High performance exhaled breath biomarkers for diagnosis of lung cancer and potential biomarkers for classification of lung cancer.

Exhaled breath analysis has emerged as a promising non-invasive method for diagnosing lung cancer (LC), whereas reliable biomarkers are lacking. Herein, a standardized and systematic study was presented for LC diagnosis, classification and metabolism exploration. To improve the reliability of biomarkers, a validation group was included, and quality control for breath sampling and analysis, comprehensive pollutants analysis, and strict biomarker screening were performed. The performance of exhaled breath biomarkers was shown to be excellent in diagnosing LC even in early stages (stage I and II) with surpassing 0.930 area under the receiver operating characteristic (ROC) curve (AUC), 90% of sensitivity and 88% of specificity both in the discovery and validation analyses. Meanwhile, in these two groups, diagnosing subtypes of LC attained AUCs over 0.930 and reached 1.00 in the two subtypes of adenocarcinomas. It is demonstrated that the metabolism changes in LC are possibly related to lipid oxidation, gut microbial, cytochrome P450 and glutathione S-transferase, and glutathione pathways change in LC progression. Overall, the reliable biomarkers contribute to the clinical application of breath analysis in screening LC patients as well as those in early stages.

Sol-gel fabrication and performance evaluation of graphene-based hydrophobic solid-phase microextraction fibers for multi-residue analysis of pesticides in water samples.

Widespread use of organophosphorus pesticides poses serious environmental threats, and hence calls for effective analysis methods for these classes of compounds. In this study, a lab-made graphene-based solid-phase microextraction (SPME) fiber was fabricated by the sol-gel method and combined with a gas chromatography-flame photometry detector (GC-FPD) to realize the detection of trace OPPs in water samples. Compared to the commercial fiber coatings, the new sol-gel graphene fiber coatings showed advantages of good durability and solvent resistance, which were attributed to the hydrophobic and antibacterial properties of the functionalized graphene and 3-(trimethoxysilyl)propyldimethyloctadecylammonium chloride (QAS). A headspace SPME method in combination with a GC-FPD was established to evaluate the performance of the novel fibers. The proposed method showed a good linear relationship for the eight OPPs (R2≥ 0.9957) in the concentration range of 1 to 1000 µg L-1, with limits of quantification of 0.11-3.37 µg L-1 and limits of detection of 0.03-1.01 µg L-1. Furthermore, the developed method also exhibited good recoveries for the analysis of OPPs both in rainwater and lake water, which demonstrates that this method is an alternative choice for multi-residue analysis of OPPs, and it has the potential for broader applications in the future.

Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics.

Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians' clinical decision-making. Exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification. In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods, to investigate the ability of exhaled breath to distinguish AC from SCC patients. The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. The result indicated that the KNN classifier combining borderline2-SMOTE and feature reduction methods was the most promising method to discriminate AC from SCC patients and obtained the highest mean area under the receiver operating characteristic curve (0.63) and mean geometric mean (58.50) when compared to others classifiers. The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with multivariate analysis is a promising screening method for informing treatment options and facilitating individualized treatment of lung cancer subtypes patients.

VOC biomarkers identification and predictive model construction for lung cancer based on exhaled breath analysis: research protocol for an exploratory study.

