Five Things to Know about Genetically Modified (GM) Insects for Vector Control
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{"title"=>"Five Things to Know about Genetically Modified (GM) Insects for Vector Control", "type"=>"journal", "authors"=>[{"first_name"=>"Luke", "last_name"=>"Alphey", "scopus_author_id"=>"7004731464"}, {"first_name"=>"Nina", "last_name"=>"Alphey", "scopus_author_id"=>"17345108600"}], "year"=>2014, "source"=>"PLoS Pathogens", "identifiers"=>{"issn"=>"15537374", "scopus"=>"2-s2.0-84897449024", "sgr"=>"84897449024", "pui"=>"372738302", "isbn"=>"1553-7374 (Electronic)\\r1553-7366 (Linking)", "pmid"=>"24603810", "doi"=>"10.1371/journal.ppat.1003909"}, "id"=>"e4201343-57b0-3bfa-85af-dfe151eb1176", "abstract"=>"Five Things to Know about Genetically Modified (GM) Insects for Vector Control 1. Why (and how to) use GM vectors for vector control? Vector-borne diseases cause immense suffering and economic damage. Vector control remains a key element of mitigation and control strategies, particularly for pathogens such as dengue viruses for which there are no specific drugs or vaccines. Yet existing vector control tools are limited; toxic chemicals are the mainstay but difficult to deliver due to vector behaviour, emerging resistance, and/or environmental concerns. Genetically modified vectors—presently only mosquitoes—offer complementary new approaches to integrate with the best existing methods. Modified mosquitoes will actively seek out wild mosquitoes as mates, with high species specificity and minimal off-target effects. Within this overall scheme, many different genetic modifica-tions have been proposed, all delivered via this mating-based mechanism (''vertical transmission''). These may be classified according to the persistence of the modification: ''self-sustain-ing'' genetic systems are intended to persist or spread invasively in the wild population after an initial release period, while ''self-limiting'' systems will disappear relatively rapidly unless maintained by more releases. Another classification is by intended effect: ''population suppression'' strategies aim, like most current vector control programmes, to reduce the number of vector mosquitoes in the target area, while ''population replacement'' strategies aim to reduce the ability of affected mosquitoes to transmit specified pathogens, with any reduction in total number of mosquitoes being incidental. In either case, the intended result is fewer competent vectors, thereby reducing the force of infection. In computer simulations, several such strategies are capable of eliminating transmission in the programme area. These approaches are not entirely new. Some proposals [1] are simply applications of modern genetics to improve on the classical Sterile Insect Technique (SIT) [2], in which radiation-sterilised insects are released to mate with wild counterparts and thereby reduce the reproductive potential of the target pest population, leading to suppression or even local elimination. SIT has been used successfully on large and small scales against some major agricultural pests. This close relationship to an existing method means that the rollout, use, strengths, and weaknesses of such self-limiting population suppression strategies are fairly predictable and well understood. For self-sustaining strategies, looser analogies may be drawn with classical biological control, in which an exotic predator or parasite is introduced with the intention that it should establish permanently and thereby help control the pest. This analogy highlights both key strengths of self-sustaining systems— potential long-term benefit without further human action—and weaknesses—relative lack of control post-release—relative to self-limiting ones. Simulation modelling is a vital tool to inform strain development and risk assessment and mitigation, especially of the more invasive self-sustaining systems in which release is essentially irreversible.", "link"=>"http://www.mendeley.com/research/five-things-know-about-genetically-modified-gm-insects-vector-control-1", "reader_count"=>90, "reader_count_by_academic_status"=>{"Researcher"=>23, "Student > Doctoral Student"=>6, "Student > Ph. D. Student"=>24, "Student > Postgraduate"=>1, "Student > Master"=>9, "Other"=>5, "Student > Bachelor"=>19, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>23, "Student > Doctoral Student"=>6, "Student > Ph. D. 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Scopus | Further Information

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Relative Metric

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