA (p<0.001). 10 of the 14 sequences (71.4%) from PWID with HIV-RNA >106 IU/mL were clustered in 5 pairs. For 4 of these clusters (80%), there was in each one of them at least one sequence from a recently HIV-infected PWID. Conclusion: We showed that transmissions are more likely among PWID with high viremia.
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Affiliation(s)
- Evangelia-Georgia Kostaki
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Daniel Frampton
- Department of Infection and Immunity, UCL, London, United Kingdom
| | - Dimitrios Paraskevis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Bridget Ferns
- NIHR Biomedical Research Centre, UCLH/UCL, London, United Kingdom
| | - Jade Raffle
- Department of Infection and Immunity, UCL, London, United Kingdom
| | - Paul Grant
- Department of Clinical Virology, UCLH, London, United Kingdom
| | - Zisis Kozlakidis
- Division of Infection and Immunity, Faculty of Medical Sciences, UCL and Farr Institute of Health Informatics Research, London, United Kingdom
| | - Andria Hadjikou
- Medical School, University of Cyprus, Nicosia, Cyprus.,European University Cyprus, Nicosia, Cyprus
| | - Eirini Pavlitina
- Transmission Reduction Intervention Project, Athens site, Athens, Greece
| | - Leslie D Williams
- National Development and Research Institutes, New York, United States
| | - Angelos Hatzakis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Samuel R Friedman
- National Development and Research Institutes, New York, United States
| | - Eleni Nastouli
- NIHR Biomedical Research Centre, UCLH/UCL, London, United Kingdom.,Department of Population, Policy and Practice, UCL GOS Institute of Child Health, London, United Kingdom
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3
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Houlihan CF, Frampton D, Ferns RB, Raffle J, Grant P, Reidy M, Hail L, Thomson K, Mattes F, Kozlakidis Z, Pillay D, Hayward A, Nastouli E. Use of Whole-Genome Sequencing in the Investigation of a Nosocomial Influenza Virus Outbreak. J Infect Dis 2018; 218:1485-1489. [PMID: 29873767 PMCID: PMC6151078 DOI: 10.1093/infdis/jiy335] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 06/04/2018] [Indexed: 11/14/2022] Open
Abstract
Traditional epidemiological investigation of nosocomial transmission of influenza involves the identification of patients who have the same influenza virus type and who have overlapped in time and place. This method may misidentify transmission where it has not occurred or miss transmission when it has. We used influenza virus whole-genome sequencing (WGS) to investigate an outbreak of influenza A virus infection in a hematology/oncology ward and identified 2 separate introductions, one of which resulted in 5 additional infections and 79 bed-days lost. Results from WGS are becoming rapidly available and may supplement traditional infection control procedures in the investigation and management of nosocomial outbreaks.
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Affiliation(s)
- Catherine F Houlihan
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Dan Frampton
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - R Bridget Ferns
- Division of Infection and Immunity, University College London, London, United Kingdom
- National Institute for Health Research Biomedical Research Centre, London, United Kingdom
| | - Jade Raffle
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Paul Grant
- Department of Clinical Virology, UCL Hospitals National Health Service Foundation Trust, London, United Kingdom
| | - Myriam Reidy
- Infection Control Service, UCL Hospitals National Health Service Foundation Trust, London, United Kingdom
| | - Leila Hail
- Infection Control Service, UCL Hospitals National Health Service Foundation Trust, London, United Kingdom
| | - Kirsty Thomson
- Department of Blood Diseases, UCL Hospitals National Health Service Foundation Trust, London, United Kingdom
| | - Frank Mattes
- Department of Clinical Virology, UCL Hospitals National Health Service Foundation Trust, London, United Kingdom
| | - Zisis Kozlakidis
- Division of Infection and Immunity, University College London, London, United Kingdom
- Department of Infectious Disease Informatics, Farr Institute of Health Informatics Research, London, United Kingdom
| | - Deenan Pillay
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, United Kingdom
- Department of Infectious Disease Informatics, Farr Institute of Health Informatics Research, London, United Kingdom
| | - Eleni Nastouli
- Department of Population, Policy, and Practice, Great Ormond Street Institute of Child Health, University College London (UCL), London, United Kingdom
- Department of Clinical Virology, UCL Hospitals National Health Service Foundation Trust, London, United Kingdom
