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The colorimetric sensor array used in this study consists of 36 chemically sensitive dots impregnated on a disposable cartridge.
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The pattern of volatile organic compounds (VOCs) in the exhaled breath of patients with lung cancer may be unique. New sensor systems that detect patterns of VOCs have been developed. One of these sensor systems, a colorimetric sensor array, has 36 spots composed of different chemically sensitive compounds impregnated on a disposable cartridge. The...
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... are many challenges in the diagnosis of lung cancer. Lung cancer is often silent early in its course. When symptoms occur they are usually non-specific. 1 Thus, most lung cancer is diagnosed at an advanced stage when treatment is less success- ful. 2 Diagnosis relies on expensive, non-invasive and invasive testing with the potential for complications. Screening pro- grammes are yet to lead to a reduction in lung cancer-specific mortality or overall mortality. 3 4 Advances in imaging are uncovering many small lung nodules requiring serial testing. 5 For all these reasons, an accurate, inexpensive, non-invasive test would be a welcome addition to our current diagnostic tools. Metabolic changes within cancer cells can lead to changes in the production and processing of volatile organic compounds (VOCs). 6 7 A pattern of VOCs unique to lung cancer may be detected in samples of exhaled breath. Studies have evaluated the ability of gas chromatography and mass spectroscopy (GC– MS) to identify unique patterns of VOCs in the breath of individuals with lung cancer. 8–14 The results have supported the promise of this line of investigation. GC–MS systems are expensive and require expert interpretation. They are difficult to use as a point-of-care test. An easier to use method of detecting unique patterns of VOCs would permit the broader application of breath testing for the diagnosis of lung cancer. Gaseous chemical sensing and identification devices have been developed that are able to detect a single (or patterns of) odorant molecule(s) such as VOCs. The premise with most of these devices is that absorption of gases onto the sensor system causes a change in the conductivity, mass, vibration or colour of the sensor, thus altering its output. A colorimetric sensor array is one type of chemical sensor. This sensor has a group of spots composed of different chemically sensitive compounds (eg, metalloporphyrins) impregnated on a disposable cartridge ( fig 1). The colours of these spots change based on the chemicals with which they come into contact (fig 2). We hypothesised that a colorimetric sensor array would be able to detect the unique pattern of VOCs from the breath of patients with lung cancer. We enrolled subjects with non-small cell lung cancer regardless of stage of the disease. In addition, controls with disease and healthy control subjects were included in the study. The controls with disease included subjects with chronic obstructive pulmonary disease (COPD), sarcoidosis, pulmonary arterial hypertension (PAH) and idiopathic pulmonary fibrosis (IPF). The patients with COPD were required to meet the Global Initiative for Chronic Obstructive Lung Disease criteria for at least mild COPD. 15 The patients with sarcoidosis were either clinically or pathologically diagnosed. The patients with PAH were diagnosed by right-heart catheterisation. Primary and secondary aetiologies were included. The patients with IPF had disease proven clinically or by biopsy. There were no restrictions for participation based on treatment of any of the controls with disease. The healthy controls were > 18 years of age, without known lung conditions and free of active cardiopulmonary symptoms. They included smokers and non-smokers. Finally, individuals with indeterminate lung nodules, ( 30 mm in maximal diameter, were asked to participate. The nodules were determined to be cancer when proven by biopsy. They were classified as benign if a specific benign diagnosis was found on biopsy or they were followed-up for 2 years without evidence of growth on imaging. Each study subject performed tidal breathing of unfiltered room air for a total of 12 min. During this time, they inhaled through their nose and exhaled through their mouth into the breath collection device. The exhaled breath was drawn over the sensor array by a pump at approximately 250 ml/min. The sensor array was held in place on a flat-bed scanner. The system was contained in an incubator set to keep the exhaled breath at body temperature. At the end of the 12 min breath collection, all tubing and the sensor array were changed and a sample of room air was drawn across the system for another 12 min. Each colorimetric sensor array contained 36 chemically sensitive spots. Each spot had different sensitivities to VOCs. The chemicals that made up the spots on the sensor array used in this study were selected to be generally responsive (ie, not sensitive to one or two specific groups of volatiles). We chose a broadly sensitive system, as the identity of the key volatiles that make the breath of patients with lung cancer unique has not been clearly established. This type of system has been shown to be sensitive at the lower parts per million to upper parts per billion range for specific volatiles. 