SARS-CoV-2 is not found in the sweat of COVID-19 positive patients

Arslan, Bengi; Berçin, Sami; Aydoğan, Sibel; İslamoğlu, Yüce; Dinç, Bedia

doi:10.1007/s11845-021-02537-y

Cited by 11 publications

(10 citation statements)

References 20 publications

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“…However, studies conducted so far failed to demonstrate the sweat as a possible route of transmission. 8,9 We investigated the presence of SARS-CoV-2 in sweat, as a possible under-considered potential mechanism of virus transmission. Twenty-two patients (9 males, 13 females, mean age 73.5 years), hospitalized for acute COVID-19, were tested for the presence of SARS-CoV-2 in sweat.…”

Section: Sars-cov-2 In the Sweat Of Covid-19-positive Patients: A Pos...mentioning

confidence: 99%

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SARS‐CoV‐2 in the sweat of COVID‐19‐positive patients: a possible route of transmission?

Recalcati

Tonolo

Meroni

et al. 2021

Acad Dermatol Venereol

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Section: Sars-cov-2 In the Sweat Of Covid-19-positive Patients: A Pos...mentioning

confidence: 99%

“…However, studies conducted so far failed to demonstrate the sweat as a possible route of transmission. 8 , 9 …”

mentioning

confidence: 99%

SARS‐CoV‐2 in the sweat of COVID‐19‐positive patients: a possible route of transmission?

Recalcati

Tonolo

Meroni

et al. 2021

Acad Dermatol Venereol

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show abstract

“…Based on former results showing that beta-propiolactone (BPL) inactivation does not interfere with scent dog detection and for easier and safer handling of samples, most positive and negative samples were BPL inactivated as formerly described 8 14. As sweat samples from infected patients are not considered to pose a relevant infection risk,26 also non-inactivated sweat samples from SARS-CoV-2 positive and negative individuals were presented. These samples were collected during the training period at the cooperating test centre and used directly in training.…”

Section: Methodsmentioning

confidence: 99%

Canine real-time detection of SARS-CoV-2 infections in the context of a mass screening event

et al. 2022

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IntroductionPrevious research demonstrated that medical scent detection dogs have the ability to distinguish SARS-CoV-2 positive from negative samples with high diagnostic accuracy. To deploy these dogs as a reliable screening method, it is mandatory to examine if canines maintain their high diagnostic accuracy in real-life screening settings. We conducted a study to evaluate the performance of medical scent detection dogs under real-life circumstances.MethodsEight dogs were trained to detect SARS-CoV-2 RT-qPCR-positive samples. Four concerts with a total of 2802 participants were held to evaluate canines’ performance in screening individuals for SARS-CoV-2 infection. Sweat samples were taken from all participants and presented in a line-up setting. In addition, every participant had been tested with a SARS-CoV-2 specific rapid antigen test and a RT-qPCR and they provided information regarding age, sex, vaccination status and medical disease history. The participants’ infection status was unknown at the time of canine testing. Safety measures such as mask wearing and distance keeping were ensured.ResultsThe SARS-CoV-2 detection dogs achieved a diagnostic specificity of 99.93% (95% CI 99.74% to 99.99%) and a sensitivity of 81.58% (95% CI 66.58% to 90.78%), respectively. The overall rate of concordant results was 99.68%. The majority of the study population was vaccinated with varying vaccines and vaccination schemes, while several participants had chronic diseases and were under chronic medication. This did not influence dogs’ decisions.ConclusionOur results demonstrate that SARS-CoV-2 scent detection dogs achieved high diagnostic accuracy in a real-life scenario. The vaccination status, previous SARS-CoV-2 infection, chronic disease and medication of the participants did not influence the performance of the dogs in detecting the acute infection. This indicates that dogs provide a fast and reliable screening option for public events in which high-throughput screening is required.

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“…Then it takes a sample from sweat in the human axilla and stores the VOCs contained in the sweat. This device became safer by taking samples through the axilla [10].…”

Section: Introductionmentioning

confidence: 99%

Detection of Infectious Respiratory Disease Through Sweat From Axillary Using an E-Nose With Stacked Deep Neural Network

et al. 2022

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Several methods have been used to detect infectious respiratory diseases, for example, by taking samples from blood, saliva, and phlegm. Although these methods generated high accuracy, they raised more problems that increased the risk of spreading and required more time to detect. Therefore, an accurate, quick, and low-cost device is required to help detect infectious respiratory diseases. This study proposes a new approach for detecting infectious respiratory diseases using an electronic nose (E-nose) through sweat samples from the human axilla. E-nose became safer by taking samples through the axillary because infectious respiratory diseases are not transmitted through sweat. This study proposes two new feature extraction techniques called stable value and highest slope. This study also proposes a stacked Deep Neural Network (DNN) for effective infectious respiratory disease detection. In the proposed stacked DNN, five fine-tuned DNN models obtained from hyperparameter tuning are stacked then the output of each DNN model became the input of the meta-model in the form of a fully connected layer. The proposed feature extraction method outperformed the existing feature extraction and was able to separate data between classes better. Furthermore, the proposed stacked DNN model generated an accuracy of 0.934 in the testing data, outperforming DNN single models and other state-of-the-art machine learning algorithms.

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SARS-CoV-2 is not found in the sweat of COVID-19 positive patients

Cited by 11 publications

References 20 publications

SARS‐CoV‐2 in the sweat of COVID‐19‐positive patients: a possible route of transmission?

SARS‐CoV‐2 in the sweat of COVID‐19‐positive patients: a possible route of transmission?

Canine real-time detection of SARS-CoV-2 infections in the context of a mass screening event

Detection of Infectious Respiratory Disease Through Sweat From Axillary Using an E-Nose With Stacked Deep Neural Network

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