Correlation between BOD/COD Ratio and Octanol/Water Partition Coefficient for Mixture Organic Compounds

Correlation between the BOD/COD ratio and Partition coefficient of octanol/ water (Pow) on a single organic substance shows that the Pow value is directly proportional to the toxicity level and inversely proportional to BOD/COD ratio. This research examined the correlation to a mixture of organic substances. The objective is to obtain a varied range of substances, as well as determining the quality of wastewater discharging to fresh waters. Need for analysis of organic substances used as antiseptics during the Covid-19 pandemic. In addition, organic substances from the organophosphate pesticide class, diazinon, were used. BOD5, COD, Pow, and LC50-96h toxicity tests using Daphnia magna were used. Six types of the mixture of organic substances included diazinon-formaldehyde-isopropyl alcohol, ethanol-oxalic acid-formaldehyde, isopropyl alcohol-glycerol-lactose, acetic acid-isopropyl alcohol-formaldehyde, sucrose-glycerol-acetic acid, and oxalic acid-formaldehyde-diazinon, with 3 different concentrations of 10, 100, and 1000 mg/L, three repetitions. The lowest BOD/COD ratio (<0.2) and the highest Pow value (>4) are found in diazinon-formaldehyde-IPA. Its toxicity in D. magna also showed the lowest LC-50 (11.82 mg/L). Whereas, sucrose-glycerol-acetic acid had the highest BOD/COD ratio (>0.7) and lowest Pow (<0.7) with the highest LC-50 (567.88 mg/L). Other organic substances mixtures have characteristics in the range of these mixtures. Pow variability and the BOD/COD ratio have a negative correlation. A mixture of organic matter is more biodegradable making it has a higher tendency to dissolve in water.Copyright © 2022 Informatics Publishing Limited and The Society of Toxicology. All rights reserved.

Measuring the quantity of harmful volatile organic compounds inhaled through masks.

An increase in the concentration of environmental particulate matter and the spread of the COVID-19 virus have dramatically increased our time spent wearing masks. If harmful chemicals are released from these masks, there may be harmful effects on human health. In this study, the concentration of volatile organic compounds (VOCs) emitted from some commonly used masks was assessed qualitatively and quantitatively under diverse conditions (including different mask material types, time between opening the product and wearing, and mask temperature). In KF94 masks, 1-methoxy-2-propanol (221 ± 356 µg m-3), N,N-dimethylacetamide (601 ± 450 µg m-3), n-hexane (268 ± 349 µg m-3), and 2-butanone (160 ± 244 µg m-3) were detected at concentrations 22.9-147 times higher than those found in masks made from other materials, such as cotton and other functional fabrics. In addition, in KF94 masks, the total VOC (TVOC) released amounted to 3730 ± 1331 µg m-3, about 14 times more than that released by the cotton masks (267.5 ± 51.6 µg m-3). In some KF94 masks, TVOC concentration reached over 4000 µg m-3, posing a risk to human health (based on indoor air quality guidelines established by the German Environment Agency). Notably, 30 min after KF94 masks were removed from their packaging, TVOC concentrations decreased by about 80% from their initial levels to 724 ± 5.86 µg m-3; furthermore, 6 h after removal, TVOC concentrations were found to be less than 200 µg m-3. When the temperature of the KF94 masks was raised to 40 oC, TVOC concentrations increased by 119-299%. Since the types and concentrations of VOCs that will be inhaled by mask wearers vary depending on the mask use conditions, it is necessary to comply with safe mask wearing conditions.

Virus-induced breath biomarkers: A new perspective to study the metabolic responses of COVID-19 vaccinees.

Coronavirus disease 2019 (COVID-19) vaccines can protect people from the infection; however, the action mechanism of vaccine-mediated metabolism remains unclear. Herein, we performed breath tests in COVID-19 vaccinees that revealed metabolic reprogramming induced by protective immune responses. In total, 204 breath samples were obtained from COVID-19 vaccinees and non-vaccinated controls, wherein numerous volatile organic compounds (VOCs) were detected by comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry system. Subsequently, 12 VOCs were selected as biomarkers to construct a signature panel using alveolar gradients and machine learning-based procedure. The signature panel could distinguish vaccinees from control group with a high prediction performance (AUC, 0.9953; accuracy, 94.42%). The metabolic pathways of these biomarkers indicated that the host-pathogen interactions enhanced enzymatic activity and microbial metabolism in the liver, lung, and gut, potentially constituting the dominant action mechanism of vaccine-driven metabolic regulation. Thus, our findings of this study highlight the potential of measuring exhaled VOCs as rapid, non-invasive biomarkers of viral infections. Furthermore, breathomics appears as an alternative for safety evaluation of biological agents and disease diagnosis.

