Use of an electronic nose system for diagnoses of urinary tract infections

The use of volatile production patterns produced by bacterial contaminants in urine samples were examined using electronic nose technology. In two experiments 25 and 45 samples from patients were analysed for specific bacterial contaminants using agar culture techniques and the major UTI bacterial species identified. These samples were also analysed by incubation in a volatile generation test tube system for 4-5 h. The volatile production patterns were then analysed using an electronic nose system with 14 conducting polymer sensors. In the first experiment analysis of the data using a neural network (NN) enabled identification of all but one of the samples correctly when compared to the culture information. Four groups could be distinguished, i.e. normal urine, Escherichia coli infected, Proteus spp. and Staphylococcus spp. In the second experiment it was again possible to use NN systems to examine the volatile production patterns and identify 18 of 19 unknown UTI cases. Only one normal patient sample was mis-identified as an E. coli infected sample. Discriminant function analysis also differentiated between normal urine samples, that infected with E. coli and with Staphylococcus spp. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology for the first time. These findings will have implications for the development of rapid systems for use in clinical practice.     

Rapid Identification of Rice Samples Using an Electronic Nose

Four rice samples of long grain type were tested using an electronic nose (Cyranose-320).Samples of 5 g of each variety ofrice were placed individually in vials and were analyzed with the electronic nose unit consisting of 32 polymer sensors.TheCyranose-320 was able to differentiate between varieties of rice.The chemical composition of the rice odors for differentiatingrice samples needs to be investigated.The optimum parameter settings should be considered during the Cyranose-320 trainingprocess especially for multiple samples,which are helpful for obtaining an accurate training model to improve identificationcapability.Further,it is necessary to investigate the E-nose sensor selection for obtaining better classification accuracy.A re-duced number of sensors could potentially shorten the data processing time,and could be used to establish an application pro-cedure and reduce the cost for a specific electronic nose.Further research is needed for developing analytical procedures thatadapt the Cyranose-320 as a tool for testing rice quality.     

Identification of gastric cancer patients by serum protein profiling

Using surface-enhanced laser desorption ionization mass spectrometry (SELDI/TOF-MS) and ProteinChip technology, coupled with a pattern-matching algorithm and serum samples, we screened for protein patterns to differentiate gastric cancer patients from noncancer patients. A classifier ensemble, consisting of 50 decision trees, correctly classified all gastric cancers and all controls of a training set (100% sensitivity and 100% specificity). Eight of 9 stage I gastric cancers (88.9% sensitivity for stage I) were correctly classified. In addition, 28 sera from gastric cancer patients taken in different hospitals were correctly classified (100% sensitivity). Furthermore, all 11 control sera obtained from patients without gastric cancer (100% specificity) were classified correctly and 29 of 30 healthy blood-donors were classified as noncancerous. ProteinChip technology in conjunction with bioinformatics allows the highly sensitive and specific recognition of gastric cancer patients.     

Determination of volatile organic compounds as biomarkers of lung cancer by SPME-GC-TOF/MS and chemometrics

A method for qualitative and quantitative the determination of concentrations volatile organic compounds (VOCs) in human breath samples using solid phase microextraction (SPME) and gas chromatography-time of flight-mass spectrometry (GC-TOF/MS) has been carried out. They are employed for the preconcentration, separation and analysis of biological samples. The technique to rapid determination compounds present in human air, at the level of parts per billion (ppb) is applied. This method was optimized and evaluated. It showed linear correlations ranging from 0.83 to 234.05 ppb, limit of detection in the range of 0.31 to 0.75 ppb and precision, expressed as the RSD, was less then 10.00%. The unique combination of statistical methods allowed reduce the number of compounds to significant ones only and indicate the potential way to find the biomarkers of the lung cancer. Presented an analytical and statistical methods for detection composition of exhaled air could be applied as a potential non-intrusive tool for screening of lung cancer.     

Monitoring growth of the methanogenic archaea Methanobacterium formicicum using an electronic nose

Growth of the methanogenic archaea, Methanobacterium formicicum, in pure culture was monitored by analysing samples from the gas phase with an array of chemical gas sensors (an `electronic nose'). Analyses of the methane and protein formation rates were used as independent parameters of growth, and the data obtained from the electronic nose were evaluated using principal component analysis (PCA). We found that different growth phases can be distinguished with the electronic nose followed by PCA evaluation. The fast response of the sensors in combination with the high correlations with other parameters measuring growth show that the electronic nose can be a useful tool to rapidly determine methanogenic growth.     

