Investigation of volatile organic metabolites in lung cancer pleural effusions by solid-phase microextraction and gas chromatography/mass spectrometry

Determination of hexanal and heptanal in saliva samples by an adapted magnetic headspace adsorptive microextraction for diagnosis of lung cancer

In this work, an analytical method for the determination of two endogenous aldehydes (hexanal and heptanal) as lung cancer biomarkers in saliva samples is presented for the first time. The method is based on a modification of magnetic headspace adsorptive microextraction (M-HS-AME) followed by gas chromatography coupled to mass spectrometry (GC-MS). For this purpose, an external magnetic field generated by a neodymium magnet is used to hold the magnetic sorbent (i.e., CoFe₂O₄ magnetic nanoparticles embedded into a reversed-phase polymer) in the headspace of a microtube to extract the volatilized aldehydes. Subsequently, the analytes are desorbed in the appropriate solvent and the extract is injected into the GC-MS system for separation and determination. Under the optimized conditions, the method was validated and showed good analytical features in terms of linearity (at least up to 50 ng mL^-1), limits of detection (0.22 and 0.26 ng mL^-1 for hexanal and heptanal, respectively), and repeatability (RSD ≤12%). This new approach was successfully applied to saliva samples from healthy volunteers and those with lung cancer, obtaining notably differences between both groups. These results reveal the prospect of the method as potential diagnostic tool for lung cancer by saliva analysis. This work contributes to the Analytical Chemistry field presenting a double novelty: on the one hand, the use of M-HS-AME in bioanalysis is unprecedentedly proposed, thus expanding the analytical potential of this technique, and, on the other hand, the determination of hexanal and heptanal is carried out in saliva samples for the first time.

Continuous real-time monitoring of carbon dioxide emitted from human skin by quartz-enhanced photoacoustic spectroscopy

In this study, a skin gas detection system based on quartz enhanced photoacoustic spectroscopy (QEPAS) with a constant temperature collection chamber and an automatic frequency adjustment function was used to collect and monitor carbon dioxide (CO₂) emissions from human skin. The detection element of the system is an on-beam structure assembled by a 30.72 kHz quartz tuning fork (QTF). A laser with a wavelength of 4991.26 cm^-1 is emitted (with a wavelength adjustment range of 10 cm^-1) to excite the QTF. When the integration time is 365 s, the system can achieve a minimum detection limit (MDL) of 2.6 ppmv. The sensitivity of the system is 636.9 ppmv/V. The gas detection system is used to monitor the concentration of CO₂ emissions from different parts of the skin and the same part covered by different cosmetics. The CO₂ emission rate is defined as the ratio of the skin gas monitoring time of 25 min to the CO₂ concentration variable in the gas chamber (volume of 8 mL). The results were collected from three healthy volunteers. Among the six different parts, the cheeks emitted the fastest rate (the average rate was 365.5 ppmv/min) of CO₂, and the thighs emitted the slowest rate (the average rate was 56.4 ppmv/min) of CO₂. Comparing the experimental results of the six sites at different times, the order of the CO₂ emission rate is identical for all six sites. In the experiments with the three cosmetic products (experimental site: forearm), comparing the CO₂ emission rate from clean skin with the CO₂ emission rate from cosmetic-covered skin shows that sunscreen is the most breathable, followed by barrier cream, and foundation is the least breathable.

Identification of phenol 2,2-methylene bis, 6 [1,1-D] as breath biomarker of hepatocellular carcinoma (HCC) patients and its electrochemical sensing: E-nose biosensor for HCC

BACKGROUND

According to WHO, Hepatocellular cancer (HCC) was the second leading cause of death in 2019 and is gradually increasing. The lipid peroxidation mechanism in cancer cells causes the emission of VOCs in the breath. Volatile organic compounds (VOCs) in breath are becoming favorable biomarkers, especially for cancers, for their sample retrieval and specific association with early metabolic changes. Since both diagnosis and prognosis of the disease depend on the quantity and kind of circulatory biomarkers to be detected, sensitive and selective biosensors with the possibility for portability are constantly in demand.

