Canine scent detection of canine cancer: a feasibility study

Dogs' ability to detect an inflammatory immune response in cattle via olfaction

Introduction

Canine olfaction is a potential means for detection of respiratory disease in beef cattle. In a prior study, two dogs were trained to discriminate between nasal swabs from healthy cattle and cattle that developed Bovine Respiratory Disease. Dogs had some ability to identify samples from BRD-affected cattle, but results were ambiguous. The purpose of this study was to evaluate more dogs using better-controlled training and testing procedures.

Methods

Nasal and saliva swabs were collected from 96 cattle before and after administering a vaccine to induce an inflammatory immune response. Samples were stored at -80°C for up to 11 months before use, and samples from animals with an elevated body temperature at baseline were omitted. An automated olfactometer apparatus was constructed to improve blinding procedures and reduce opportunities for odor contamination. Four dogs were trained to distinguish between swabs from healthy and sickness-model cattle, including the two dogs from the previous study ("Runnels" and "Cheaps") and two inexperienced dogs ("Molokai" and "Amy"). During a seven-month training period, dogs were exposed to samples from 28 animals. Dogs were tested on 59 sets of unfamiliar samples.

Results

Performance varied among dogs (χ2 = 10.48, p = 0.02). Molokai's performance was above chance (0.73 ± 0.06, p = 0.0006), while Amy (0.44 ± 0.06, p = 0.43), Cheaps (0.53 ± 0.07, p = 0.79), and Runnels (0.56 ± 0.06, p = 0.43) did not respond correctly at a rate different from chance. Accuracy did not differ between nasal swabs (0.63 ± 0.08) and saliva swabs (0.53 ± 0.08, χ2 = 0.81, p = 0.37).

Discussion

The results of this study indicate that canine olfaction may be an effective means of detecting illness in beef cattle. However, individual dogs' aptitude for this detection task varies.

Exhaled VOCs can discriminate subjects with COVID-19 from healthy controls

COVID-19 detection currently relies on testing by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing. However, SARS-CoV-2 is expected to cause significant metabolic changes in infected subjects due to both metabolic requirements for rapid viral replication and host immune responses. Analysis of volatile organic compounds (VOCs) from human breath can detect these metabolic changes and is therefore an alternative to RT-PCR or antigen assays. To identify VOC biomarkers of COVID-19, exhaled breath samples were collected from two sample groups into Tedlar bags: negative COVID-19 (n= 12) and positive COVID-19 symptomatic (n= 14). Next, VOCs were analyzed by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry. Subjects with COVID-19 displayed a larger number of VOCs as well as overall higher total concentration of VOCs (p< 0.05). Univariate analyses of qualified endogenous VOCs showed approximately 18% of the VOCs were significantly differentially expressed between the two classes (p< 0.05), with most VOCs upregulated. Machine learning multivariate classification algorithms distinguished COVID-19 subjects with over 95% accuracy. The COVID-19 positive subjects could be differentiated into two distinct subgroups by machine learning classification, but these did not correspond with significant differences in number of symptoms. Next, samples were collected from subjects who had previously donated breath bags while experiencing COVID-19, and subsequently recovered (COVID Recovered subjects (n= 11)). Univariate and multivariate results showed >90% accuracy at identifying these new samples as Control (COVID-19 negative), thereby validating the classification model and demonstrating VOCs dysregulated by COVID are restored to baseline levels upon recovery.

Working Dog Training for the Twenty-First Century

Dogs are trained for a variety of working roles including assistance, protection, and detection work. Many canine working roles, in their modern iterations, were developed at the turn of the 20th century and training practices have since largely been passed down from trainer to trainer. In parallel, research in psychology has advanced our understanding of animal behavior, and specifically canine learning and cognition, over the last 20 years; however, this field has had little focus or practical impact on working dog training. The aims of this narrative review are to (1) orient the reader to key advances in animal behavior that we view as having important implications for working dog training, (2) highlight where such information is already implemented, and (3) indicate areas for future collaborative research bridging the gap between research and practice. Through a selective review of research on canine learning and behavior and training of working dogs, we hope to combine advances from scientists and practitioners to lead to better, more targeted, and functional research for working dogs.

Training with Multiple Structurally Related Odorants Fails to Improve Generalization of Ammonium Nitrate Detection in Domesticated Dogs (Canis familiaris)

Simple Summary

Domestic dogs are used by military and police forces to detect improvised explosive devices (IEDs) and other explosives. A challenge with training explosive detection dogs is that the ingredients used by someone to make an IED can vary. It is therefore critical that dogs be able to detect an IED with unfamiliar ingredients. This ability can be improved if the dog’s training allows them to categorize similar odors together. Many IEDs are created using ammonium nitrate, which was the focus of our study. Based on preliminary odor training performance, we equally assigned dogs to two experimental groups. Dogs in the first group were trained with two odors related to ammonium nitrate, while dogs in the second group were trained to six related odors. We anticipated that dogs trained to six odors would be more likely to form a category. However, this was not the case since dogs in both experimental groups were unable to form a category that allowed them to identify a novel ammonium nitrate mixture. Based on our results, the use of authentic explosive materials likely remains the most cost-effective and efficient way to train explosive scent detection dogs.

Abstract

A critical aspect of canine scent detection involves the animal’s ability to respond to odors based on prior odor training. In the current study, dogs (n = 12) were initially trained on an olfactory simple discrimination task using vanillin as the target odorant. Based on their performance on this task, dogs were assigned to experimental groups. Dogs in group 1 and 2 (n = 5 dogs/group; 1 dog/group were removed due to low motivation or high error rates) were trained with either two or six forms of ammonium nitrate (AN), respectively. Dogs were then assessed with a mock explosive with AN and powdered aluminum. Dogs in both groups failed to respond to the novel AN-aluminum odor. Mean success rates were 56 ± 5 and 54 ± 4% for groups 1 and 2, respectively. Overall, and individual dog performance was not statistically higher than chance indicating that dogs did not generalize from AN to a similar AN-based odorant at reliable levels desired for explosive detection dogs. These results suggest the use of authentic explosive materials, without the added complication of including category-learning methods, likely remains a cost-effective and efficient way to train explosive scent detection dogs.

Methodological Considerations in Canine Olfactory Detection Research

Dogs are increasingly used in a wide range of detection tasks including explosives, narcotics, medical, and wildlife detection. Research on detection dog performance is important to understand olfactory capabilities, behavioral characteristics, improve training, expand deployment practices, and advance applied canine technologies. As such, it is important to understand the influence of specific variables on the quantification of detection dog performance such as test design, experimental controls, odor characteristics, and statistical analysis. Methods for testing canine scent detection vary influencing the outcome metrics of performance and the validity of results. Operators, management teams, policy makers, and law enforcement rely on scientific data to make decisions, design policies, and advance canine technologies. A lack of scientific information and standardized protocols in the detector dog industry adds difficulty and inaccuracies when making informed decisions about capability, vulnerability, and risk analysis. Therefore, the aim of this review is to highlight important methodological issues and expand on considerations for conducting scientifically valid detection dog research.