Smelling the Diagnosis: The Electronic Nose as Diagnostic Tool in Inflammatory Arthritis. A Case-Reference Study

Objective

To investigate whether exhaled breath analysis using an electronic nose can identify differences between inflammatory joint diseases and healthy controls.

Methods

In a cross-sectional study, the exhaled breath of 21 rheumatoid arthritis (RA) and 18 psoriatic arthritis (PsA) patients with active disease was compared to 21 healthy controls using an electronic nose (Cyranose 320; Smiths Detection, Pasadena, CA, USA). Breathprints were analyzed with principal component analysis, discriminant analysis, and area under curve (AUC) of receiver operating characteristics (ROC) curves. Volatile organic compounds (VOCs) were identified by gas chromatography and mass spectrometry (GC-MS), and relationships between breathprints and markers of disease activity were explored.

Results

Breathprints of RA patients could be distinguished from controls with an accuracy of 71% (AUC 0.75, 95% CI 0.60–0.90, sensitivity 76%, specificity 67%). Breathprints from PsA patients were separated from controls with 69% accuracy (AUC 0.77, 95% CI 0.61–0.92, sensitivity 72%, specificity 71%). Distinction between exhaled breath of RA and PsA patients exhibited an accuracy of 69% (AUC 0.72, 95% CI 0.55–0.89, sensitivity 71%, specificity 72%). There was a positive correlation in RA patients of exhaled breathprints with disease activity score (DAS28) and number of painful joints. GC-MS identified seven key VOCs that significantly differed between the groups.

Conclusions

Exhaled breath analysis by an electronic nose may play a role in differential diagnosis of inflammatory joint diseases. Data from this study warrant external validation.     

Solid phase microextraction for analysis of alkanes and aromatic hydrocarbons in human breath

In this work, solid phase microextraction-gas chromatograph (SPME-GC) was applied to analyze alkanes and aromatic hydrocarbons in human breath, providing a potential non-invasive method to screen lung cancer. This method has been optimized and evaluated. It provided quantification limits ranging from 0.04 to 4.2 ng/mL, linear correlations ranging from 0.9845 to 0.9966 and R.S.D. values less than 9.8%. Total 30 breath samples, from 15 lung cancer patients and 15 healthy persons, were analyzed, and the alkanes and aromatic hydrocarbons were detected in 73.3% lung cancer patients and in 13.3% healthy persons by this method. Above all, It was demonstrated that this SPME-GC method provided a sensitive and non-invasive measure means to analyze alkanes and aromatic hydrocarbons in human breath, and brought forward a potential application for screening lung cancer.     

Exhaled Breath Analysis Using Electronic Nose in Cystic Fibrosis and Primary Ciliary Dyskinesia Patients with Chronic Pulmonary Infections

The current diagnostic work-up and monitoring of pulmonary infections may be perceived as invasive, is time consuming and expensive. In this explorative study, we investigated whether or not a non-invasive exhaled breath analysis using an electronic nose would discriminate between cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) with or without various well characterized chronic pulmonary infections. We recruited 64 patients with CF and 21 with PCD based on known chronic infection status. 21 healthy volunteers served as controls. An electronic nose was employed to analyze exhaled breath samples. Principal component reduction and discriminant analysis were used to construct internally cross-validated receiver operator characteristic (ROC) curves. Breath profiles of CF and PCD patients differed significantly from healthy controls p = 0.001 and p = 0.005, respectively. Profiles of CF patients having a chronic P. aeruginosa infection differed significantly from to non-chronically infected CF patients p = 0.044. We confirmed the previously established discriminative power of exhaled breath analysis in separation between healthy subjects and patients with CF or PCD. Furthermore, this method significantly discriminates CF patients suffering from a chronic pulmonary P. aeruginosa (PA) infection from CF patients without a chronic pulmonary infection. Further studies are needed for verification and to investigate the role of electronic nose technology in the very early diagnostic workup of pulmonary infections before the establishment of a chronic infection.     

