Sniffer dogs performance is stable over time in detecting COVID-19 positive samples and agrees with the rapid antigen test in the field

Recapitulating COVID-19 detection methods: RT-PCR, sniffer dogs and electronic nose

In December 2019, a number of subjects presenting with an unexplained pneumonia-like illness were suspected to have a link to a seafood market in Wuhan, China. Subsequently, this illness was identified as the 2019-novel coronavirus (2019-nCoV) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the World Committee on Virus Classification. Since its initial identification, the virus has rapidly sperad across the globe, posing an extraordinary challenge for the medical community. Currently, the Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is considered the most reliable method for diagnosing SARS-CoV-2. This procedure involves collecting oro-pharyngeal or nasopharyngeal swabs from individuals. Nevertheless, for the early detection of low viral loads, a more sensitive technique, such as droplet digital PCR (ddPCR), has been suggested. Despite the high effectiveness of RT-PCR, there is increasing interest in utilizing highly trained dogs and electronic noses (eNoses) as alternative methods for screening asymptomatic individuals for SARS-CoV-2. These dogs and eNoses have demonstrated high sensitivity and can detect volatile organic compounds (VOCs), enabling them to distinguish between COVID-19 positive and negative individuals. This manuscript recapitulates the potential, advantages, and limitations of employing trained dogs and eNoses for the screening and control of SARS-CoV-2.

Olfactory immunology: the missing piece in airway and CNS defence

The olfactory mucosa is a component of the nasal airway that mediates the sense of smell. Recent studies point to an important role for the olfactory mucosa as a barrier to both respiratory pathogens and to neuroinvasive pathogens that hijack the olfactory nerve and invade the CNS. In particular, the COVID-19 pandemic has demonstrated that the olfactory mucosa is an integral part of a heterogeneous nasal mucosal barrier critical to upper airway immunity. However, our insufficient knowledge of olfactory mucosal immunity hinders attempts to protect this tissue from infection and other diseases. This Review summarizes the state of olfactory immunology by highlighting the unique immunologically relevant anatomy of the olfactory mucosa, describing what is known of olfactory immune cells, and considering the impact of common infectious diseases and inflammatory disorders at this site. We will offer our perspective on the future of the field and the many unresolved questions pertaining to olfactory immunity.

Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning

False results and time delay are longstanding challenges in biosensing. While classification models and deep learning may provide new opportunities for improving biosensor performance, such as measurement confidence and speed, it remains a challenge to ensure that predictions are explainable and consistent with domain knowledge. Here, we show that consistency of deep learning classification model predictions with domain knowledge in biosensing can be achieved by cost function supervision and enables rapid and accurate biosensing using the biosensor dynamic response. The impact and utility of the methodology were validated by rapid and accurate quantification of microRNA (let-7a) across the nanomolar (nM) to femtomolar (fM) concentration range using the dynamic response of cantilever biosensors. Data augmentation and cost function supervision based on the consistency of model predictions and experimental observations with the theory of surface-based biosensors improved the F1 score, precision, and recall of a recurrent neural network (RNN) classifier by an average of 13.8%. The theory-guided RNN (TGRNN) classifier enabled quantification of target analyte concentration and false results with an average prediction accuracy, precision, and recall of 98.5% using the initial transient or entire dynamic response, which is indicative of high prediction accuracy and low probability of false-negative and false-positive results. Classification scores were used to establish new relationships among biosensor performance characteristics (e.g., measurement confidence) and design parameters (e.g., inputs and hyperparameters of classification models and data acquisition parameters) that may be used for characterizing biosensor performance.

Animal use in detection of disease within pediatric populations

There is a need for novel techniques for disease detection in humans. Research has shown that using animals for detection of disease is a promising area of study. A literature review was conducted using the terms animals, disease detection, seizures, epilepsy, infectious disease, cancer, and pediatrics to determine the published literature to date of the use of animals in detection of disease. Research studies between 1999-2022 were included in this article. The published studies demonstrate that animals have been used for disease detection in seizures, infectious diseases, Type I diabetes mellitus, and cancer. However, these studies have predominantly focused on the adult patient population. There is limited data available regarding the use of animals in disease detection within pediatrics, which warrants further research into this topic.

COVID-19 scent dog research highlights and synthesis during the pandemic of December 2019-April 2023

CONTEXT

This review was undertaken to provide information concerning the advancement of research in the area of COVID-19 screening and testing during the worldwide pandemic from December 2019 through April 2023. In this review, we have examined the safety, effectiveness, and practicality of utilizing trained scent dogs in clinical and public situations for COVID-19 screening. Specifically, results of 29 trained scent dog screening peer-reviewed studies were compared with results of real-time reverse-transcription polymerase chain reaction (RT-PCR) and rapid antigen (RAG) COVID-19 testing methods.

OBJECTIVES

The review aims to systematically evaluate the strengths and weaknesses of utilizing trained scent dogs in COVID-19 screening.

METHODS

At the time of submission of our earlier review paper in August 2021, we found only four peer-reviewed COVID-19 scent dog papers: three clinical research studies and one preprint perspective paper. In March and April 2023, the first author conducted new literature searches of the MEDLINE/PubMed, Google Scholar, and Cochrane Library websites. Again, the keyword phrases utilized for the searches included "COVID detection dogs," "COVID scent dogs," and "COVID sniffer dogs." The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 Checklist was followed to ensure that our review adhered to evidence-based guidelines for reporting. Utilizing the results of the reviewed papers, we compiled statistics to intercompare and summarize basic information concerning the scent dogs and their training, the populations of the study participants, the types of sampling methods, the comparative tests utilized, and the effectiveness of the scent dog screening.

RESULTS

A total of 8,043 references were identified through our literature search. After removal of duplicates, there were 7,843 references that were screened. Of these, 100 were considered for full-text eligibility, 43 were included for qualitative synthesis, and 29 were utilized for quantitative analysis. The most relevant peer-reviewed COVID-19 scent dog references were identified and categorized. Utilizing all of the scent dog results provided for this review, we found that 92.3 % of the studies reached sensitivities exceeding 80 and 32.0 % of the studies exceeding specificities of 97 %. However, 84.0 % of the studies reported specificities above 90 %. Highlights demonstrating the effectiveness of the scent dogs include: (1) samples of breath, saliva, trachea-bronchial secretions and urine as well as face masks and articles of clothing can be utilized; (2) trained COVID-19 scent dogs can detect presymptomatic and asymptomatic patients; (3) scent dogs can detect new SARS-CoV-2 variants and Long COVID-19; and (4) scent dogs can differentiate SARS-CoV-2 infections from infections with other novel respiratory viruses.

CONCLUSIONS

The effectiveness of the trained scent dog method is comparable to or in some cases superior to the real-time RT-PCR test and the RAG test. Trained scent dogs can be effectively utilized to provide quick (seconds to minutes), nonintrusive, and accurate results in public settings and thus reduce the spread of the COVID-19 virus or other viruses. Finally, scent dog research as described in this paper can serve to increase the medical community's and public's knowledge and acceptance of medical scent dogs as major contributors to global efforts to fight diseases.