RESULTS

In this study, breath samples of HCC patients were screened for identification of VOCs via GCMS and later verified by applying unsupervised machine learning models. Phenol 2,2 methylene bis [6-(1,1-dimethyl ethyl)-4-methyl] (MBMBP) was found to be significant VOC in the breath of HCC patients, with a minimum concentration of 2100 ppm. Thiol-modified AuNPs were synthesized, as we reported earlier, and immobilized on the working electrode surface to electrochemically sense MBMBP in purified form and later from clinical breath samples. During the electrochemical experiment of AuNPs with MPMBP, the analyte gets electro-oxidized, whereas the Au (III) ions get reduced to the phenoxy radical's species. The electrochemical analysis of MBMBP detection using hexane thiol AuNPs showed a LOD of 0.005 molL ¹. The thiolated AuNPs-based biosensor for HCC diagnosis via VOC detection confirmed MPMBP in lab standards and raw clinical breath samples of HCC patients.

SIGNIFICANCE

This study reveals that GCE modified with hexanethiol AuNPs for the adsorption of significant breath biomarker, is a potential platform for the development of e-nose sensor for the detection of HCC at early stage.

A Panel of Bile Volatile Organic Compounds Servers as a Potential Diagnostic Biomarker for Gallbladder Cancer

As no reliable diagnostic methods are available, gallbladder cancer (GBC) is often diagnosed until advanced stages, resulting in a poor prognosis. In the present study, we assessed whether volatile organic compounds (VOCs) could be used as a diagnostic tool for GBC. The VOCs in bile samples collected from 32 GBC patients were detected by gas chromatography-ion mobility spectrometry (GC-IMS), and 54 patients with benign gallbladder diseases (BGD) were used as controls. Both principal component analysis and unsupervised hierarchical clustering analysis gave a clear separation of GBC and BGD based on the bile VOC data collected from GC-IMS. A total of 12 differentially expressed VOCs were identified, including four upregulated (cyclohexanone, 2-ethyl-1-hexanol, acetophenone, and methyl benzoate) and eight downregulated [methyl acetate, (E)-hept-2-enal, hexanal, (E)-2-hexenal, (E)-2-pentenal, pentan-1-ol, 1-octen-3-one, and (E)-2-octenal] in GBC compared with BGD. ROC analysis demonstrated a 12-VOC panel con-structed by four machine learning algorithms, which was superior to the traditional tumor marker, CA19-9. Among them, support vector machines and linear discriminant analysis provided the highest AUCs of 0.972, with a sensitivity of 100% and a specificity of 94.4% in the diagnosis of GBC. Collectively, VOCs might be used as a potential tool for the diagnosis of GBC.

Volatile compounds in human breath: critical review and meta-analysis

Volatile compounds contained in human breath reflect the inner workings of the body. A large number of studies have been published that link individual components of breath to disease, but diagnostic applications remain limited, in part due to inconsistent and conflicting identification of breath biomarkers. New approaches are therefore required to identify effective biomarker targets. Here, volatile organic compounds have been identified in the literature from four metabolically and physiologically distinct diseases and grouped into chemical functional groups (e.g. - methylated hydrocarbons or aldehydes; based on known metabolic and enzymatic pathways) to support biomarker discovery and provide new insight on existing data. Using this functional grouping approach, principal component analysis doubled explanatory capacity from 19.1% to 38% relative to single individual compound approaches. Random forest and linear discriminant analysis reveal 93% classification accuracy for cancer. This review and meta-analysis provides insight for future research design by identifying volatile functional groups associated with disease. By incorporating our understanding of the complexities of the human body, along with accounting for variability in methodological and analytical approaches, this work demonstrates that a suite of targeted, functional volatile biomarkers, rather than individual biomarker compounds, will improve accuracy and success in diagnostic research and application.

Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer

The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups-including naphthalene derivatives, phenols, and organosulphurs-augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.

