Diagnosis of SARS-CoV-2 based on bees

In a recent study published on Biology Open journal, the researchers showed that honey bees could be conditioned to detect samples infected with the severe acute respiratory syndrome coronavirus (SARS-CoV-2).

Study: Bees can be trained to identify samples infected with SARS-CoV-2. Image Credit: Vera Larina/Shutterstock

Background

The SARS-CoV-2 pandemic has highlighted the importance of developing accurate and rapid diagnostic procedures for novel zoonotic viral diseases in animals and humans. Pathologies cause measurable changes in an animal’s volatile organic compound (VOC) pattern, which can be tracked and used to construct a rapid VOC-based diagnosis.

VOCs harbor an olfactory imprint unique to each individual depending on sex, age, genetic heritage, metabolic conditions and diet. An individual’s state of health could be determined by analyzing this olfactory fingerprint. Accordingly, the investigation of VOCs has been used for the diagnosis of disease in humans/animals, often by the analysis of feces and breath.

According to previous research, animals can identify variations in VOCs released by healthy animals/humans and those infected with SARS-CoV-2 at the individual level. Dogs were trained to distinguish people with and without CoV 2019 (COVID-19) disease with high diagnostic sensitivity and specificity. Nevertheless, bees could be a viable alternative to dogs for detecting SARS-CoV-2 infection due to their minimal maintenance and availability costs.

About the study

The objective of the present research was to analyze the efficiency of trained bees in detecting samples from animals infected with SARS-CoV-2. The authors used Pavlovian conditioning methods to successfully condition Apis mellifera (bees) to identify people infected with SARS-CoV-2 Neovison mink (mink). Investigators said bees could be easily trained to react only to odors from mink infected with SARS-CoV-2 and could therefore offer a broader COVID-19 diagnostic approach.

The team compared the effectiveness of two separate training methods to analyze the accuracy, memory retention and learning rate of bees. They designed a non-invasive rapid test that involves many bees testing the same samples in tandem to get precise information about a subject’s health status.

Photo of conditioning procedure during protocol 1. A single bee harnessed inside our custom-made bee carrier.  The bee has just been exposed to a positive sample and given a wooden stick soaked in sugar water, causing it to express the PER.

Photo of conditioning procedure during protocol 1. A single bee harnessed inside our custom-made bee carrier. The bee has just been exposed to a positive sample and given a wooden stick soaked in sugar water, causing it to express the PER.

Researchers simulated a diagnostic evaluation trial using information derived from training studies to anticipate the likely effectiveness of the diagnostic test. They used a Generalized Linear Mixed Model (GLMM) to assess the learning curve of bees during conditioning in two protocols. In both protocols, they examined the memory recall of bees one hour after the conditioning phase. Each bee was tested against three different odors: odors from newly healthy, old SARS-CoV-2 infected, and newly infected mink samples.

The researchers also analyzed the proportion of bees with the proboscis extension reflex (PER) based on the cycle threshold (Ct) values ​​of the infected samples. They studied the distribution of simulated diagnostic results using a group of 10 bees as a diagnostic instrument per sample.

Results

According to the results, bees could distinguish between SARS-CoV-2-infected and uninfected samples based on smell differences. The bees differentiated between samples from healthy individuals and those from patients with COVID-19. Even though the bees’ ability to distinguish between new infected and uninfected samples declined between one and 24 hours after training, the scientists found they were still able to do so a day later.

Additionally, the SARS-CoV-2 load of the samples, indicated by the Ct values, did not affect the ability of the bees to detect a COVID-19 positive sample. The bees were just as good at recognizing samples with higher Ct values, suggesting a reduced viral load, as those with lower Ct values ​​used for training. Scientists predicted that honey bees could be effective for the diagnosis of COVID-19 with a projected specificity and sensitivity of 86% and 92%, respectively, after performing simulations of the possible clinical application of honey bees as a diagnostic tool. screening.

Additionally, protocol 1 was shorter than protocol 2 and required no unpleasant unconditioned stimulus (US) and few samples during conditioning, making it a faster technique to train bees. However, unlike protocol 2, protocol 1 did not allow the bees to be able to distinguish between newly infected samples and new healthy samples, implying that the old protocol was more efficient in conditioning the samples. bees for diagnostic screening of SARS-CoV-2.

conclusion

The present research highlighted the screening capability of a bee-based diagnostic tool for the diagnosis of COVID-19. Furthermore, the study results showed that the developed honeybee-based diagnostic test for SARS-CoV-2 infection exhibited significant diagnostic specificity and sensitivity.

According to the authors, current bee-based diagnostics could result in a reliable and rapid test that is readily available and requires little input compared to currently available screening techniques. Furthermore, such diagnostic analysis could be particularly important in developing and remote communities without the infrastructure and resources required for conventional testing procedures.

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