16 Colour changes on the array were imaged with the scanner at 2 min intervals during the breath collection (ie, 6 images per study subject). Colour changes were converted into numerical values for the change in the red, green and blue component of each spot, for each scan taken. This resulted in a 108-dimensional vector (36 spots, 3 values per spot). The difference between exhaled breath and room air results was used in the analysis. Data collected on the study subjects with lung cancer included gender, age, smoking status, size of primary tumour, location of primary tumour, histology of cancer, stage of cancer, comorbidities and medications. Data collected on the study subjects without lung cancer included gender, age, smoking status, pulmonary diagnosis (if any), comorbidities and medications. The random forest method was used to develop a model for discriminating patients with lung cancer from those without. 17 The random forest is a model for the classification of observations that is an alternative to logistic regression models, single classification trees and other classification models. Forests or trees are more flexible than logistic regression models since they allow for a broader scope of possible relationships between the VOC model predictors and lung cancer, and they do not require any additional variable selection or data reduction techniques. Random forests provide an additional benefit over a single tree since there is a reduced risk of bias in estimating prediction performance. A random forest is a collection of classification trees derived from bootstrap samples of the data. The drawback of the random forest is that it is a complex structure that cannot be succinctly summarised. Despite the complexity of the forest, its construc- tion and the classification of future observations is a simple process, making the validation and initial assessments of the classification strength of a set of predictors easy. We used R V.1.9.0, including the ‘‘randomForest’’ package, to produce and evaluate the random forest. Evaluation of the random forest was carried out using percentage estimates of error rates. In all, ...
Citations
... For example, Mazzone et al. developed a colorimetric sensor-array for the identification of the metabolic biosignature of lung cancers. 222,223 Lung cancer is often asymptomatic and nonspecific symptoms delay diagnosis until a late, less treatable stage, highlighting the need for non-invasive and cost-effective testing methods. Thirty-six chemically sensitive dots were utilized and impregnated on a disposable cartridge (Fig. 23a). ...
... In this section, we The scanner images the sensor array at baseline (b) and after being exposed to a chemical sample such as exhaled breath (c). 222,223 The difference in the colors from time A to B is pictured in panel (d). 222 organize the presentation flow and chemistry insights based on various analytes, and describe the use of inorganic nanoparticles and organic chromophores as color agents. ...
... 222,223 The difference in the colors from time A to B is pictured in panel (d). 222 organize the presentation flow and chemistry insights based on various analytes, and describe the use of inorganic nanoparticles and organic chromophores as color agents. ...
This review summarizes insights into colorant selection and signal mechanisms for the development of colorimetric sensing and POC sensors.
... However, the complexity of the human DNA and the need to fractionate blood to enrich for RNAs hinder the applicability of this approach [3]. Another non-invasive and safe technique of detecting LC biomarkers is through indicative volatile organic compounds (VOCs) in exhaled breath such as isoprene "C 5 H 8 ", toluene "C 7 H 8 ", 1-propanol "C 3 H 8 O", and 2-propenal "C 3 H 4 O" [4][5][6][7][8][9][10][11][12][13][14][15]. ...
... In the present study, the IV characteristics are calculated using the NEGF method [65]. We selected MoS 2 for the study of transport properties as it shows superiority than many others TMD MLs from the perspective of thermodynamic stability [11]. Furthermore, in the sensing device, the left and right electrodes were simulated by converting the structure of MoS 2 (shown in Fig. 1) into a device system as in reference [66]. ...
... In another article, the authors dealt with the detection of lung cancer. They found that the colorimetric sensor can identify biosignals in the breath of lung cancer patients with moderate accuracy but can be further optimized by evaluating specific histology and incorporating clinical risk factors [241,242]. Colorimetric monitoring of exhaled breath is also able to record the balance of the human organism by indicating the biomarker Nitric Oxide (NO) and thus indicate oxidative stress. NO is detected using m-cresol purple, bromophenol blue, and Alizaringelb dye and analyzed by ultraviolet-visible (UV-Vis) spectroscopy. ...
This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.