[Advances on Collection and Analysis of Volatile Organic Compounds in the Diagnosis of Lung Cancer]

Lung cancer is a leading cause of cancer-related morbidity and mortality globally, which is the biggest menace to the health and life of the population. Screening and early detection of lung cancer are effective in reducing its mortality, and the measurement of volatile organic compounds (VOCs) has become a promising clinical means for early detection, course detection and prognosis management of lung cancer, with advantages of rapid speed, non-invasiveness and convenience. Now, a variety of VOCs collection ways and analysis methods have emerged at home and abroad. This report summarized three aspects, including VOCs collection, multiple methods of analysis and progress in the diagnosis and treatment of lung cancer. At last, we discussed the limitations and prospects of VOCs analysis.
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Odors and cancer: Current status and future directions

Cancer is the second leading cause of death in the world. Because tumors detected at early stages are easier to treat, the search for biomarkers-especially non-invasive ones-that allow early detection of malignancies remains a central goal to reduce cancer mortality. Cancer, like other pathologies, often alters body odors, and much has been done by scientists over the last few decades to assess the value of volatile organic compounds (VOCs) as signatures of cancers. We present here a quantitative review of 208 studies carried out between 1984 and 2020 that explore VOCs as potential biomarkers of cancers. We analyzed the main findings of these studies, listing and classifying VOCs related to different cancer types while considering both sampling methods and analysis techniques. Considering this synthesis, we discuss several of the challenges and the most promising prospects of this research direction in the war against cancer.

Profiles of Volatile Biomarkers Detect Tuberculosis from Skin

Tuberculosis (TB) is an infectious disease that threatens >10 million people annually. Despite advances in TB diagnostics, patients continue to receive an insufficient diagnosis as TB symptoms are not specific. Many existing biodiagnostic tests are slow, have low clinical performance, and can be unsuitable for resource-limited settings. According to the World Health Organization (WHO), a rapid, sputum-free, and cost-effective triage test for real-time detection of TB is urgently needed. This article reports on a new diagnostic pathway enabling a noninvasive, fast, and highly accurate way of detecting TB. The approach relies on TB-specific volatile organic compounds (VOCs) that are detected and quantified from the skin headspace. A specifically designed nanomaterial-based sensors array translates these findings into a point-of-care diagnosis by discriminating between active pulmonary TB patients and controls with sensitivity above 90%. This fulfills the WHO's triage test requirements and poses the potential to become a TB triage test.

The investigation of the volatile metabolites of lung cancer from the microenvironment of malignant pleural effusion

For malignant pleural effusions, pleural fluid cytology is a diagnostic method, but sensitivity is low. The pleural fluid contains metabolites directly released from cancer cells. The objective of this study was to diagnose lung cancer with malignant pleural effusion using the volatilomic profiling method. We recruited lung cancer patients with malignant pleural effusion and patients with nonmalignant diseases with pleural effusion as controls. We analyzed the headspace air of the pleural effusion by gas chromatography-mass spectrometry. We used partial least squares discriminant analysis (PLS-DA) to identify metabolites and the support vector machine (SVM) to establish the prediction model. We split data into a training set (80%) and a testing set (20%) to validate the accuracy. A total of 68 subjects were included in the final analysis. The PLS-DA showed high discrimination with an R² of 0.95 and Q² of 0.58. The accuracy of the SVM in the test set was 0.93 (95% CI 0.66, 0.998), the sensitivity was 83%, the specificity was 100%, and kappa was 0.85, and the area under the receiver operating characteristic curve was 0.96 (95% CI 0.86, 1.00). Volatile metabolites of pleural effusion might be used in patients with cytology-negative pleural effusion to rule out malignancy.