Analysis of Volatile Organic Compounds in Exhaled Breath of COVID-19 Patients

Background: More than 2 years since COVID-19's first cases were reported in 2019. Diagnosis of COVID-19 is a key to controlling the pandemic. Sample for COVID-19 testing is collected by naso-oro-pharyngeal swab. This procedure is often uncomfortable and requires a trained examiner. Exhaled breath contains thousands of volatile organic compounds (VOC) which are likely to change during infection. Aims and objectives: This study aims to analyze the difference of VOC in the exhaled breath between COVID-19 and healthy subjects. Method(s): A cross-sectional study was carried out recruiting 90 confirmed cases of COVID-19 and 42 healthy subjects. A sample of exhaled breath was collected by using a 500 ml airbag in both groups. Contained VOC was analyzed using an arrayed sensor breath analyzer to quantify the concentration of CO2, C7H8, C6H14, CH2O, NH4, TVOC, NO2, PM1.0, CO, NH3and Acetone. Statistical analysis was conducted using Mann whitney test. Result(s): The median of CO2, C6H14, NH4, TVOC, NO2, and Acetone are significantly higher in COVID-19 patients compared to healthy subjects (respectively 1175.1 vs 607.3, 0.47 vs 0.0, 1.05 vs 0.0, 146.6 vs 0.05, 1.55 vs 0.04, and 0.23 vs 0.0) while C7H8, CH2O, PM1.0, CO, and NH3are significantly lower (respectively 0.0 vs 0.92, 0.01 vs 0.55, 0.0 vs 4.13, 0.0 vs 0.24, and 0.67 vs 1.99;all with p-value of <0.05.). Furthermore, we found NH4, Acetone, NH3, and CO are positively correlate with severity of COVID-19. Conclusion(s): COVID-19 patients emit distinctive VOC profiles in comparison with healthy subjects.

Health risk assessment of volatile organic compounds (VOCs) emitted from landfill working surface via dispersion simulation enhanced by probability analysis

The assessment of the health risks of volatile organic compounds (VOCs) emitted from landfills via dispersion model is crucial but also challenging because of remarkable variations in their emissions and meteorological conditions. This study used a probabilistic approach for the assessment of the health risks of typical VOCs by combining artificial neural network models for emission rates and a numerical dispersion model enhanced by probability analysis. A total of 8753 rounds of simulation were performed with distributions of waste compositions and the valid hourly meteorological conditions for 1 year. The concentration distributions and ranges of the typical health-risky VOCs after dispersion were analyzed with 95% probability. The individual and cumulative non-carcinogenic risks of the typical VOCs were acceptable with all values less than 1 in the whole study domain. For individual carcinogenic risks, only ethylbenzene, benzene, chloroform, and 1, 2-dichloroethane at extreme concentrations showed minor or moderate risks with a probability of 0.1%-1% and an impact distance of 650-3000 m at specific directions. The cumulative carcinogenic risks were also acceptable at 95% probability in the whole study domain, but exceeded 1 x 10-6 or even 1 x 10-4 at some extreme conditions, especially within the landfill area. The vertical patterns of the health risks with height initially increased, and then decreased rapidly, and the peak values were observed around the height of the emission source. The dispersion simulation and health risk assessment of the typical health-risky VOCs enhanced by Monte Carlo can accurately reflect their probabilistic dispersion patterns and health risks to surrounding residents from both spatial and temporal dimensions. With this approach, this study can provide important scientific basis and technical support for the health risk assessment and management of landfills.Copyright © 2022 Elsevier Ltd

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.

