The potential of a portable, point-of-care electronic nose to diagnose tuberculosis

Teixeira, Rosarito Coronel; Rodríguez, Mabel; Romero, Nilda; Bruins, Marjan J.; Gomez, Roscio; Yntema, Jan Bart; Abente, Gilberto Chaparro; Gerritsen, Jan W.; Wiegerinck, Wim; Bejerano, Domingo Pérez; Magis-Escurra, Cécile

doi:10.1016/j.jinf.2017.08.003

Cited by 60 publications

(45 citation statements)

References 23 publications

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“…Development of accurate, efficient, and noninvasive methods to detect MTBC in wild swine would greatly benefit surveillance and disease management efforts. Volatile organic compound (VOC) analysis technology is being explored extensively for its potential to detect tuberculosis in humans as well as in other species, such as cattle (Bos taurus) and white-tailed deer (Odocoileus virginianus) [4][5][6][7][8][9][10]. Use of these compounds, obtained from various biological samples such as breath and feces, has great potential to fulfill needs for an ideal remote or point-of-care surveillance tool in all species.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Volatile Organic Compounds Obtained from Breath and Feces to Detect Mycobacterium tuberculosis Complex in Wild Boar (Sus scrofa) in Doñana National Park, Spain

et al. 2020

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The presence of Mycobacterium tuberculosis complex (MTBC) in wild swine, such as in wild boar (Sus scrofa) in Eurasia, is cause for serious concern. Development of accurate, efficient, and noninvasive methods to detect MTBC in wild swine would be highly beneficial to surveillance and disease management efforts in affected populations. Here, we describe the first report of identification of volatile organic compounds (VOC) obtained from the breath and feces of wild boar to distinguish between MTBC-positive and MTBC-negative boar. We analyzed breath and fecal VOC collected from 15 MTBC-positive and 18 MTBC-negative wild boar in Donaña National Park in Southeast Spain. Analyses were divided into three age classes, namely, adults (>2 years), sub-adults (12–24 months), and juveniles (<12 months). We identified significant compounds by applying the two-tailed statistical t-test for two samples assuming unequal variance, with an α value of 0.05. One statistically significant VOC was identified in breath samples from adult wild boar and 14 were identified in breath samples from juvenile wild boar. One statistically significant VOC was identified in fecal samples collected from sub-adult wild boar and three were identified in fecal samples from juvenile wild boar. In addition, discriminant function analysis (DFA) was used to build classification models for MTBC prediction in juvenile animals. Using DFA, we were able to distinguish between MTBC-positive juvenile wild boar and MTBC-negative juvenile wild boar using breath VOC or fecal VOC. Based on our results, further research is warranted and should be performed using larger sample sizes, as well as wild boar from various geographic locations, to verify these compounds as biomarkers for MTBC infection in this species. This new approach to detect MTBC infection in free-ranging wild boar potentially comprises a reliable and efficient screening tool for surveillance in animal populations.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Volatile Organic Compounds Obtained from Breath and Feces to Detect Mycobacterium tuberculosis Complex in Wild Boar (Sus scrofa) in Doñana National Park, Spain

et al. 2020

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“…The study in Bangladesh used a prototype of the e-nose (participants exhaled into a bag, then the bag content was examined using a laboratory version of the e-nose), and with smaller sample size [11]. Other studies with e-nose devices did not diagnose PTB among patients with suspected PTB [12,24,25]. The sensitivity in our study was modest, while specificity was low.…”

Section: Discussionmentioning

confidence: 67%

“…They found sensitivity of 93.5% and a specificity of 85.3% to differentiate PTB patients from healthy controls, and a sensitivity of 76.5% and specificity of 87.2% when differentiating PTB patients from other subjects (non-PTB patients and healthy subjects) [11]. However, this e-nose prototype used separate bags to collect exhaled breath that might introduce errors due to interaction between the Volatile Organic Compounds (VOCs) with the bags materials [12]. A newer device that is portable, use rechargeable battery, and does not use separate bags (Aeonose) was tested in Paraguay, and showed high sensitivity (88%) and specificity (92%) to differentiate PTB patients from asthma/COPD and healthy subjects [12].…”