Urinary Volatile Compounds as Biomarkers for Lung Cancer: A Proof of Principle Study Using Odor Signatures in Mouse Models of Lung Cancer

A potential strategy for diagnosing lung cancer, the leading cause of cancer-related death, is to identify metabolic signatures (biomarkers) of the disease. Although data supports the hypothesis that volatile compounds can be detected in the breath of lung cancer patients by the sense of smell or through bioanalytical techniques, analysis of breath samples is cumbersome and technically challenging, thus limiting its applicability. The hypothesis explored here is that variations in small molecular weight volatile organic compounds (“odorants”) in urine could be used as biomarkers for lung cancer. To demonstrate the presence and chemical structures of volatile biomarkers, we studied mouse olfactory-guided behavior and metabolomics of volatile constituents of urine. Sensor mice could be trained to discriminate between odors of mice with and without experimental tumors demonstrating that volatile odorants are sufficient to identify tumor-bearing mice. Consistent with this result, chemical analyses of urinary volatiles demonstrated that the amounts of several compounds were dramatically different between tumor and control mice. Using principal component analysis and supervised machine-learning, we accurately discriminated between tumor and control groups, a result that was cross validated with novel test groups. Although there were shared differences between experimental and control animals in the two tumor models, we also found chemical differences between these models, demonstrating tumor-based specificity. The success of these studies provides a novel proof-of-principle demonstration of lung tumor diagnosis through urinary volatile odorants. This work should provide an impetus for similar searches for volatile diagnostic biomarkers in the urine of human lung cancer patients.     

Estimation of bacteriological spoilage of pork cutlets by electronic nose

The utility of chemosensor array (EN) signals of head-space volatiles of aerobically stored pork cutlets as a non-invasive technique for monitoring their microbiological load was studied during storage at 4, 8 and 12 degrees C, respectively. The bacteriological quality of the meat samples was determined by standard total aerobic plate counts (TAPC) and colony count of selectively estimated Pseudomonas (PS) spp., the predominant aerobic spoilage bacteria. Statistical analysis of the electronic nose measurements were principal component analysis (PCA), and canonical discriminant analysis (CDA). Partial least squares (PLS) regression was used to model correlation between microbial loads and EN signal responses, the degree of bacteriological spoilage, independently of the temperature of the refrigerated storage. Sensor selection techniques were applied to reduce the dimensionality and more robust calibration models were computed by determining few individual sensors having the smallest cross correlations and highest correlations with the reference data. Correlations between the predicted and "real" values were given on cross-validated data from both data reduced models and for full calibrations using the 23 sensor elements. At the same time, sensorial quality of the raw cutlets was noted subjectively on faultiness of the odour and colour, and drip formation of the samples. These preliminary studies indicated that the electronic nose technique has a potential to detect bacteriological spoilage earlier or at the same time as olfactory quality deterioration.     

Investigation of volatile biomarkers in lung cancer blood using solid-phase microextraction and capillary gas chromatography-mass spectrometry

In the present work, solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS) was developed for investigation of lung cancer volatile biomarkers. Headspace SPME conditions (fiber coating, extraction temperature and extraction time) and desorption conditions were optimized and applied to determination of volatiles in human blood. To find the biomarkers of lung cancer, investigation of volatile compounds in lung cancer blood and control was performed by using the present method. Concentrations of hexanal and heptanal in lung cancer blood were found to be much higher than those in control blood. The two molecules of hexanal and heptanal were regarded as biomarkers of lung cancer. By comparison of volatiles in breath and in blood, it is demonstrated that hexanal and heptanal in breath were originated from blood and screening of lung cancer by breath analysis be feasible. These results show that SPME/GC-MS is a simple, rapid and sensitive method very suitable for investigation of volatile disease markers in human blood.     

Bacteria classification using Cyranose 320 electronic nose

Background  

An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds.     

Physiologically Motivated Monitoring of Fermentation Processes by Means of an Electronic Nose

An on‐line approach of non‐invasive monitoring of the physiological changes in fermentation processes is presented. In yeast batch and bacterial fed‐batch fermentations it is shown that metabolic state changes can be revealed using an electronic nose. The transient responses of the gas sensors to the changes in the composition of the volatiles emitted from the cell cultures during fermentation are used to retrieve a semi‐quantitative representation of the physiological state of the cultures. With the sensor responses of the electronic nose it is shown that physiological variables such as rates of growth, substrate uptake and product formation can be depicted. The non‐invasive method thus seems as a pertinent alternative to conventional bioreactor monitoring methods.     

Monitoring the aroma production during wine-must fermentation with an electronic nose.