... The volatile organic compounds (VOCs) contained in human exhaled breath can be derived from a variety of metabolic processes in the body. Given the proximity of the breath to the respiratory tract, VOCs in exhaled breath have been studied for their diagnostic potential in lung diseases such as lung cancer, COPD, pneumonia, and asthma [6][7][8][9][10]. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t Similarly, VOCs in exhaled breath could be analyzed in response to exhaustive exercise. As most VOCs exchange between the systemic blood and the air in the lungs at the alveolar membrane, exhaled breath is enriched for VOCs generated from physiological processes throughout the body, such as from the liver and the intestines [11][12][13]. ...
Exhaustive exercise can induce unique physiological responses in the lungs and other parts of the human body. The volatile organic compounds (VOCs) in exhaled breath are ideal for studying the effects of exhaustive exercise on the lungs due to the proximity of the breath matrix to the respiratory tract. As breath VOCs can originate from the bloodstream, changes in abundance should also indicate broader physiological effects of exhaustive exercise on the body. Currently, there is limited published data on the effects of exhaustive exercise on breath VOCs. Breath has great potential for biomarker analysis as it can be collected non-invasively, and capture real-time metabolic changes to better understand the effects of exhaustive exercise. In this study, we collected breath samples from a small group of elite runners participating in the 2019 Ultra-Trail du Mont Blanc (UTMB) ultra-marathon. The final analysis included matched paired samples collected before and after the race from 24 subjects. All 48 samples were analyzed using the Breath Biopsy Platform with GC-Orbitrap™ via thermal desorption gas chromatography-mass spectrometry (TD-GC-MS). The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjusted p < 0.05). We identified a total of 793 VOCs in the breath samples of elite runners. Of these, 63 showed significant differences between pre- and post-race samples after correction for multiple testing (12 decreased, 51 increased). The specific VOCs identified suggest the involvement of fatty acid oxidation, inflammation, and possible altered gut microbiome activity in response to exhaustive exercise. This study demonstrates significant changes in VOC abundance resulting from exhaustive exercise. Further investigation of VOC changes along with other physiological measurements can help improve our understanding of the effect of exhaustive exercise on the body and subsequent differences in VOCs in exhaled breath.
... discrimination [46], biological and environmental monitoring [47], fruit ripeness indicator [48], food freshness indicator [49], and detection of ascorbic acid in fruit juices and vitamin C tablets [50]. Many materials have already been used for these purposes, for example, metalloporphyrin [45], zwitterionic compounds [46], polymer-quantum dot integrated graphene oxide [47], chlorophenol red [48], mixed natural dyes [49], and poly(methacrylic acid)-stabilized silver nanoclusters [50]. Color-responsive polydiacetylenes (PDAs) are of interest due to their ability to detect various classes of stimuli [1-15, 26, 34]. ...
Color-responsive polydiacetylenes (PDAs) have been utilized for detecting different types of stimuli. However, the utilization of PDA-based materials for sensing polymers is rarely applied. This work introduces a facile approach to develop the PDA-based nanocomposites for colorimetric detection of acidic polymers in aqueous solutions. The sensors can be fabricated by a simple mixing of PDA/Zn²⁺/ZnO nanocomposites and cetyltrimethylammonium bromide (CTAB). The PDA/Zn²⁺/ZnO-CTAB sensors exhibit colorimetric responses to poly(acrylic acid) (PAA) and poly(4-styrenesulfonic acid). The sensors change color at different PAA concentrations, governed by the ratios of CTAB within the nanocomposites. The ability to control the sensitivity allows applications of this system for semi-quantitative analysis. Furthermore, the sensors can differentiate the molecular weight and structure of the acidic polymers. Our approach is simple and low-cost, desirable for large-scale production. This study offers a new development path of the PDA-based materials, expanding their utilization in polymer sensing technologies.
Graphical abstract
... Because it can link specific breath volatile organic compounds (VOCs) or breath-prints (i.e., patterns of VOCs) to the health status [9]. De Vries et al. [10] in 2019 used an eNose to analyze VOCs from lung cancer patients to predict whether patients on immunotherapy will achieve objective remission. The accuracy of its prediction can be as high as 85%. ...