Headspace analysis of mesothelioma cell lines differentiates biphasic and epithelioid sub-types

Malignant mesothelioma (MM) is an incurable cancer. MM is often misdiagnosed, with a poor 5-year survival and limited treatment options. The discovery of endogenous volatile organic compounds (VOCs) is required in order to accelerate the development of a breath test as an alternative to conventional MM diagnosis. For the first time, this study used solid-phase microextraction and gas chromatography-mass spectrometry to identify VOCs released directly from the biphasic MM cell line MSTO-211H and the epithelioid MM cell line NCI-H28 as well as the non-malignant mesothelial cell line MET-5A. Multivariate statistical analysis showed separation between MSTO-211H, NCI-H28 and MET-5A results. 2-ethyl-1-hexanol was significantly increased in both MSTO-211H and NCI-H28 cells compared to MET-5A controls. In addition, ethyl propionate and cyclohexanol were significantly increased in MSTO-211H cells and dodecane was significantly increased in NCI-H28 cells. This is the first study reporting headspace analysis of these MM cell lines and the first to consider the effects of mesothelioma sub-type on VOC profile. Current results further highlight the potential for a diagnostic mesothelioma breath test as well as providing proof of concept for the differentiation between biphasic and epithelioid mesothelioma based on VOC profiles.

Volatile organic compounds in human matrices as lung cancer biomarkers: a systematic review

Volatile organic compounds (VOCs) have shown potential as non-invasive breath biomarkers for lung cancer, but their unclear biological origin currently limits clinical applications. This systematic review explores headspace analysis of VOCs in patient-derived body fluids and lung cancer cell lines to pinpoint lung cancer-specific VOCs and uncover their biological origin. A search was performed in the databases MEDLINE and Web of Science. Twenty-two articles were included in this systematic review. Since there is no standardised approach to analyse VOCs, a plethora of techniques and matrices/cell lines were explored, which is reflected in the various VOCs identified. However, comparing VOCs in the headspace of urine, blood and pleural effusions from patients and lung cancer cell lines showed some overlapping VOCs, indicating their potential use in lung cancer diagnosis. This review demonstrates that VOCs are promising biomarkers for lung cancer. However, due to lack of inter-matrix consensus, standardised prospective trials will have to be conducted to validate clinically relevant biomarkers.

Advances in lung cancer biomarkers: The role of (metal-) metabolites and selenoproteins

Lung cancer (LC) is the second most common cause of death in men after prostate cancer, and the third most recurrent type of tumor in women after breast and colon cancers. Unfortunately, when LC symptoms begin to appear, the disease is already in an advanced stage and the survival rate only reaches 2%. Thus, there is an urgent need for early diagnosis of LC using specific biomarkers, as well as effective therapies and strategies against LC. On the other hand, the influence of metals on more than 50% of proteins is responsible for their catalytic properties or structure, and their presence in molecules is determined in many cases by the genome. Research has shown that redox metal dysregulation could be the basis for the onset and progression of LC disease. Moreover, metals can interact between them through antagonistic, synergistic and competitive mechanisms, and for this reason metals ratios and correlations in LC should be explored. One of the most studied antagonists against the toxic action of metals is selenium, which plays key roles in medicine, especially related to selenoproteins. The study of potential biomarkers able to diagnose the disease in early stage is conditioned by the development of new analytical methodologies. In this sense, omic methodologies like metallomics, proteomics and metabolomics can greatly assist in the discovery of biomarkers for LC early diagnosis.

Metabolic and lipidomic characterization of malignant pleural effusion in human lung cancer

Malignant pleural effusion (MPE) is an important hallmark for late-stage lung cancer with metastasis. Current clinical diagnosis methods require tedious work to distinguish MPE from benign pleural effusion (BPE). The objective of this study was to characterize the metabolic signatures in MPE of lung cancer, and identify potential metabolite biomarkers for diagnosis of MPE. MPE from lung cancer (n = 46) and BPE from tuberculosis patients (n = 32) were investigated by liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based global metabolomic and lipidomic profiling. Multivariate partial least-square discriminative analysis models exhibited distinct metabolic profiles between MPE and BPE. A total of 25 ether lipids, including phosphatidylcholines (PC), lysophosphatidylcholines (LPC) and phosphatidylethanolamines (PE), were observed to be significantly downregulated in MPE with excellent diagnostic potential. Plasmalogen PC(40:3p) showed highest AUC value of 0.953 in receiver operating characteristic (ROC) model. Oxidized polyunsaturated fatty acids (PUFA) were upregulated in MPE. The obtained results implied a high oxidative stress and peroxisome disorder in lung cancer patients. Combined metabolomic and lipidomic profiling have discovered potential biomarkers in MPE with excellent clinical diagnostic capability. Dysregulated ether lipids and oxidized PUFAs have implied an aberrant redox metabolism, which provides novel insights into the pathology of MPE in lung cancer.