OCCUCON 2022 Abstracts

The proceedings contain 40 papers. The topics discussed include: respiratory hypersensitivity profiling among farmers with pesticide exposure: field- based, cross-sectional study;requirements of prescription safety eye wear;Covid-19 and comorbidities: deleterious impact on infected patients;knowledge regarding heat stress and practice of personal protective equipment use among healthcare workers during the Covid 19 pandemic;arrhythmia burden in Covid-19 patients from industrial workforce evaluated by remote patient monitoring technology;a qualitative perspective of construction site migrant workers' plight during covid-19 lockdown in Bhavnagar (Western India);elimination of volatile organic compound VOCs exposure at chemical testing laboratory: through effective OHIH assessment;and perceived morbidity, its risks and catastrophic health expenditure among construction workers: a cross sectional observation from Ahmedabad.

Detection of COVID-19 in hospitalised patients by exhaled breath analysis

Introduction: SARS-COV-2 is mainly transmitted through respiratory droplets. The standard diagnostic procedure is based on a reverse transcription polymerase chain reaction (RT-PCR). Aim(s): 1) To develop a safe and easy to perform breath test for the detection of COVID-19 in hospitalised patients based on the analysis of volatile organic compounds (VOCs) in exhaled breath. 2) To differentiate in hospitalised patients with respiratory symptoms those with and without COVID-19. Method(s): We performed a monocenter, cross-sectional, case-control study in 38 subjects (63% males, age 62+/-12.7 yrs) admitted at the pulmonology ward. Breath samples were taken using a home-made sampling system. Analysis of breath samples was performed by proton transfer high resolution mass spectrometry (PTR-HRMS). A lassoregression with leave-one-out cross-validation was performed to differentiate the groups and designate the most differentiating VOCs. Result(s): COVID-19 positive (n=22) and control respiratory patients (n=16) were similar with respect to baseline characteristics, except for lower blood neutrophil and lymphocyte counts and higher ferritin level in COVID+ve patients (p<0.05). Lasso-regression revealed 6 VOCs as potential biomarkers that differentiated between both groups with 84% accuracy, 100% specificity and 100% positive predictive value based on PTR-HRMS data. Conclusion(s): Breath analysis could identify a breathprint differentiating between hospitalised COVID-19 and nonCOVID-19 patients with respiratory symptoms with a good accuracy. Therefore, VOCs profiling could be integrated in sensors allowing a fast breathalyzer for COVID-19 for large-scale screening.

Volatile Organic Compounds in Patients' Breath during Conversation

Purpose: The protracted coronavirus disease (COVID-19) pandemic has caused an unprecedented global health, social, economic, and psychological crisis. COVID-19 is transmitted via droplets, which include volatile organic compounds (VOCs) emitted by COVID-19 carriers. As a result, medical healthcare workers interacting with COVID-19 patients are at a high risk of infection. In this study, we measured the concentration of total VOCs (TVOCs) in the droplets of patients during conversations. Method(s): Thirty patients aged 20-88 years were enrolled in this study. The amounts of VOCs, formaldehyde (HCHO), and carbon dioxide (CO2) as surrogate parameters for the patient's droplets were measured at a distance of 1 m from the patients under the following conditions: 1) no conversation with a mask on, 2) conversation with a mask on, 3) conversation without a mask on, and 4) no conversation without a mask on. Result(s): The average concentrations of TVOCs (mg/m3 ), HCHO (mg/m3 ), and CO2 (ppm) were all the lowest before the masked conversation (1.79 +/- 1.72, 0.25 +/- 0.25, 1193 +/- 516), increased during the masked conversation (1.99 +/- 1.87, 0.29 +/- 0.24, 1288 +/- 555), were the highest during the unmasked conversation (3.10 +/- 1.86, 0.45 +/- 0.28, 1705 +/- 729), and decreased to baseline after the unmasked conversation (1.89 +/- 1.88, 0.26 +/- 0.27, 1191 +/- 518, respectively). Variations in TVOC and HCHO concentrations were positively correlated with patient age (TVOC: r = 0.42, p = 0.019 and HCHO: r = 0.47, p = 0.008). Conclusion(s): Wearing a mask reduced the VOC concentrations measured during conversations more than when a mask was not worn. Therefore, wearing a mask can reduce the emission of airborne droplet-derived VOCs and thereby reduce the risk of transmission of unknown patient-derived infections. Clinical Trial Registration no: The Clinical Trial Registration no: (UMIN000039595).Copyright © 2023 Ito et al.

Intelligent COVID-19 screening platform based on breath analysis.

