AuNP-Coated Cotton as VOC Sensor for Disease Detection from Breath

The COVID-19 pandemic outbreak, declared in March 2020, has led to several behavioral changes in the general population, such as social distancing and mask usage among others. Furthermore, the sanitary emergency has stressed health system weaknesses in terms of disease prevention, diagnosis, and cure. Thus, smart technologies allowing for early and quick detection of diseases are called for. In this framework, the development of point-of-care devices can provide new solutions for sanitary emergencies management. This work focuses on the development of useful tools for early disease diagnosis based on nanomaterials on cotton substrates, to obtain a low-cost and easy-to-use detector of breath volatiles as disease markers. Specifically, we report encouraging experimental results concerning acetone detection through impedance measurements. Such findings can pave the way to the implementation of VOCs (Volatile Organic Compounds) sensors into smart and user friendly diagnostic devices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Detection of Post-COVID-19 Patients Using Medical Scent Detection Dogs-A Pilot Study.

There is a growing number of COVID-19 patients experiencing long-term symptoms months after their acute SARS-CoV-2 infection. Previous research proved dogs' ability to detect acute SARS-CoV-2 infections, but has not yet shown if dogs also indicate samples of patients with post-COVID-19 condition (Long COVID). Nine dogs, previously trained to detect samples of acute COVID-19 patients, were confronted with samples of Long COVID patients in two testing scenarios. In test scenario I (samples of acute COVID-19 vs. Long COVID) dogs achieved a mean sensitivity (for acute COVID-19) of 86.7% (95%CI: 75.4-98.0%) and a specificity of 95.8% (95%CI: 92.5-99.0%). When dogs were confronted with Long COVID and negative control samples in scenario IIa, dogs achieved a mean sensitivity (for Long COVID) of 94.4 (95%CI: 70.5-100.0%) and a specificity of 96.1% (95%CI: 87.6-100.0%). In comparison, when acute SARS-CoV-2 positive samples and negative control samples were comparatively presented (scenario IIb), a mean sensitivity of 86.9 (95%CI: 55.7-100.0%) and a specificity of 88.1% (95%CI: 82.7-93.6%) was attained. This pilot study supports the hypothesis of volatile organic compounds (VOCs) being long-term present after the initial infection in post-COVID-19 patients. Detection dogs, trained with samples of acute COVID-19 patients, also identified samples of Long COVID patients with a high sensitivity when presented next to samples of healthy individuals. This data may be used for further studies evaluating the pathophysiology underlying Long COVID and the composition of specific VOC-patterns released by SARS-CoV-2 infected patients throughout the course of this complex disease.

The Use and Potential of Biomedical Detection Dogs During a Disease Outbreak.

Biomedical detection dogs offer incredible advantages during disease outbreaks that are presently unmatched by current technologies, however, dogs still face hurdles of implementation due to lack of inter-governmental cooperation and acceptance by the public health community. Here, we refine the definition of a biomedical detection dog, discuss the potential applications, capabilities, and limitations of biomedical detection dogs in disease outbreak scenarios, and the safety measures that must be considered before and during deployment. Finally, we provide recommendations on how to address and overcome the barriers to acceptance of biomedical detection dogs through a dedicated research and development investment in olfactory sciences.

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.

Discrimination of SARS-CoV-2 Infections From Other Viral Respiratory Infections by Scent Detection Dogs.

Background: Testing of possibly infected individuals remains cornerstone of containing the spread of SARS-CoV-2. Detection dogs could contribute to mass screening. Previous research demonstrated canines' ability to detect SARS-CoV-2-infections but has not investigated if dogs can differentiate between COVID-19 and other virus infections. Methods: Twelve dogs were trained to detect SARS-CoV-2 positive samples. Three test scenarios were performed to evaluate their ability to discriminate SARS-CoV-2-infections from viral infections of a different aetiology. Naso- and oropharyngeal swab samples from individuals and samples from cell culture both infected with one of 15 viruses that may cause COVID-19-like symptoms were presented as distractors in a randomised, double-blind study. Dogs were either trained with SARS-CoV-2 positive saliva samples (test scenario I and II) or with supernatant from cell cultures (test scenario III). Results: When using swab samples from individuals infected with viruses other than SARS-CoV-2 as distractors (test scenario I), dogs detected swab samples from SARS-CoV-2-infected individuals with a mean diagnostic sensitivity of 73.8% (95% CI: 66.0-81.7%) and a specificity of 95.1% (95% CI: 92.6-97.7%). In test scenario II and III cell culture supernatant from cells infected with SARS-CoV-2, cells infected with other coronaviruses and non-infected cells were presented. Dogs achieved mean diagnostic sensitivities of 61.2% (95% CI: 50.7-71.6%, test scenario II) and 75.8% (95% CI: 53.0-98.5%, test scenario III), respectively. The diagnostic specificities were 90.9% (95% CI: 87.3-94.6%, test scenario II) and 90.2% (95% CI: 81.1-99.4%, test scenario III), respectively. Conclusion: In all three test scenarios the mean specificities were above 90% which indicates that dogs can distinguish SARS-CoV-2-infections from other viral infections. However, compared to earlier studies our scent dogs achieved lower diagnostic sensitivities. To deploy COVID-19 detection dogs as a reliable screening method it is therefore mandatory to include a variety of samples from different viral respiratory tract infections in dog training to ensure a successful discrimination process.

COVID-19 lockdown: Effects on selected volatile organic compound (VOC) emissions over the major Indian metro cities.

Due to the COVID-19 pandemic, many countries across the world, including India, have imposed nationwide lockdowns to contain the spread of the virus. Many studies reported that the air quality had improved much due to the lockdown. This study examines the variation of Volatile Organic Compounds (VOCs) over the Indian metropolitan cities during the lockdown period by using ground-based and satellite observations. Ground-based BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) measurements from various metropolitan cities have shown a drastic drop of about 82% in the first phase of lockdown when compared with the pre-lockdown period. Whereas the spatial distribution of formaldehyde (HCHO), obtained from the TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinal-5P satellite, did not show any significant variation due to COVID-19 lockdown, indicating the major source of HCHO is biogenic or pyrogenic. The BTEX ratios were evaluated for a better understanding of the source and photochemical age of the air samples. The ozone forming potential of BTEX in all locations was found reduced; however, the corresponding decrease in ozone concentrations was not observed. The increase in ozone concentrations during the same period indicates alternative sources contributing to ozone formation.