Solid-phase microextraction: An appealing alternative for the determination of endogenous substances - A review

The determination of endogenous substances is of great significance for obtaining important biotic information such as biological components, metabolic pathways and disease biomarkers in different living organisms (e.g. plants, insects, animals and humans). However, due to the complex matrix and the trace concentrations of target analytes, the sample preparation procedure is an essential step before the analytes of interest are introduced into a detection instrument. Solid-phase microextraction (SPME), an emerging sample preparation technique that integrates sampling, extraction, concentration, and sample introduction into one step, has gained wide acceptance in various research fields, including in the determination of endogenous compounds. In this review, recent developments and applications of SPME for the determination of endogenous substances over the past five years are summarized. Several aspects, including the design of SPME devices (sampling configuration and coating), applications (in vitro and in vivo sampling), and coupling with emerging instruments (comprehensive two-dimensional gas chromatography (GC × GC), ambient mass spectrometry (AMS) and surface enhanced Raman scattering (SERS)) are involved. Finally, the challenges and opportunities of SPME methods in endogenous substances analysis are also discussed.

Real-time monitoring of skin ethanol gas by a high-sensitivity gas phase biosensor (bio-sniffer) for the non-invasive evaluation of volatile blood compounds

In this study, a highly sensitive and selective biochemical gas sensor (bio-sniffer) and real-time monitoring system with skin gas cell was constructed for the determination of ethanol gas concentration on human skin. This bio-sniffer measured the concentration of ethanol according to the change in fluorescence intensity of nicotinamide adenine dinucleotide (NADH), which is produced in an enzymatic reaction by alcohol dehydrogenase (ADH). The NADH detection system used an ultraviolet light emitting diode (UV-LED) as the excitation light, and a highly sensitive photomultiplier tube as a fluorescence intensity detector. The calibration range of the ethanol bio-sniffer was validated from 25 ppb to 128 ppm. To measure the concentration of ethanol within skin gas, subjects ingested an alcohol beverage, and the sensor output was monitored. We chose the central part of the palm, a back of the hand, and a wrist as targets. The real-time concentration of skin ethanol gas at each target was measured after drinking. The maximum output values were reached at approximately 70 min after drinking and then gradually decreased. We showed that ethanol release kinetics were different depending on the part of the hand measured with the developed monitoring system. Accordingly, this highly sensitive and selective bio-sniffer with a skin gas cell could be used to measure ethanol on the skin surface and could be applied for breath and skin gas research, as well as investigation of volatile blood compounds used as biomarkers for clinical diagnosis.

Prospects and Challenges of Volatile Organic Compound Sensors in Human Healthcare

The chemical signatures of volatile organic compounds (VOCs) in humans can be utilized for point-of-care (POC) diagnosis. Apart from toxic exposure studies, VOCs generated in humans can provide insights into one's healthy and diseased metabolic states, acting as a biomarker for identifying numerous diseases noninvasively. VOC sensors and the technology of e-nose have received significant attention for continuous and selective monitoring of various physiological and pathophysiological conditions of an individual. Noninvasive detection of VOCs is achieved from biomatrices of breath, sweat and saliva. Among these, detection from sweat and saliva can be continuous in real-time. The sensing approaches include optical, chemiresistive and electrochemical techniques. This article provides an overview of such techniques. These, however, have limitations of reliability, precision, selectivity, and stability in continuous monitoring. Such limitations are due to lack of sensor stability and complexity of samples in a multivariate environment, which can lead to false readings. To overcome selectivity barriers, sensor arrays enabling multimodal sensing, have been used with pattern recognition techniques. Stability and precision issues have been addressed through advancements in nanotechnology. The use of various forms of nanomaterial not only enhance sensing performance, but also plays a major role in detection on a miniaturized scale. The rapid growth in medical Internet of Things (IoT) and artificial intelligence paves a pathway for improvements in human theranostics.

