Sniffer dogs as a screening/diagnostic tool for COVID-19: a proof of concept study

Background: Sniffer dogs are able to detect certain chemical particles and are suggest to be capable of helping diagnose some medical conditions and complications, such as colorectal cancer, melanoma, bladder cancer, and even critical states such as hypoglycemia in diabetic patients. With the global spread of COVID-19 throughout the world and the need to have a real-time screening of the population, especially in crowded places, this study aimed to investigate the applicability of sniffer dogs to carry out such a task. Methods: Firstly, three male and female dogs from German shepherd (Saray), German black (Kuzhi) and Labrador (Marco) breeds had been intensively trained throughout the classical conditioning method for 7 weeks. They were introduced to human specimens obtained from the throat and pharyngeal secretions of participants who were already reported positive or negative for SARS-COV-2 infection be RT-PCR. Each dog underwent the conditioning process for almost 1000 times. In the meantime another similar condition process was conducted on clothes and masks of COVID-19 patient using another three male and female dogs from Labrador (Lexi), Border gypsy (Sami), and Golden retriever (Zhico) breeds. In verification test for the first three dogs, 80 pharyngeal secretion samples consisting of 26 positive and 54 negative samples from different medical centers who underwent RT-PCR test were in a single-blind method. In the second verification test for the other three dogs, masks and clothes of 50 RT-PCR positive and 70 RT-PCR negative cases from different medical center were used. Results: In verification test using pharyngeal secretion, the sniffer dogs� detection capability was associated with a 65 of sensitivity and 89 of specificity and they amanged to identify 17 out of the 26 positive and 48 out of the 54 true negative samples. In the next verification test using patients� face masks and clothes, 43 out of the 50 positive samples were correctly identified by the dogs. Moreover, out of the 70 negative samples, 65 samples were correctly found to be negative. The sensitivity of this test was as high as 86 and its specificity was 92.9. In addition, the positive and negative predictive values were 89.6 and 90.3, respectively. Conclusion: Dogs are capable of being trained to identify COVID-19 cases by sniffing their odour, so they can be used as a reliable tool in limited screening. © 2021, The Author(s)

Application of probability estimation models for familial cancer

Many empiric and computer-based risk/probability estimation models have been developed, particularly after the discovery of BRCA1/2 genes, for estimating counselee’s probability of being carrier or to predict her risk of developing breast cancer. In this study, the performances of 6 such models have been compared. These are the Claus and Ford programs from Cyrillic3, BOADICEA, Manchester Scoring System, Tyrer-Cuzick, and COS which is a new model and being validated for the first time in this study. Model pedigrees and 172 Grampian families have been used to ascertain models’ performance. In this study the Claus and Ford models have the highest sensitivity but their extremely low specificities make them useless for any clinical or epidemiological use. In contrast COS and MSS have the second highest sensitivity (94% and 90% respectively) at reasonable specificities (53% and 41% respectively) and PPVs (56% and 49% respectively) showing that they are the most useful models for reducing the likelihood of mutations in BRCA1/2 (where the result is negative) and having the lowest false negative rate. From the ROC plots, COS and MSS also have the highest accuracy (within a range of all possible cut-off points) indicating their ability to discriminate between carriers and non-carriers. BOADICEA and T-C generated the most accurate overall predicted prevalence of mutations for all types of family histories and also increased the likelihood of carrying a mutation. BOADICEA and T-C have a sensitivity of 67%, specificity of 76% and 74% respectively and PPV of 64% and 62% respectively. COS and especially MSS can discriminate between BRCA1- and BRCA2-mutation carriers better than other models. These models identified a larger proportion of BRCA1- and BRCA2-mutation carriers correctly. COS and MSS have the higher sensitivity (73% and 64% respectively) at reasonable specificities (76% and 67% respectively) for the families with 3 or fewer cases of breast cancers in comparison with other models, while BOADICEA and COS have the most reasonable combination of sensitivity (80% and 100% respectively) and specificity (56% and 44% respectively) for the families with 6 or more cases of breast and/or ovarian cancer. Interestingly this study has shown that combined use of COS and MSS would significantly increase the specificity to 66% at the expense of few present loss of sensitivity. The single most effective model for clinical use is COS. However mutation prediction could be further improved if different models for different clinical circumstances (e.g. different family histories) were used. However it is practically cumbersome to have all models available in a busy clinic.EThOS - Electronic Theses Online ServiceGBUnited Kingdo