This work discusses the feasibility of using the electronic nose for the on-line and real-time monitoring of the production of a complex aroma profile during a bioconversion process. As a case study, the formation of the muscatel aroma during the wine-must fermentation was selected. During wine-must fermentation, aroma compounds responsible for the organoleptic character are produced in the ppm range, while simultaneously one of the main metabolic products, ethanol, is produced in much higher quantities (up to 10% wt). Because the sensors of the electronic nose array are cross-selective to different volatile compounds, it was investigated in detail how far the electronic nose was able to evaluate the aroma profile along the fermentation. This article discusses and evaluates subsequently the integration of a membrane separation process-organophilic pervaporation-for selectively enriching aroma compounds relative to ethanol, to improve sample discrimination.     

Rapid Diagnosis of Lung Tumors,a Feasability Study Using Maldi-Tof Mass Spectrometry

ObjectiveDespite recent advances in imaging and core or endoscopic biopsies, a percentage of patients have a major lung resection without diagnosis. We aimed to assess the feasibility of a rapid tissue preparation/analysis to discriminate cancerous from non-cancerous lung tissue.MethodsFresh sample preparations were analyzed with the Microflex LT^TM MALDI-TOF analyzer. Each main reference spectra (MSP) was consecutively included in a database. After definitive pathological diagnosis, each MSP was labeled as either cancerous or non-cancerous (normal, inflammatory, infectious nodules). A strategy was constructed based on the number of concordant responses of a mass spectrometry scoring algorithm. A 3-step evaluation included an internal and blind validation of a preliminary database (n = 182 reference spectra from the 100 first patients), followed by validation on a whole cohort database (n = 300 reference spectra from 159 patients). Diagnostic performance indicators were calculated.Results127 cancerous and 173 non-cancerous samples (144 peripheral biopsies and 29 inflammatory or infectious lesions) were processed within 30 minutes after biopsy sampling. At the most discriminatory level, the samples were correctly classified with a sensitivity, specificity and global accuracy of 92.1%, 97.1% and 95%, respectively.ConclusionsThe feasibility of rapid MALDI-TOF analysis, coupled with a very simple lung preparation procedure, appears promising and should be tested in several surgical settings where rapid on-site evaluation of abnormal tissue is required. In the operating room, it appears promising in case of tumors with an uncertain preoperative diagnosis and should be tested as a complementary approach to frozen-biopsy analysis.     

Reproducibility and Respiratory Function Correlates of Exhaled Breath Fingerprint in Chronic Obstructive Pulmonary Disease

Background

The electronic nose (e nose) provides distinctive breath fingerprints for selected respiratory diseases. Both reproducibility and respiratory function correlates of breath fingerprint are poorly known.

Objectives

To measure reproducibility of breath fingerprints and to assess their correlates among respiratory function indexes in elderly healthy and COPD subjects.

Method

25 subjects (5 COPD patients for each GOLD stage and 5 healthy controls) over 65 years underwent e-nose study through a seven sensor system and respiratory function tests at times 0, 7, and 15 days. Reproducibility of the e nose pattern was computed. The correlation between volatile organic compound (VOC) pattern and respiratory function/clinical parameters was assessed by the Spearman''s rho.

Measurements and Main Results

VOC patterns were highly reproducible within healthy and GOLD 4 COPD subjects, less among GOLD 1–3 patients.VOC patterns significantly correlated with expiratory flows (Spearman''s rho ranging from 0.36 for MEF25% and sensor Co-Buti-TPP, to 0.81 for FEV1% and sensor Cu-Buti-TPP p<0.001)), but not with residual volume and total lung capacity.

Conclusions

VOC patterns strictly correlated with expiratory flows. Thus, e nose might conveniently be used to assess COPD severity and, likely, to study phenotypic variability. However, the suboptimal reproducibility within GOLD 1–3 patients should stimulate further research to identify more reproducible breath print patterns.     