Robust volcano plot: identification of differential metabolites in the presence of outliers

BACKGROUND

The identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers.

RESULTS

We propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites.

CONCLUSION

Our data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .

Solid-phase microextraction set-up for the analysis of liver volatolome to detect livestock exposure to micropollutants

Starting from a critical analysis of a first "proof of concept" study on the utility of the liver volatolome for detecting livestock exposure to environmental micropollutants (Berge et al., 2011), the primary aim of this paper is to improve extraction conditions so as to obtain more representative extracts by using an extraction temperature closer to livestock physiological conditions while minimizing analytical variability and maximizing Volatile Organic Compound (VOC) abundancies. Levers related to extraction conditions and sample preparation were assessed in the light of both abundance and coefficient of variation of 22 candidate VOC markers identified in earlier volatolomic studies. Starting with a CAR/PDMS fiber and a 30min extraction, the reduction of SPME temperature to 40°C resulted in a significant decrease in the area of 14 candidate VOC markers (p<0.05), mainly carbonyls and alcohols but also a reduction in the coefficient of variation for 17 of them. In order to restore VOC abundances and to minimize variability, two approaches dealing with sample preparation were investigated. By increasing sample defrosting time at 4°C from 0 to 24h yielded higher abundances and lower variabilities for 15 and 13 compounds, respectively. Lastly, by using additives favouring the release of VOCs (1.2g of NaCl) the sensitivity of the analysis was improved with a significant increase in VOC abundances of more than 50% for 13 out of the 22 candidate markers. The modified SPME parameters significantly enhanced the abundances while decreasing the analytical variability for most candidate VOC markers. The second step was to validate the ability of the revised SPME protocol to discriminate intentionally contaminated broiler chickens from controls, under case/control animal testing conditions. After verification of the contamination levels of the animals by national reference laboratories, data analysis by a multivariate chemometric method (Common Components and Specific Weights Analysis - ComDim) showed that the liver volatolome could reveal dietary exposure of broilers to a group of environmental pollutants (PCBs), a veterinary treatment (monensin), and a pesticide (deltamethrin), thus confirming the usefulness of this analytical set-up.

The lung cancer breath signature: a comparative analysis of exhaled breath and air sampled from inside the lungs

Results collected in more than 20 years of studies suggest a relationship between the volatile organic compounds exhaled in breath and lung cancer. However, the origin of these compounds is still not completely elucidated. In spite of the simplistic vision that cancerous tissues in lungs directly emit the volatile metabolites into the airways, some papers point out that metabolites are collected by the blood and then exchanged at the air-blood interface in the lung. To shed light on this subject we performed an experiment collecting both the breath and the air inside both the lungs with a modified bronchoscopic probe. The samples were measured with a gas chromatography-mass spectrometer (GC-MS) and an electronic nose. We found that the diagnostic capability of the electronic nose does not depend on the presence of cancer in the sampled lung, reaching in both cases an above 90% correct classification rate between cancer and non-cancer samples. On the other hand, multivariate analysis of GC-MS achieved a correct classification rate between the two lungs of only 76%. GC-MS analysis of breath and air sampled from the lungs demonstrates a substantial preservation of the VOCs pattern from inside the lung to the exhaled breath.

In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology

Background

Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.

Results

This study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy.

Conclusions

The results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.