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Yebra G, Frampton D, Gallo Cassarino T, Raffle J, Hubb J, Ferns RB, Waters L, Tong CYW, Kozlakidis Z, Hayward A, Kellam P, Pillay D, Clark D, Nastouli E, Leigh Brown AJ. A high HIV-1 strain variability in London, UK, revealed by full-genome analysis: Results from the ICONIC project. PLoS One 2018; 13:e0192081. [PMID: 29389981 PMCID: PMC5794160 DOI: 10.1371/journal.pone.0192081] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [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: 08/31/2017] [Accepted: 12/28/2017] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND & METHODS The ICONIC project has developed an automated high-throughput pipeline to generate HIV nearly full-length genomes (NFLG, i.e. from gag to nef) from next-generation sequencing (NGS) data. The pipeline was applied to 420 HIV samples collected at University College London Hospitals NHS Trust and Barts Health NHS Trust (London) and sequenced using an Illumina MiSeq at the Wellcome Trust Sanger Institute (Cambridge). Consensus genomes were generated and subtyped using COMET, and unique recombinants were studied with jpHMM and SimPlot. Maximum-likelihood phylogenetic trees were constructed using RAxML to identify transmission networks using the Cluster Picker. RESULTS The pipeline generated sequences of at least 1Kb of length (median = 7.46Kb, IQR = 4.01Kb) for 375 out of the 420 samples (89%), with 174 (46.4%) being NFLG. A total of 365 sequences (169 of them NFLG) corresponded to unique subjects and were included in the down-stream analyses. The most frequent HIV subtypes were B (n = 149, 40.8%) and C (n = 77, 21.1%) and the circulating recombinant form CRF02_AG (n = 32, 8.8%). We found 14 different CRFs (n = 66, 18.1%) and multiple URFs (n = 32, 8.8%) that involved recombination between 12 different subtypes/CRFs. The most frequent URFs were B/CRF01_AE (4 cases) and A1/D, B/C, and B/CRF02_AG (3 cases each). Most URFs (19/26, 73%) lacked breakpoints in the PR+RT pol region, rendering them undetectable if only that was sequenced. Twelve (37.5%) of the URFs could have emerged within the UK, whereas the rest were probably imported from sub-Saharan Africa, South East Asia and South America. For 2 URFs we found highly similar pol sequences circulating in the UK. We detected 31 phylogenetic clusters using the full dataset: 25 pairs (mostly subtypes B and C), 4 triplets and 2 quadruplets. Some of these were not consistent across different genes due to inter- and intra-subtype recombination. Clusters involved 70 sequences, 19.2% of the dataset. CONCLUSIONS The initial analysis of genome sequences detected substantial hidden variability in the London HIV epidemic. Analysing full genome sequences, as opposed to only PR+RT, identified previously undetected recombinants. It provided a more reliable description of CRFs (that would be otherwise misclassified) and transmission clusters.
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Affiliation(s)
- Gonzalo Yebra
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom
| | - Dan Frampton
- UCL Division of Infection and Immunity, Faculty of Medical Sciences, London, United Kingdom
| | | | - Jade Raffle
- UCL Division of Infection and Immunity, Faculty of Medical Sciences, London, United Kingdom
- Department of Clinical Virology, UCL Hospital NHS Foundation Trust, London, United Kingdom
| | - Jonathan Hubb
- Department of Virology, Barts Health NHS Trust, London, United Kingdom
| | - R. Bridget Ferns
- Department of Clinical Virology, UCL Hospital NHS Foundation Trust, London, United Kingdom
- NIHR UCLH/UCL Biomedical Research Centre, London, United Kingdom
| | - Laura Waters
- Department of HIV Medicine, Mortimer Market Centre, Central & North West London NHS Trust, London, United Kingdom
| | - C. Y. William Tong
- Department of Virology, Barts Health NHS Trust, London, United Kingdom
- Queen Mary University, London, United Kingdom
| | - Zisis Kozlakidis
- UCL Division of Infection and Immunity, Faculty of Medical Sciences, London, United Kingdom
- UCL Institute of Disease Informatics, Farr Institute of Health Informatics Research, London, United Kingdom
| | - Andrew Hayward
- UCL Institute of Epidemiology and Health Care, London, United Kingdom
| | - Paul Kellam
- Division of Infectious Diseases, Department of Medicine, Imperial College London, London, United Kingdom
| | - Deenan Pillay
- UCL Division of Infection and Immunity, Faculty of Medical Sciences, London, United Kingdom
| | - Duncan Clark
- Department of Virology, Barts Health NHS Trust, London, United Kingdom
- School of Life Sciences, University of Glasgow. Glasgow, United Kingdom
| | - Eleni Nastouli
- Department of Clinical Virology, UCL Hospital NHS Foundation Trust, London, United Kingdom
- Department of Population, Policy and Practice, UCL GOS Institute of Child Health, London, United Kingdom
| | - Andrew J. Leigh Brown
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, United Kingdom
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5
