Assessment of ovarian cancer conditions from exhaled breath

Amal, Haitham; Shi, Dayou; Ionescu, Radu; Zhang, Wei; Hua, Qingling; Pan, Yueyin; Li, Tao; Liu, Hu; Haick, Hossam

doi:10.1002/ijc.29166

Cited by 118 publications

(85 citation statements)

References 38 publications

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“…These VOCs are well-known, documented in the literature, and each single VOC has been reported in different disease states. For example, nonanal was linked to ovarian cancer, 25 inflammatory bowel disease, 45 breast cancer, 61 esophageal and gastric adenocarcinoma. 62 As another example, isoprene could be linked to chronic liver disease, 63 kidney disease, 51 diabetes, 64 and others.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

et al. 2016

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We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development.

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Section: Discussionmentioning

confidence: 99%

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

et al. 2016

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“…Aldehydes are widely used as synthetic precursors for many compounds during industrial production and are volatilized in these processes. Long‐term exposure to aldehyde vapors can cause a variety of ailments such as sick building syndrome, lung cancer, chronic airway inflammation, and so on . Therefore, detection and monitoring of volatile aldehydes have attracted the interest of researchers in various fields.…”

Section: Introductionmentioning

confidence: 99%

Fabrication of Liquid Crystal Droplet Patterns for Monitoring Aldehyde Vapors

Jang

2019

ChemPlusChem

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A sensing method based on the pattern of liquid crystal droplets was developed for detecting and monitoring low levels of organic aldehyde vapors. Exposure of the LC droplet pattern covered with glycine solution to aldehyde vapors induced an optical signal transition from a bright fan shape to a dark cross appearance, as observed by polarized light microscopy. Aldehyde and glycine react at the air/solution interface to form a Schiff‐base compound, which controls the orientation of the LCs and induces a change in the optical signals of the LC droplet pattern. The results show that the glycine/LC droplet pattern system is particularly sensitive and selective to aldehydes. In the actual environment, the sensor is exposed to the aldehyde and the signal transition is completed within a few minutes (2–7 min). The LC‐based method has the advantages of simple construction, easy operation, convenient data reading, and shows excellent prospects for real‐time detection of aldehyde vapors.

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“…It is well known that vapors of aldehydes such as formaldehyde, benzaldehye and nonanal, are typical indoor pollutants related to sick building syndrome [1]. Volatile hexanal and nonanal present in the exhaled breath are identified by many studies as biomarkers related to lung cancer [2,3,4,5]. In addition, as important metabolic intermediates of lipid peroxides, the increased level of nonanal reflects the oxidative stress or chronic airway inflammation [6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Molecularly Imprinted Sol-Gel-Based QCM Sensor Arrays for the Detection and Recognition of Volatile Aldehydes

Liu

Wyszynski

Yatabe

et al. 2017

Sensors

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The detection and recognition of metabolically derived aldehydes, which have been identified as important products of oxidative stress and biomarkers of cancers; are considered as an effective approach for early cancer detection as well as health status monitoring. Quartz crystal microbalance (QCM) sensor arrays based on molecularly imprinted sol-gel (MISG) materials were developed in this work for highly sensitive detection and highly selective recognition of typical aldehyde vapors including hexanal (HAL); nonanal (NAL) and bezaldehyde (BAL). The MISGs were prepared by a sol-gel procedure using two matrix precursors: tetraethyl orthosilicate (TEOS) and tetrabutoxytitanium (TBOT). Aminopropyltriethoxysilane (APT); diethylaminopropyltrimethoxysilane (EAP) and trimethoxy-phenylsilane (TMP) were added as functional monomers to adjust the imprinting effect of the matrix. Hexanoic acid (HA); nonanoic acid (NA) and benzoic acid (BA) were used as psuedotemplates in view of their analogous structure to the target molecules as well as the strong hydrogen-bonding interaction with the matrix. Totally 13 types of MISGs with different components were prepared and coated on QCM electrodes by spin coating. Their sensing characters towards the three aldehyde vapors with different concentrations were investigated qualitatively. The results demonstrated that the response of individual sensors to each target strongly depended on the matrix precursors; functional monomers and template molecules. An optimization of the 13 MISG materials was carried out based on statistical analysis such as principle component analysis (PCA); multivariate analysis of covariance (MANCOVA) and hierarchical cluster analysis (HCA). The optimized sensor array consisting of five channels showed a high discrimination ability on the aldehyde vapors; which was confirmed by quantitative comparison with a randomly selected array. It was suggested that both the molecularly imprinting (MIP) effect and the matrix effect contributed to the sensitivity and selectivity of the optimized sensor array. The developed MISGs were expected to be promising materials for the detection and recognition of volatile aldehydes contained in exhaled breath or human body odor.

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Assessment of ovarian cancer conditions from exhaled breath

Cited by 118 publications

References 38 publications

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

Fabrication of Liquid Crystal Droplet Patterns for Monitoring Aldehyde Vapors

Molecularly Imprinted Sol-Gel-Based QCM Sensor Arrays for the Detection and Recognition of Volatile Aldehydes

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