The spread of coronavirus disease 2019 (COVID-19) results in an increasing incidence and mortality. The typical diagnosis technique for severe acute respiratory syndrome coronavirus 2 infection is reverse transcription polymerase chain reaction, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic. Here we constructed an intelligent system based on the volatile organic compound (VOC) biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p< 0.001) between the COVID-19 and the control groups. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection. The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and anF1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network,k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model. The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.

Accuracy of Volatile Organic Compound (VOC) Detection in Exhaled Breath Compared to Reverse-transcriptase Polymerase Chain Reaction (RT-PCR) for Diagnosis of COVID-19: An Evidence-based Case Report

Background: Coronavirus disease 2019 (COVID-19) is a contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) declared this infection a global pandemic in 2020. In addition, various methods have been developed to diagnose COVID-19 rapidly and accurately to reverse transcription-polymerase chain re-action (RT-PCR) as a gold standard method. One of these methods is the detection of volatile organic compounds (VOC) in exhaled breath. Objectives: The aim was to collect and investigate studies on the accuracy of VOC detection as a diagnostic method for COVID-19. Methods: A literature search was performed in five electronic databases, including PubMed, Cochrane Library, ProQuest, EBSCO-host, and Scopus, along with hand searching. The search was conducted in the titles and s of articles using keywords and their equivalent terms, combined with the Boolean operators (OR and AND). The search results were then selected according to the inclusion and exclusion criteria and compatibility with the Population, Intervention, Control, and Outcomes (PICO) framework. Results: Based on the search results, two cross-sectional studies by Wintjens et al. and Ruszkiewicz et al. were selected, which were then critically appraised. Both studies showed good validity. Wintjens et al. reported 86% sensitivity and 54% specificity for their method, with a positive predictive value (PPV) and a negative predictive value (NPV) of 40% and 92%, respectively. Besides, Ruszkiewicz et al., who conducted a study in two different locations, reported 82.4% sensitivity and 75% specificity for their method in Edinburgh (UK), with PPV and NPV of 87.5% and 66.7%, respectively, while they reported 90% sensitivity and 80% specificity in Dortmund (Germany), with PPV and NPV of 45% and 97.8%, respectively. The accuracy of these three methods was 62%, 80%, and 82%, respectively. Conclusions: Detection of VOCs from exhaled breath can be a rapid, cost-effective, and simple method for diagnosing COVID-19. However, the accuracy of this method is still relatively low (62 -82%) and inconsistent;therefore, it is only recommended for screen-ing.

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.

Rethinking of the adverse effects of NOx-control on the reduction of methane and tropospheric ozone – Challenges toward a denitrified society

Nitrogen oxides (NOx = NO + NO2) are key precursors of tropospheric ozone (O3) together with volatile organic compounds (VOC) and carbon monoxide (CO). Since O3 has positive radiative forcing and is harmful to human health, the reduction of anthropogenic emissions of NOx is thought to be beneficial from the perspectives of climate change and air pollution in principle. However, there have been discussions contending that the reduction of NOx emissions is not necessarily beneficial for the mitigation of climate change and improvement of air quality, since 1) it decreases the atmospheric mixing ratio of hydroxyl radicals (OH), which increases the atmospheric lifetime of methane (CH4), and 2) O3 formation is VOC-limited in urban areas and the decrease of NOx emission would increases urban O3 by facilitating the NO titration effect. In order to scrutinize such discussion, literature review have been made on the temporal variations of the increasing rate of tropospheric CH4 in the last 30 years, and on urban/rural O3 issues related to the NOx-limited/VOC-limited regime. Based on the review, it may be concluded that the variation of emissions of CH4 itself paly a dominant role, and the variation of consumption rate by OH play a minor role for the recent variation of CH4. It has been suggested that NOx and NMVOC should be reduced simultaneously in order to avoid the adverse effect on climate change mitigation. From the review on policy-related discussion of NOx-limited and VOC-limited O3 formation, the increase of O3 by the decrease in NOx emissions has generally been seen in winter and nighttime when photochemical production is minimal, and the higher percentile or diurnal maximum mixing ratios of O3 in summer tends to decrease with the decrease in NOx emissions. We suggested that the NOx-limited/VOC-limited approach is not appropriate as a long-term policy guideline for ozone control, since it is unreasonable that NOx reduction is not recommended when ambient NOx levels are high, while further NOx reduction is recommended only when the VOC/NOx ratio gets high after NOx control has been achieved based on other policy principle. Simultaneous reduction of NOx and NMVOC would be beneficial for reducing global, regional, and urban O3 to alleviate climate change and human health impacts. The ultimate reduction of anthropogenic emissions of NOx can be envisioned toward a denitrified (de-NOx) society along with a decarbonized (de-CO2) society.