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Garattini C, Raffle J, Aisyah DN, Sartain F, Kozlakidis Z. Big Data Analytics, Infectious Diseases and Associated Ethical Impacts. Philos Technol 2017; 32:69-85. [PMID: 31024785 PMCID: PMC6451937 DOI: 10.1007/s13347-017-0278-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Accepted: 08/02/2017] [Indexed: 12/16/2022]
Abstract
The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in the information accumulation models. These are discussed by comparing the traditional and new models of data accumulation. Big data analytics is fast becoming a crucial component for the modelling of transmission-aiding infection control measures and policies-emergency response analyses required during local or international outbreaks. However, the application of big data analytics in infectious diseases is coupled with a number of ethical impacts. Four key areas are discussed in this paper: (i) automation and algorithmic reliance impacting freedom of choice, (ii) big data analytics complexity impacting informed consent, (iii) reliance on profiling impacting individual and group identities and justice/fair access and (iv) increased surveillance and population intervention capabilities impacting behavioural norms and practices. Furthermore, the extension of big data analytics to include information derived from personal devices, such as mobile phones and wearables as part of infectious disease frameworks in the near future and their potential ethical impacts are discussed. Considered together, the need for a constructive and transparent inclusion of ethical questioning in this rapidly evolving field becomes an increasing necessity in order to provide a moral foundation for the societal acceptance and responsible development of the technological advancement.
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Affiliation(s)
- Chiara Garattini
- Anthropology and UX Research, Health and Life Sciences, Intel, London, UK
| | - Jade Raffle
- Division of Infection and Immunity, University College London, Cruciform Building, Gower Street, London, WC1E 6BT UK
| | - Dewi N Aisyah
- Department of Infectious Disease Informatics, University College London, Farr Institute of Health Informatics Research, 222 Euston Road, London, NW1 2DA UK
| | | | - Zisis Kozlakidis
- Division of Infection and Immunity, University College London, Cruciform Building, Gower Street, London, WC1E 6BT UK
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Harvala H, Frampton D, Grant P, Raffle J, Ferns RB, Kozlakidis Z, Kellam P, Pillay D, Hayward A, Nastouli E. Emergence of a novel subclade of influenza A(H3N2) virus in London, December 2016 to January 2017. ACTA ACUST UNITED AC 2017; 22:30466. [PMID: 28251889 PMCID: PMC5356434 DOI: 10.2807/1560-7917.es.2017.22.8.30466] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 02/23/2017] [Indexed: 12/01/2022]
Abstract
We report the molecular investigations of a large influenza A(H3N2) outbreak, in a season characterised by sharp increase in influenza admissions since December 2016. Analysis of haemagglutinin (HA) sequences demonstrated co-circulation of multiple clades (3C.3a, 3C.2a and 3C.2a1). Most variants fell into a novel subclade (proposed as 3C.2a2); they possessed four unique amino acid substitutions in the HA protein and loss of a potential glycosylation site. These changes potentially modify the H3N2 strain antigenicity.
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Affiliation(s)
- Heli Harvala
- Department of Clinical Virology, University College London Hospitals NHS Foundation Trust, London, United Kingdom.,Department of Infection and Immunity, University College of London, London, United Kingdom
| | - Dan Frampton
- Department of Infection and Immunity, University College of London, London, United Kingdom
| | - Paul Grant
- Department of Clinical Virology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Jade Raffle
- Department of Infection and Immunity, University College of London, London, United Kingdom
| | - Ruth Bridget Ferns
- Department of Infection and Immunity, University College of London, London, United Kingdom.,NIHR UCLH/UCL Biomedical Research Centre, London, United Kingdom
| | - Zisis Kozlakidis
- Department of Infection and Immunity, University College of London, London, United Kingdom
| | - Paul Kellam
- Department of Medicine, Imperial College Faculty of Medicine, London, United Kingdom
| | - Deenan Pillay
- Department of Infection and Immunity, University College of London, London, United Kingdom
| | - Andrew Hayward
- Department of Infectious Disease Informatics, Farr Institute of Health Informatics Research, London, United Kingdom
| | - Eleni Nastouli
- Department of Clinical Virology, University College London Hospitals NHS Foundation Trust, London, United Kingdom.,NIHR UCLH/UCL Biomedical Research Centre, London, United Kingdom.,Department of Population, Policy and Practice, UCL GOS Institute of Child Health, London, United Kingdom
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- The members of these networks are listed at the end of the article
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