The research developed an improved intelligent enhancement learning algorithm based on AdaBoost, that can be applied for lung cancer breath detection by the electronic nose (eNose). First, collected the breath signals from volunteers by eNose, including healthy individuals and people who had lung cancer. Additionally, the signals' features were extracted and optimized. Then, multi sub-classifiers were obtained, and their coefficients were derived from the training error. To improve generalization performance, K-fold cross-validation was used when constructing each sub-classifier. The prediction results of a sub-classifier on the test set were then achieved by the voting method. Thus, an improved AdaBoost classifier would be built through heterogeneous integration. The results shows that the average precision of the improved algorithm classifier for distinguishing between people with lung cancer and healthy individuals could reach 98.47%, with 98.33% sensitivity and 97% specificity. And in 100 independent and randomized tests, the coefficient of variation of the classifier's performance hardly exceeded 4%. Compared with other integrated algorithms, the generalization and stability of the improved algorithm classifier are more superior. It is clear that the improved AdaBoost algorithm may help screen out lung cancer more comprehensively. Additionally, it will significantly advance the use of eNose in the early identification of lung cancer.
... The colorimetric sensor array (CSA) has attracted increasing attention as a method for the classification and adulteration detection in chemically diverse analytes [12][13][14][15][16][17]. The CSA simulates the human olfactory system, presenting different combined responses to different compounds based on cross-reactive sensing receptors [18,19]. ...
Apo pickle is a traditional Chinese fermented vegetable. However, the traditional fermentation process of Apo pickle is slow, easy to ruin, and cannot be judged with regard to time. To improve fermentation, LP-165 (L. Plantarum), which has a high salt tolerance, acidification, and growth capacity, was chosen as the starter culture. Meanwhile, a colorimetric sensor array (CSA) sensitive to pickle volatile compounds was developed to differentiate Apo pickles at varying degrees of fermentation. The color components were extracted from each dye in the color change profiles and were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA). The fermentation process of the Apo pickle was classified into four phases by LDA. The accuracy of backward substitution verification was 99% and the accuracy of cross validation was 92.7%. Furthermore, the partial least squares regression (PLSR) showed that data from the CSA were correlated with pH total acid, lactic acid, and volatile acids of the Apo pickle. These results illustrate that the CSA reacts quickly to inoculated Apo pickle and could be used to detect fermentation.
... Phillips et al. demonstrated a statistically significant difference in alkane contours between subjects aged 9 to 31 and 46 to 89 using GC-MS [95]. On the other hand, other groups have found no difference in breath analysis between age groups [68,69,96]. Dragonieri et al. found no significant difference in smell-prints using the Cyranose 320 in 20 subjects aged between < 45 and > 45 years old [69]. ...
... Wehinger et al. demonstrated no difference in VOC pattern across ages using PTR-MS [96]. Studies have also demonstrated that sex has no impact on the breath analysis [68,70,96]. ...
Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.
... The opto-E-noses have been utilized in the diagnosis of diseases such as lung cancer [28] and sinusitis (via exhaled breath metabolites) [29], leukemia (via whole blood metabolites) [24], and urinary tract infection (via urinary metabolites) [22]. These arrays are made of porphyrins, organic dyes, and salts or functionalized gold and silver NPs immobilized on a polymer substrate such as polyvinylidene difluoride (PVDF) [20]. ...
... These arrays are made of porphyrins, organic dyes, and salts or functionalized gold and silver NPs immobilized on a polymer substrate such as polyvinylidene difluoride (PVDF) [20]. The sampling for colorimetric breath analysis can be performed by collecting the volatile metabolites in a bag, followed by injecting them into the sensor array chamber using an external pump or an inert gas stream [28]. Therefore, it is possible that the device is not usable for on-site analysis, and that enough metabolites do not exist in the collected sample or the sample is not correctly passed on the sensor surface. ...
According to World Health Organization reports, large numbers of people around the globe have been infected or died for COVID-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with COVID-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 78%84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker can be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.
... Recently, E-nose consists of different types of sensors that have been developed (hybrid gas sensor arrays or multi-transducer arrays) [195,196], which can reduce correlations between the responses of different sensor types, and thus improve the power of data analysis [197][198][199]. The quartz crystal microbalance (QCM) [200,201] and surface acoustic wave (SAW) [202,203] sensors are two common types of mass transduced gas sensor arrays applied in breath analysis. As for optical transduced gas sensors, colorimeter [204,205] and nondispersive infrared (NDIR, for CO 2 monitoring) [206] types are used. ...
Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.
