The Biology Open journal, researchers showed that Honeybee could be conditioned to detect severe acute respiratory syndrome coronavirus (SARS-CoV-2)-infected samples.

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

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

VOCs harbor an olfactory fingerprint unique to each individual based on sex, age, genetic background, metabolic conditions, and diet. An individual’s health status could be determined by analyzing this odor fingerprint. As a result, VOC investigation has been employed for disease diagnostics in humans/animals, often through feces and breath analysis.

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

The goal of the present research was to analyze how effective trained bees were at detecting animal samples with SARS-CoV-2 infection. The authors used Pavlovian conditioning methods to successfully condition Apis mellifera (honeybees) to identify SARS-CoV-2-infected Neovison vison (minks). The investigators stated that bees could be easily trained to react only to the scents of SARS-CoV-2-infected minks and hence might offer a broader COVID-19 diagnostic approach.

The team compared the effectiveness of two distinct training methods Honeybee for analyzing the bees’ accuracy, memory retention, and learning rate. They devised a non-invasive fast test that involves numerous bees testing the same samples in tandem to obtain accurate information about a subject’s health state

he researchers simulated a diagnostic assessment trial using Honeybee the information derived from the training studies to anticipate the diagnostic test’s probable effectiveness. They used a generalized linear mixed model (GLMM) to assess the learning curve of the bees during conditioning in two protocols. In both protocols, they examined the memory recall of the bees one hour following the conditioning phase. Each bee was tested against three different odors: scents of new-healthy, old-SARS-CoV-2-infected, and new-infected samples from minks.

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

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