INTRODUCTION: Lung cancer is the most common cancer and the leading cause of cancer death in China, as well as in the world. Late diagnosis is the main obstacle to improving survival. Currently, early detection methods for lung cancer have many limitations, for example, low specificity, risk of radiation exposure and overdiagnosis. Exhaled breath analysis is one of the most promising non-invasive techniques for early detection of lung cancer. The aim of this study is to identify volatile organic compound (VOC) biomarkers in lung cancer and to construct a predictive model for lung cancer based on exhaled breath analysis. METHODS AND ANALYSIS: The study will recruit 389 lung cancer patients in one cancer centre and 389 healthy subjects in two lung cancer screening centres. Bio-VOC breath sampler and Tedlar bag will be used to collect breath samples. Gas chromatography-mass spectrometry coupled with solid phase microextraction technique will be used to analyse VOCs in exhaled breath. VOC biomarkers with statistical significance and showing abilities to discriminate lung cancer patients from healthy subjects will be selected for the construction of predictive model for lung cancer. ETHICS AND DISSEMINATION: The study was approved by the Ethics Committee of Sichuan Cancer Hospital on 6 April 2017 (No. SCCHEC-02-2017-011). The results of this study will be disseminated in presentations at academic conferences, publications in peer-reviewed journals and the news media. TRIAL REGISTRATION NUMBER: ChiCTR-DOD-17011134; Pre-results.

A double-functionalized polymeric ionic liquid used as solid-phase microextraction coating for efficient aromatic amine extraction and detection with gas chromatography-mass spectrometry.

A solid-phase microextraction (SPME) fiber based on a new polymeric ionic liquid was prepared for the extraction of trace aromatic amines in water and their detection by gas chromatography-mass spectrometry (GC-MS). The newly designed polymeric ionic liquid with two functional groups (benzene ring and ether group) was synthesized and fixed on stainless steel wire to effectively extract aromatic amines. Parameters that affect the extraction efficiency of the SPME fiber (extraction temperature, extraction time, alkali concentration, and salt concentration) were optimized to establish a headspace SPME-GC-MS method. The correlation coefficients were 0.996 or greater for concentration of the aromatic amines ranging from 0.01 to 10 µg mL-1. In addition, the limits of detection for the new fiber are as low as 0.67 ng mL-1, which is lower than that obtained with polyacrylate. The relative standard deviations of five consecutive extractions for the solution spiked at 1 µg mL-1 by the same fiber were all below 8.3%, and the interfiber relative standard deviations for the solution spiked at the same concentration ranged from 8.9% to 15.2%. Furthermore, long lifetime and good solvent resistance are exhibited by the fiber. Finally, satisfactory relative recovery in the range from 85.3% to 101.9 % was achieved for two environmental water samples.

Rapid identification and desorption mechanisms of nitrogen-based explosives by ambient micro-fabricated glow discharge plasma desorption/ionization (MFGDP) mass spectrometry.

A novel technique of micro-fabricated glow discharge plasma desorption/ionization mass spectrometry was investigated for the first time in negative ion mode in this study. Negative ion micro-fabricated glow discharge plasma desorption/ionization mass spectrometry (NI-MFGDP-MS) was successfully applied to identify trace explosives in open air. Six explosives and explosives-related compounds were directly analyzed in seconds with this ion source. The ions of [M-H]- were predominant for 2-methyl-1,3,5-trinitrobenzene (trinitrotoluene, TNT) and 2,4,6-trinitrophenol (picric acid), and [M+NO3]- were dominant ions for 1,3,5-trinitro-perhydro-1,3,5-triazine (cyclonite, RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (octogen, HMX), 1,2,3-trinitroxypropane (nitroglycerin, NG), and pentaerythritol tetranitrate (PETN). The limits of detection (LOD) were from 87.5pgmm-2 to 0.4 fg mm-2 and the relative standard deviation (RSD) ranged between 5.8% and 16.8% for the explosives involved in this study. The reliability of NI-MFGDP-MS was characterized by the analysis of a picric acid-RDX-PETN mixture and a mixture of RDX-pond water. NI-MFGDP-MS and ESI-MS were compared with these explosives and along with collision induced dissociation (CID) experiments. The results showed that electron capture, proton abstraction reaction, nucleophilic attack, ion-molecule attachment, decomposition and anion attachment took place during the NI-MFGDP-MS measurement. These findings provide a guideline and a supplement to the chemical libraries for rapid and accurate detection of explosives. The method shows great potential for fast, in situ, on-line and high throughput detection of explosives in the field of antiterrorism.