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1.
Anal Bioanal Chem ; 414(12): 3617-3624, 2022 May.
Article in English | MEDLINE | ID: covidwho-1750681

ABSTRACT

There is an urgent need to have reliable technologies to diagnose post-coronavirus disease syndrome (PCS), as the number of people affected by COVID-19 and related complications is increasing worldwide. Considering the amount of risks associated with the two chronic lung diseases, asthma and chronic obstructive pulmonary disease (COPD), there is an immediate requirement for a screening method for PCS, which also produce symptoms similar to these conditions, especially since very often, many COVID-19 cases remain undetected because a good share of such patients is asymptomatic. Breath analysis techniques are getting attention since they are highly non-invasive methods for disease diagnosis, can be implemented easily for point-of-care applications even in primary health care centres. Electronic (E-) nose technology is coming up with better reliability, ease of operation, and affordability to all, and it can generate signatures of volatile organic compounds (VOCs) in exhaled breath as markers of diseases. The present report is an outcome of a pilot study using an E-nose device on breath samples of cohorts of PCS, asthma, and normal (control) subjects. Match/no-match and k-NN analysis tests have been carried out to confirm the diagnosis of PCS. The prediction model has given 100% sensitivity and specificity. Receiver operating characteristics (ROC) has been plotted for the prediction model, and the area under the curve (AUC) is obtained as 1. The E-nose technique is found to be working well for PCS diagnosis. Our study suggests that the breath analysis using E-nose can be used as a point-of-care diagnosis of PCS.Trial registrationBreath samples were collected from the Kasturba Hospital, Manipal. Ethical clearance was obtained from the Institutional Ethics Committee, Kasturba Medical College, Manipal (IEC 60/2021, 13/01/2021) and Indian Council of Medical Research (ICMR) (CTRI/2021/02/031357, 06/02/2021) Government of India; trials were prospectively registered.


Subject(s)
Asthma , COVID-19 , Volatile Organic Compounds , Asthma/diagnosis , Breath Tests/methods , COVID-19/diagnosis , Electronic Nose , Exhalation , Humans , Pilot Projects , Reproducibility of Results , Technology , Volatile Organic Compounds/analysis
2.
Biosensors (Basel) ; 11(11)2021 Nov 22.
Article in English | MEDLINE | ID: covidwho-1533784

ABSTRACT

(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.


Subject(s)
Breath Tests , Electronic Nose , Biomarkers , Humans , Sensitivity and Specificity
3.
Diagn Microbiol Infect Dis ; 102(2): 115589, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1487685

ABSTRACT

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.


Subject(s)
Breath Tests/methods , COVID-19/diagnosis , Gas Chromatography-Mass Spectrometry , Volatile Organic Compounds/analysis , Electronic Nose , Humans , Mass Screening/methods , SARS-CoV-2/isolation & purification , Sensitivity and Specificity
4.
PLoS One ; 16(6): e0252121, 2021.
Article in English | MEDLINE | ID: covidwho-1256036

ABSTRACT

Rapid diagnosis is key to curtailing the Covid-19 pandemic. One path to such rapid diagnosis may rely on identifying volatile organic compounds (VOCs) emitted by the infected body, or in other words, identifying the smell of the infection. Consistent with this rationale, dogs can use their nose to identify Covid-19 patients. Given the scale of the pandemic, however, animal deployment is a challenging solution. In contrast, electronic noses (eNoses) are machines aimed at mimicking animal olfaction, and these can be deployed at scale. To test the hypothesis that SARS CoV-2 infection is associated with a body-odor detectable by an eNose, we placed a generic eNose in-line at a drive-through testing station. We applied a deep learning classifier to the eNose measurements, and achieved real-time detection of SARS CoV-2 infection at a level significantly better than chance, for both symptomatic and non-symptomatic participants. This proof of concept with a generic eNose implies that an optimized eNose may allow effective real-time diagnosis, which would provide for extensive relief in the Covid-19 pandemic.


Subject(s)
COVID-19/diagnosis , SARS-CoV-2/genetics , Volatile Organic Compounds/analysis , Adult , Deep Learning , Electronic Nose/trends , Female , Humans , Israel/epidemiology , Male , Middle Aged , Pandemics , Proof of Concept Study , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity
5.
Nature ; 589(7843): 630-632, 2021 01.
Article in English | MEDLINE | ID: covidwho-1049956
6.
Surg Endosc ; 35(12): 6671-6678, 2021 12.
Article in English | MEDLINE | ID: covidwho-956162

ABSTRACT

BACKGROUND: Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. METHODS: Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between - 1 and + 1, indicating the infection probability. RESULTS: 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. CONCLUSIONS: The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.


Subject(s)
COVID-19 , SARS-CoV-2 , Electronic Nose , Humans , Mass Screening , Predictive Value of Tests
7.
Isr Med Assoc J ; 22(7): 401-403, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-941868

ABSTRACT

BACKGROUND: There is a high prevalence of olfaction changes, especially in the early presentation, in COVID-19 patients. The mechanisms through which the virus leads to anosmia/hyposmia is still not fully understood. However, olfaction changes could be used as an indication for testing or quarantine. Screening for infections and other diseases by recognizing volatile organic compounds (VOCs) has been previously conducted. Hence, if the coronavirus infection also results in VOCs excretion, physicians could "smell" the virus by using electronic noses. We conducted a literature review on olfaction changes and the COVID-19. Our results suggest that these changes could be used an indication for early testing, even as an isolated symptom. We propose that the electronic nose be used as a future screening tool, especially in agglomeration spaces such as airports, for screening for the COVID-19 infection.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , Electronic Nose , Olfaction Disorders/virology , SARS-CoV-2 , COVID-19/complications , COVID-19 Testing/instrumentation , Humans , Olfaction Disorders/diagnosis
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