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Functions of Essential Oils and Natural Volatiles in Plant-Insect Interactions
Published in K. Hüsnü Can Başer, Gerhard Buchbauer, Handbook of Essential Oils, 2020
Chemical ecologists face at least three major challenges in their quest to identify the natural functions of EOs in plants. The first challenge is to determine which volatile compounds are released into the headspace of living plants, where they are most likely to mediate biological interactions. Natural volatile composition and dosage are important because attraction, repellence, toxicity, growth inhibition, and other functions often are determined by quantitative relationships among EO blend components (Dötterl and Vereecken, 2010; Galen et al., 2011). This largely methodological challenge has been addressed using non-invasive headspace sorption techniques and sensitive analytical methods. These approaches now extend beyond the traditional coupled gas chromatography-mass spectrometry (GC-MS) to include hyphenated GC systems (Mondello et al., 2008; Mitrevski and Marriott, 2012), direct volatile sampling via proton-transfer-reaction mass spectrometry (PTR-MS; Riffell et al., 2014; Farré-Armengol et al., 2016) and tissue surface chemical mapping using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (Kaspar et al., 2011; Shroff et al., 2015).
Insight into Knapsack Metabolite Ecology Database: A Comprehensive Source of Species: Voc-Biological Activity Relationships
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
Azian Azamimi Abdullah, M.D. Altaf-Ul-Amin, Shigehiko Kanaya
Advancement in analytical methods such as gas chromatography-mass spectrometry (GC-MS), proton transfer reaction mass spectrometry (PTR-MS) and selected ion flow tube mass spectrometry (SIFT-MS) have provided an opportunity to identify the volatile metabolites of living organisms in research laboratories. These analytical approaches generate a large amount of data and require specialized mathematical, statistical and bioinformatics tools to analyze such data. Despite the advances in sampling and detection by these analytical methods, only a few databases have been developed to handle these large and complex datasets. There are some VOC databases, which can be accessed freely. However, their applicability is often limited by several elements. Most of these databases only focus on volatiles, which are emitted by certain living organisms and have limited applications. None of these databases provide information on biological activities of VOCs and species-species interaction based on volatiles. To meet this purpose, we have developed a VOC database of microorganisms, fungi, and plants as well as human being, which comprises the relation between emitting species, volatiles and their biological activities (Abdullah et al., 2015). We have deposited the VOC data into KNApSAcK Metabolite Ecology Database, and this database is currently available at http://kanaya.naist.jp/MetaboliteEcology/top.jsp. Also, the database can be accessed online by clicking the corresponding button in the main window (Figure 9.1). Apart from the database development, we also analyzed the VOC data using hierarchical clustering and network clustering based on DPClus algorithm. In addition, we also performed the heatmap clustering based on Tanimoto coefficient as the similarity index between chemical structures to cluster all VOCs emitted by various biological species to understand the relationships between chemical structures of VOCs and their biological activities.
Molecular markers for cervical cancer screening
Published in Expert Review of Proteomics, 2021
Coşkun Güzel, Jenny van Sten-van’t Hoff, Inge M.C.M. de Kok, Natalia I. Govorukhina, Alexander Boychenko, Theo M. Luider, Rainer Bischoff
Another approach that may become interesting in the future, not only for population screening but also for guiding surgery, is REIMS. REIMS was developed for the analysis of aerosols generated during electrosurgery and combined with tandem high-resolution MS was tested on histology samples showing excellent accuracy for discriminating normal, CIN and cancer tissues [65]. A similar approach with LA-REIMS led to the development of a high-throughput screening procedure in cell pellets from LBC samples to discriminate hrHPV positive from negative samples and normal from CIN2+ [66]. Discrimination was most likely based on differences in lipid patterns and analysis used a neural network-based approach. LDI-MS from a plasmonic chip was used for metabolite analysis of serum from patients with cervical cancer and healthy controls [67], and exhaled breath analysis with proton transfer reaction mass spectrometry has also been used as well for this purpose [68]. In both cases, the technology allows upscaling and fast analysis but has not been tested in the context of population screening and the triage of hrHPV positive women.
Cancer breath testing: a patent review
Published in Expert Opinion on Therapeutic Patents, 2018
K. M. Mohibul Kabir, William A. Donald
In 1971, Linus Pauling first demonstrated that normal human breath can contain more than 250 volatile organic compounds (VOCs) [21–23]. Since then, researchers have focused on the diagnosis of various disease conditions such as chronic lung inflammation and infectious diseases by analyzing thousands of VOC biomarkers extracted from human breath samples [22,24–30]. Although a few breath tests, including the analysis of nitric oxide for the inflammation of airways, have been approved by the United States Food and Drug Administration (FDA) for clinical use, most methods have been limited to research laboratories [31]. Most of the breath testing methods that have been disclosed are associated with the detection of VOC biomarkers in breath samples using: (i) spectrometric-based instrumentation, including gas chromatography mass spectrometry (GC-MS) [32,33], proton transfer reaction mass spectrometry (PTR-MS) [34,35], selected ion flow tube mass spectrometry [36], and ion mobility spectrometry (IMS) [37,38]; and (ii) solid state sensors, including quartz crystal microbalance (QCM), surface acoustic wave, and electronic nose (E-nose)-based sensors [39–41]. These techniques will be discussed in detail in Section 3.
Lung cancer breath tests
Published in Expert Review of Respiratory Medicine, 2019
Among all analytical methods, mass spectrometry (MS) has great impact on defining an ‘individual breathprint’. Real-time MS techniques, mainly proton transfer reaction-mass spectrometry (PTR-MS) and selected ion flow tube-mass spectrometry (SIFT-MS), have paved the way to investigate the dynamics of human volatiles without any sample manipulation [9]. An alternative (so far, non-commercial) method to analyze vapors in real-time is based on the exposure of neutral vapors to electrosprays of pure solvent at atmospheric pressure and subsequent mass analysis. These techniques are powerful tools for detecting volatile biomarkers. However, to date, their use has been impeded by the need for expensive equipment, the high level of expertise required to operate such instruments, the speed required for sampling and analysis, and the need for preconcentration techniques. For breath testing to become a clinical reality, several advances in the knowledge of specific lung cancer VOCs and sensor development need to occur. Chemical sensor matrices are more likely to become a clinical and laboratory diagnostic tool, because they are significantly smaller, easier-to-use, and less expensive [9]. An ideal chemical sensor for volatile biomarker analysis should be sensitive at very low analyte concentrations in the presence of water vapor, because headspace of clinical samples is fully humidified. Furthermore, it should respond rapidly and differently to small changes in concentration and provide a consistent output that is specific to a given exposure. When not in contact with the analyte, the sensor should return to its baseline state rapidly, or be simple and inexpensive enough to manufacture large numbers of units.