Social and environmental benefits of virtual fracture clinics in trauma and orthopaedic surgery: Reduced patient Travel Time

Aim: Several papers have analysed the clinical benefits and safety of Virtual Fracture Clinics (VFCs). A significant increase in the use of Trauma and Orthopaedic (T&O) VFCs was seen during the COVID-19 pandemic. This study aims to investigate the social impact of VFCs on the travel burden and travel costs of T&O patients, as well as the potential environmental benefits in relation to fuel consumption and travelrelated pollutant emissions. Method: All patients referred for T&O VFC review from March 2020 to June 2020 were retrospectively analysed. The travel burden and environmental impacts of hypothetical face-to-face consultations were compared with these VFC reviews. The primary outcomes measured were patient travel time saved, patient travel distance saved, patient cost savings and reduction in air-pollutant emissions. Results: Over a four-month period, 1359 VFC consultations were conducted. The average travel distance saved by VFC review was 88.6 kilometres (range 3.3-615), with an average of 73 minutes (range 9-390) of travel-time saved. Patients consumed, on average, 8.2 litres (range 0.3-57.8) less fuel and saved an average of e11.02 (range 0.41-76.59). The average reduction in air-pollutant vehicle emissions, including carbon dioxide, carbon monoxide, nitric oxides and volatile organic compounds was 20.3 kilograms (range 0.8-140.8), 517.3 grams (g) (range 19.3-3592.3), 38.1g (range 1.4-264.8) and 56.9g (range 2.1-395.2), respectively. Conclusions: VFCs reduce patient travel distance, travel time and travel costs. In addition, VFCs confer significant environmental benefits through reduced fuel consumption and reduction of harmful environmental emissions.

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.

Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis.

(1) Background: An electronic nose applies a sensor array to detect volatile biomarkers in exhaled breath to diagnose diseases. The overall diagnostic accuracy remains unknown. The objective of this review was to provide an estimate of the diagnostic accuracy of sensor-based breath tests for the diagnosis of diseases. (2) Methods: We searched the PubMed and Web of Science databases for studies published between 1 January 2010 and 14 October 2021. The search was limited to human studies published in the English language. Clinical trials were not included in this review. (3) Results: Of the 2418 records identified, 44 publications were eligible, and 5728 patients were included in the final analyses. The pooled sensitivity was 90.0% (95% CI, 86.3-92.8%, I2 = 47.7%), the specificity was 88.4% (95% CI, 87.1-89.5%, I2 = 81.4%), and the pooled area under the curve was 0.93 (95% CI 0.91-0.95). (4) Conclusion: The findings of our review suggest that a standardized report of diagnostic accuracy and a report of the accuracy in a test set are needed. Sensor array systems of electronic noses have the potential for noninvasiveness at the point-of-care in hospitals. Nevertheless, the procedure for reporting the accuracy of a diagnostic test must be standardized.

The potential of volatile organic compounds-based breath analysis for COVID-19 screening: a systematic review & meta-analysis.

COVID-19 is a major problem with an increasing incidence and mortality. The discovery of Volatile Organic Compounds (VOCs) based on breath analysis offers a reliable, rapid, and affordable screening method. This study examined VOC-based breath analysis diagnostic performance for SARS-COV-2 infection compared to RT-PCR. A systematic review was conducted in 8 scientific databases based on the PRISMA guideline. Original English studies evaluating human breaths for COVID-19 screening and mentioning sensitivity and specificity value compared to RT-PCR were included. Six studies were included with a total of 4093 samples from various settings. VOCs-based breath analysis had the cumulative sensitivity of 98.2% (97.5% CI 93.1%-99.6%) and specificity of 74.3% (97.5% CI 66.4%-80.9%). Subgroup analysis on chemical analysis (GC-MS) and pattern recognition (eNose) revealed higher sensitivity in the eNose group. VOC-based breath analysis shows high sensitivity and promising specificity for COVID-19 public screening.