Sensitive determination of atorvastatin in human plasma by dispersive liquid-liquid microextraction and solidification of floating organic drop followed by high-performance liquid chromatography

A novel and sensitive dispersive liquid-liquid microextraction method based on the solidification of the floating organic drop combined with high-performance liquid chromatography and ultraviolet detection was used for the determination of atorvastatine in blood serum samples. The chromatographic separation of atorvastatin was carried out using methanol as the mobile phase organic modifier. Various parameters affecting the extraction efficiency were optimized, such as the kind and volume of extraction solvent (1-undecanol) and disperser solvent (acetonitrile), pH, and the extraction time. The calibration curve was linear in the range of 0.2-6000 μg/L of atorvastatin (r(2) = 0.995) with a limit of detection of 0.07 μg/L. The relative standard deviation for 100 μg/L of atorvastatin in human plasma was 8.4% (n = 4). The recoveries of plasma samples spiked with atorvastatin were in the range of 98.8-113.8%. The obtained results showed that the proposed method is fast, simple, and reliable for the determination of very low concentrations of atorvastatin in human plasma samples.

Reactions of the selected ion flow tube mass spectrometry reagent ions H3O(+) and NO(+) with a series of volatile aldehydes of biogenic significance

RATIONALE

It has been shown that aldehydes are often present in biogenic media. For their analysis by selected ion flow tube mass spectrometry (SIFT-MS), the rate coefficients and the product ion distributions for the reactions of the analyte ions H3O(+) and NO(+) with volatile aldehydes in the presence of water vapour are required.

METHODS

The reactions of H3O(+) and NO(+) ions have been studied with a series of n-aldehydes ranging from acetaldehyde (designated as C2), through undecanal (C11) under the conditions used for SIFT-MS analyses (1 Torr He, 0.1 Torr air sample, 300 K) and over a range of sample gas absolute humidity from 1% to 7%. For comparison, the C5 pentanal isomer 3-methyl butanal, the unsaturated trans-2-pentenal and trans-2-octenal and the aromatic benzaldehyde were also included in the study.

RESULTS

The H3O(+) reactions led to the formation of protonated molecules MH(+) and their hydrates MH(+)(H2O)0,1,2,3 , and (MH(+)-H2O). The NO(+) reactions resulted in the production of NO(+)M adduct ions and of [M-H](+) fragment ions. The percentages of the different product ions for each aldehyde are seen to be dependent on the air sample humidity. Kinetic modelling was used to quantitatively explain these observations and to obtain rate coefficients for the association reactions producing NO(+) M adduct ions.

CONCLUSIONS

This detailed study has provided the kinetics data, in particular the product ion distributions, for the reactions of a number of volatile aldehydes, which allows their analyses by SIFT-MS in humid air, including exhaled breath, food emanations and other biogenic media.

The volatome of Aspergillus fumigatus

Early detection of invasive aspergillosis is absolutely required for efficient therapy of this fungal infection. The identification of fungal volatiles in patient breath can be an alternative for the detection of Aspergillus fumigatus that still remains problematic. In this work, we investigated the production of volatile organic compounds (VOCs) by A. fumigatus in vitro, and we show that volatile production depends on the nutritional environment. A. fumigatus produces a multiplicity of VOCs, predominantly terpenes and related compounds. The production of sesquiterpenoid compounds was found to be strongly induced by increased iron concentrations and certain drugs, i.e., pravastatin. Terpenes that were always detectable in large amounts were α-pinene, camphene, and limonene, as well as sesquiterpenes, identified as α-bergamotene and β-trans-bergamotene. Other substance classes that were found to be present in the volatome, such as 1-octen-3-ol, 3-octanone, and pyrazines, were found only under specific growth conditions. Drugs that interfere with the terpene biosynthesis pathway influenced the composition of the fungal volatome, and most notably, a block of sesquiterpene biosynthesis by the bisphosphonate alendronate fundamentally changed the VOC composition. Using deletion mutants, we also show that a terpene cyclase and a putative kaurene synthase are essential for the synthesis of volatile terpenes by A. fumigatus. The present analysis of in vitro volatile production by A. fumigatus suggests that VOCs may be used in the diagnosis of infections caused by this fungus.