Bees can be trained to identify SARS-CoV-2 infected samples

The COVID-19 pandemic has illustrated the need for the development of fast and reliable testing methods for novel, zoonotic, viral diseases in both humans and animals. Pathologies lead to detectable changes in the volatile organic compound (VOC) profile of animals, which can be monitored, thus allowing the development of a rapid VOC-based test. In the current study, we successfully trained honeybees (Apis mellifera) to identify SARS-CoV-2 infected minks (Neovison vison) thanks to Pavlovian conditioning protocols. The bees can be quickly conditioned to respond specifically to infected mink's odours and could therefore be part of a wider SARS-CoV-2 diagnostic system.We tested two different training protocols to evaluate their performance in terms of learning rate, accuracy and memory retention.We designed a non-invasive rapid test in which multiple bees are tested in parallel on the same samples. This provided reliable results regarding a subject's health status. Using the data from the training experiments, we simulated a diagnostic evaluation trial to predict the potential efficacy of our diagnostic test, which yielded a diagnostic sensitivity of 92% and specificity of 86%. We suggest that a honeybee-based diagnostics can offer a reliable and rapid test that provides a readily available, low-input addition to the currently available testing methods. A honeybee-based diagnostic test might be particularly relevant for remote and developing communities that lack the resources and infrastructure required for mainstream testing methods.

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Main Authors: Kontos, Evangelos, Samimi, Aria, Hakze-Van der Honing, Renate W., Priem, Jan, Avargués-Weber, Aurore, Haverkamp, Alexander, Dicke, Marcel, Gonzales, Jose L., van der Poel, Wim H.M.
Format: Article/Letter to editor biblioteca
Language:English
Subjects:Conditioning, Covid-19, Detection, Honeybees, Olfaction, SARS-CoV2,
Online Access:https://research.wur.nl/en/publications/bees-can-be-trained-to-identify-sars-cov-2-infected-samples
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spelling dig-wur-nl-wurpubs-5979462024-10-30 Kontos, Evangelos Samimi, Aria Hakze-Van der Honing, Renate W. Priem, Jan Avargués-Weber, Aurore Haverkamp, Alexander Dicke, Marcel Gonzales, Jose L. van der Poel, Wim H.M. Article/Letter to editor Biology Open 11 (2022) 4 ISSN: 2046-6390 Bees can be trained to identify SARS-CoV-2 infected samples 2022 The COVID-19 pandemic has illustrated the need for the development of fast and reliable testing methods for novel, zoonotic, viral diseases in both humans and animals. Pathologies lead to detectable changes in the volatile organic compound (VOC) profile of animals, which can be monitored, thus allowing the development of a rapid VOC-based test. In the current study, we successfully trained honeybees (Apis mellifera) to identify SARS-CoV-2 infected minks (Neovison vison) thanks to Pavlovian conditioning protocols. The bees can be quickly conditioned to respond specifically to infected mink's odours and could therefore be part of a wider SARS-CoV-2 diagnostic system.We tested two different training protocols to evaluate their performance in terms of learning rate, accuracy and memory retention.We designed a non-invasive rapid test in which multiple bees are tested in parallel on the same samples. This provided reliable results regarding a subject's health status. Using the data from the training experiments, we simulated a diagnostic evaluation trial to predict the potential efficacy of our diagnostic test, which yielded a diagnostic sensitivity of 92% and specificity of 86%. We suggest that a honeybee-based diagnostics can offer a reliable and rapid test that provides a readily available, low-input addition to the currently available testing methods. A honeybee-based diagnostic test might be particularly relevant for remote and developing communities that lack the resources and infrastructure required for mainstream testing methods. en application/pdf https://research.wur.nl/en/publications/bees-can-be-trained-to-identify-sars-cov-2-infected-samples 10.1242/bio.059111 https://edepot.wur.nl/570962 Conditioning Covid-19 Detection Honeybees Olfaction SARS-CoV2 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research
institution WUR NL
collection DSpace
country Países bajos
countrycode NL
component Bibliográfico
access En linea
databasecode dig-wur-nl
tag biblioteca
region Europa del Oeste
libraryname WUR Library Netherlands
language English
topic Conditioning
Covid-19
Detection
Honeybees
Olfaction
SARS-CoV2
Conditioning
Covid-19
Detection
Honeybees
Olfaction
SARS-CoV2
spellingShingle Conditioning
Covid-19
Detection
Honeybees
Olfaction
SARS-CoV2
Conditioning
Covid-19
Detection
Honeybees
Olfaction
SARS-CoV2
Kontos, Evangelos
Samimi, Aria
Hakze-Van der Honing, Renate W.
Priem, Jan
Avargués-Weber, Aurore
Haverkamp, Alexander
Dicke, Marcel
Gonzales, Jose L.
van der Poel, Wim H.M.
Bees can be trained to identify SARS-CoV-2 infected samples
description The COVID-19 pandemic has illustrated the need for the development of fast and reliable testing methods for novel, zoonotic, viral diseases in both humans and animals. Pathologies lead to detectable changes in the volatile organic compound (VOC) profile of animals, which can be monitored, thus allowing the development of a rapid VOC-based test. In the current study, we successfully trained honeybees (Apis mellifera) to identify SARS-CoV-2 infected minks (Neovison vison) thanks to Pavlovian conditioning protocols. The bees can be quickly conditioned to respond specifically to infected mink's odours and could therefore be part of a wider SARS-CoV-2 diagnostic system.We tested two different training protocols to evaluate their performance in terms of learning rate, accuracy and memory retention.We designed a non-invasive rapid test in which multiple bees are tested in parallel on the same samples. This provided reliable results regarding a subject's health status. Using the data from the training experiments, we simulated a diagnostic evaluation trial to predict the potential efficacy of our diagnostic test, which yielded a diagnostic sensitivity of 92% and specificity of 86%. We suggest that a honeybee-based diagnostics can offer a reliable and rapid test that provides a readily available, low-input addition to the currently available testing methods. A honeybee-based diagnostic test might be particularly relevant for remote and developing communities that lack the resources and infrastructure required for mainstream testing methods.
format Article/Letter to editor
topic_facet Conditioning
Covid-19
Detection
Honeybees
Olfaction
SARS-CoV2
author Kontos, Evangelos
Samimi, Aria
Hakze-Van der Honing, Renate W.
Priem, Jan
Avargués-Weber, Aurore
Haverkamp, Alexander
Dicke, Marcel
Gonzales, Jose L.
van der Poel, Wim H.M.
author_facet Kontos, Evangelos
Samimi, Aria
Hakze-Van der Honing, Renate W.
Priem, Jan
Avargués-Weber, Aurore
Haverkamp, Alexander
Dicke, Marcel
Gonzales, Jose L.
van der Poel, Wim H.M.
author_sort Kontos, Evangelos
title Bees can be trained to identify SARS-CoV-2 infected samples
title_short Bees can be trained to identify SARS-CoV-2 infected samples
title_full Bees can be trained to identify SARS-CoV-2 infected samples
title_fullStr Bees can be trained to identify SARS-CoV-2 infected samples
title_full_unstemmed Bees can be trained to identify SARS-CoV-2 infected samples
title_sort bees can be trained to identify sars-cov-2 infected samples
url https://research.wur.nl/en/publications/bees-can-be-trained-to-identify-sars-cov-2-infected-samples
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