Section: Introductionmentioning

confidence: 99%

“…However, this e-nose prototype used separate bags to collect exhaled breath that might introduce errors due to interaction between the Volatile Organic Compounds (VOCs) with the bags materials [12]. A newer device that is portable, use rechargeable battery, and does not use separate bags (Aeonose) was tested in Paraguay, and showed high sensitivity (88%) and specificity (92%) to differentiate PTB patients from asthma/COPD and healthy subjects [12].…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity and specificity of an electronic nose in diagnosing pulmonary tuberculosis among patients with suspected tuberculosis

et al. 2019

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Objective To investigate the potency of a hand-held point-of-care electronic-nose to diagnose pulmonary tuberculosis (PTB) among those suspected of PTB. Methods Setting: Lung clinics and Dr. Sardjito Hospital, Yogyakarta, Indonesia. Participants: patients with suspected PTB and healthy controls. Sampling: 5 minutes exhaled breath. Sputum-smear-microscopy, culture, chest-radiography, and follow-up for 1.5–2.5 years, were used to classify patients with suspected PTB as active PTB, probably active PTB, probably no PTB, and no PTB. After building a breath model based on active PTB, no PTB, and healthy controls (Calibration phase), we validated the model in all patients with suspected PTB (Validation phase). In each variable (sex, age, Body Mass Index, co-morbidities, smoking status, consumption of alcohol, use of antibiotics, flu symptoms, stress, food and drink intake), one stratum’s Receiver Operating Characteristic (ROC)-curve indicating sensitivity and specificity of the breath test was compared with another stratum’s ROC-curve. Differences between Area-under-the-Curve between strata ( p <0.05) indicated an association between the variable and sensitivity—specificity of the breath test. Statistical analysis was performed using STATA/SE 15. Results Of 400 enrolled participants, 73 were excluded due to extra-pulmonary TB, incomplete data, previous TB, and cancer. Calibration phase involved 182 subjects, and the result was validated in 287 subjects. Sensitivity was 85% (95%CI: 75–92%) and 78% (95%CI: 70–85%), specificity was 55% (95%CI: 44–65%) and 42% (95%CI: 34–50%), in calibration and validation phases, respectively. Test sensitivity and specificity were lower in men. Conclusion The electronic-nose showed modest sensitivity and low specificity among patients with suspected PTB. To improve the sensitivity, a larger calibration group needs to be involved. With its portable form, it could be used for TB screening in remote rural areas and health care settings.

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“…Ghasemi-Varnamkhasti M [22] et al applied bionic olfaction technology to detect beer aging; Natale C D [23,24] et al applied this technology to detect the freshness of fish. Gobbi E [25,26,27,28] et al applied bionic olfaction technology to colony detection, with high accuracy; Lim J H [29,30] et al applied this technology to medical research. Therefore, the electronic nose has potential in detecting fish meal quality.…”

Section: Introductionmentioning

confidence: 99%

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

Ren

Shao

et al. 2019

Sensors

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In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.

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The potential of a portable, point-of-care electronic nose to diagnose tuberculosis

Cited by 60 publications

References 23 publications

Evaluation of Volatile Organic Compounds Obtained from Breath and Feces to Detect Mycobacterium tuberculosis Complex in Wild Boar (Sus scrofa) in Doñana National Park, Spain

Evaluation of Volatile Organic Compounds Obtained from Breath and Feces to Detect Mycobacterium tuberculosis Complex in Wild Boar (Sus scrofa) in Doñana National Park, Spain

Sensitivity and specificity of an electronic nose in diagnosing pulmonary tuberculosis among patients with suspected tuberculosis

Research on Distinguishing Fish Meal Quality Using Different Characteristic Parameters Based on Electronic Nose Technology

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