Discovery and identification of Serum Amyloid A protein elevated in lung cancer serum

Two hundred and eighteen serum samples from 175 lung cancer patients and 43 healthy individuals were analyzed by using Surface Enhaced Laser Desorption/Ionization Time of Flight Mass Spectrome-try (SELDI-TOF-MS). The data analyzed by both Biomarker Wizard™ and Biomarker Patterns™ software showed that a protein peak with the molecular weight of 11.6 kDa significantly increased in lung cancer. Meanwhile, the level of this biomarker was progressively increased with the clinical stages of lung cancer. The candidate biomarker was then obtained from tricine one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis by matching the molecular weight with peaks on WCX2 chips and was identified as Serum Amyloid A protein (SAA) by MALDI/MS-MS and database searching. It was further validated in the same serum samples by immunoprecipitation with commercial SAA antibody. To confirm the SAA differential expression in lung cancer patients, the same set of serum samples was measured by ELISA assay. The result showed that at the cutoff point 0.446 (OD value) on the Receiver Operating Characteristic (ROC) curve, SAA could better discriminate lung cancer from healthy individuals with sensitivity of 84.1% and specificity of 80%. These findings demonstrated that SAA could be characterized as a biomarker related to pathological stages of lung cancer.     

P53抗体联合CYFRA21-1和CEA在非小细胞肺癌中的应用

目的：研究血清P53抗体在非小细胞肺癌临床病理特征之间的关系,并结合血清中的癌胚抗原、角质蛋白21-1以指导对临床上肺癌复发和转移的分析,用来选择合理的治疗方案。方法：正常组30例,肺良性疾病组10例,肺癌组45例,肺癌全组分别于手术前1天、术后10、30、60和90天时抽取清晨空腹静脉血2ml,23例肺癌病例于手术后120天,15例病例于手术后180天抽取清晨空腹静脉血2ml,肺良性疾病组分别于手术前1天、抽取清晨空腹静脉血2ml。正常组清晨空腹采集静脉血2ml。采用酶联免疫吸附法（ELISA）检测血清P53抗体和角质蛋白21-1,采用荧光酶标免疫法检测血清癌胚抗原。结果：血清P53抗体、CYFRA21-1和CEA在正常人组、良性疾病组、肺癌组术前阳性率的比较三种肿瘤标志物阳性率经X2检验,在肺癌组分别与正常人组和良性病例组有显著性差异（P〈0.05）,良性病例组和正常人组之间无显著行差异（P〉0.05）。并与手术后复发与转移相关。结论：联合检测癌胚抗原、角质蛋白21-1及血清P53抗体水平有助于肺部良恶性疾病的诊断;手术前后动态测定肺癌患者血清P53抗体和角质蛋白21-1的变化规律,有助于判断疗效,监测预后和指导肺癌术后的综合治疗。     

Exercise changes volatiles in exhaled breath assessed by an electronic nose

Exercise-caused metabolic changes can be followed by monitoring exhaled volatiles; however it has not been previously reported if a spectrum of exhaled gases is modified after physical challenge. We have hypothesized that changes in volatile molecules assessed by an electronic nose may be the reason for the alkalization of the exhaled breath condensate (EBC) fluid following physical exercise.Ten healthy young subjects performed a 6-minute running test. Exhaled breath samples pre-exercise and post-exercise (0 min, 15 min, 30 min and 60 min) were collected for volatile pattern ("smellprint") determination and pH measurements (at 5.33 kPa CO2), respectively. Exhaled breath smellprints were analyzed using principal component analysis and were related to EBC pH.Smellprints (p=0.04) and EBC pH (p=0.01) were altered during exercise challenge. Compared to pre-exercise values, smellprints and pH differed at 15 min, 30 min and 60 min following exercise (p<0.05), while no difference was found at 0 min post-exercise. In addition, a significant correlation was found between volatile pattern of exhaled breath and EBC pH (p=0.01, r=-0.34).Physical exercise changes the pattern of exhaled volatiles together with an increase in pH of breath. Changes in volatiles may be responsible for increase in EBC pH.     

The use of gas-sensor arrays to diagnose urinary tract infections

Sensorial analysis based on the utilisation of human senses, is one of the most important and straightforward investigation methods in food and chemical analysis. An electronic nose has been used to detect in vivo Urinary Tract Infections from 45 suspected cases that were sent for analysis in a UK Health Laboratory environment. These samples were analysed by incubation in a volatile generation test tube system for 4-5 h. The volatile production patterns were then analysed using an electronic nose system with 14 conducting polymer sensors. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on learning techniques has been considered. The implementation of an Extended Normalised Radial Basis Function network with advanced features for determining its size and parameters and the concept of fusion of multiple classifiers dedicated to specific feature parameters has been also adopted in this study. The proposed scheme achieved a very high classification rate of the testing dataset, demonstrating in this way the efficiency of the proposed scheme compared with other approaches. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.