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BACKGROUND RESULTS CONCLUSION Spearmint and peppermint teas are widely consumed around the world for their flavor and therapeutic properties. Dynamic headspace sampling (HS) coupled to gas chromatography/mass spectrometry (GC–MS) with principal component analysis (PCA) of ‘fingerprint’ volatile profiles were used to investigate 27 spearmint and peppermint teas. Additionally, comparisons between mint teas were undertaken with an electronic nose (enose).Twenty compounds, all previously known in the literature, were identified using HS–GC–MS. PCA found distinct differences between the fingerprint volatile profiles of spearmint, peppermint and spearmint/peppermint combination teas. HS–GC–MS analysis performed with an achiral column allowed faster processing time and yielded tighter clustering of PCA tea groups than the analysis which used a chiral column. Two spearmint outliers were detected. One showed a high degree of variation in volatile composition and a second wholly overlapped with the peppermint PCA grouping. Enose analysis separated all treatments with no overlaps.Characterizing the volatile fingerprints of mint teas is critical to quality control for this valuable agricultural product. The results of this study show that fingerprint volatile profiles and enose analysis of mint teas are distinctive and could be used to rapidly identify unknown samples. With specific volatile profiles identified for each tea, samples could be tested in the laboratory, or potentially on a farm or along the supply chain, to confirm the provenance and authenticity of mint food or beverage commodities. © 2024 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

Abstract Context Prostate‐specific antigen is non‐specific for prostate cancer. This is improved by multiparametric MRI but a significant amount of indolent prostate cancer is detected by the current MRI pathway and data is emerging that clinically significant cancers maybe missed using a standard PSA threshold. Volatile organic compound (VOC) analysis may offer novel biomarkers for prostate cancer and clinically significant disease. Objective To perform a systematic review of the literature to evaluate the current evidence for the use of VOCs as novel biomarkers for prostate cancer and clinically significant prostate cancer. Evidence Acquisition A systematic search of MEDLINE, Scopus, Web of Science and the Cochrane Library was undertaken by two independent reviewers and papers were assessed for inclusion in the review. Study characteristics, sensitivity and specificity of GC–MS or eNose were extracted. Risk of bias and applicability issues were determined using QUADAS 2 and the quality of reporting using the STARD checklist. Evidence Synthesis Nineteen studies were included, of which 6 utilised eNose and 13 GC–MS. eNose sensitivity and specificity were 0.71–0.95 and 0.79–0.96, respectively, and GC–MS found a sensitivity and specificity of 0.66–1.00 and 0.53–0.97, respectively. There were concerns about bias in patient recruitment due to differences in the timing of the index test relative to the reference standard. Conclusion This review has found promising early results for urinary metabolomics in the detection of prostate cancer. However, there is a need for larger, high‐quality studies to validate this. Future work should focus on the detection of clinically significant prostate cancer.

Background: Disorders of mucociliary clearance, such as cystic fibrosis (CF), primary ciliary dyskinesia (PCD) and bronchiectasis of unknown origin, are characterised by periods with increased respiratory symptoms, referred to as pulmonary exacerbations. These exacerbations are hard to predict and associated with lung function decline and the loss of quality of life. To optimise treatment and preserve lung function, there is a need for non-invasive and reliable methods of detection. Breath analysis might be such a method. Methods: We systematically reviewed the existing literature on breath analysis to detect pulmonary exacerbations in mucociliary clearance disorders. Extracted data included the study design, technique of measurement, definition of an exacerbation, identified compounds and diagnostic accuracy. Results: Out of 244 identified articles, 18 were included in the review. All studies included patients with CF and two also with PCD. Age and the definition of exacerbation differed between the studies. There were five that measured volatile organic compounds (VOCs) in exhaled breath using gas chromatography with mass spectrometry, two using an electronic nose and eleven measured organic compounds in exhaled breath condensate. Most studies showed a significant correlation between pulmonary exacerbations and one or multiple compounds, mainly hydrocarbons and cytokines, but the validation of these results in other studies was lacking. Conclusions: The detection of pulmonary exacerbations by the analysis of compounds in exhaled breath seems possible but is not near clinical application due to major differences in results, study design and the definition of an exacerbation. There is a need for larger studies, with a longitudinal design, international accepted definition of an exacerbation and validation of the results in independent cohorts. [ABSTRACT FROM AUTHOR]

Introduction: Prostate cancer (PCa) is known for its highly diverse clinical behavior, ranging from low-risk, slow-growing tumors to aggressive and lifethreatening forms. To avoid over-treatment of low-risk PCa patients, it would be very important prior to any therapeutic intervention to appropriately classify subjects based on tumor aggressiveness. Unfortunately, there is currently no reliable test available for this purpose. The aim of the present study was to evaluate the ability of risk stratification of PCa subjects using an electronic nose (eNose) detecting PCa-specific volatile organic compounds (VOCs) in urine samples. Methods: The study involved 120 participants who underwent diagnostic prostate biopsy followed by robot assisted radical prostatectomy (RARP). PCa risk was categorized as low, intermediate, or high based on the D'Amico risk classification and the pathological grade (PG) assessed after RARP. The eNose's ability to categorize subjects for PCa risk stratification was evaluated based on accuracy and recall metrics. Results: The study population comprised 120 participants. When comparing eNose predictions with PG an accuracy of 79.2% (95%CI 70.8 - 86%) was found, while an accuracy of 74.2% (95%CI 65.4 - 81.7%) was found when compared to D'Amico risk classification system. Additionally, if compared low-versus -intermediate-/high-risk PCa, the eNose achieved an accuracy of 87.5% (95% CI 80.2-92.8%) based on PG or 90.8% (95%CI 84.2-95.3%) based on D'Amico risk classification. However, when using low-/-intermediate versus -high-risk PCa for PG, the accuracy was found to be 91.7% (95%CI 85.2-95.9%). Finally, an accuracy of 80.8% (95%CI72.6-87.4%) was found when compared with D'Amico risk classification. Discussion: The findings of this study indicate that eNose may represent a valid alternative not only for early and non-invasive diagnosis of PCa, but also to categorize patients based on tumor aggressiveness. Further studies including a wider sample population will be necessary to confirm the potential clinical impact of this new technology. [ABSTRACT FROM AUTHOR]

Many odors, such as poisonous and exhaust gases, are unsuitable for human detection. Therefore, techniques for predicting gas types and concentrations are essential. The metal oxide sensor utilized in this study is a standard gas sensor due to its excellent stability, affordability, and high sensitivity. However, it does have a drawback: lower selectivity towards different gases. To address this, we employ temperature modulation to enhance the sensor’s selectivity. By employing temperature modulation, we can observe the dynamic response of the sensor and capture more features. We utilize low-frequency square wave and triangular wave signals as heating voltages. The former represents rapid heating, while the latter embodies a slower heating process. The objective is to employ these two methods to classify and predict the concentrations of ethanol, methanol, MEK, and ethyl acetate, as well as mixtures of the four gases. Following feature extraction, a neural network is employed for classifying and predicting gas types and concentrations. The results demonstrate a 100% classification accuracy under two different heating voltages. Regarding concentration prediction, using the square wave alone yields an error range of +42.09 to -32.41 and a root mean square error (RMSE) of 4.68%; utilizing the triangular wave alone yields an error range of +13.86 to -18.10 and an RMSE of 4.33%. Employing both waveform types simultaneously results in an error range of +37.97 to -17.48 and an RMSE of 2.91%.

Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD. Can eNose technology be used for prospective detection of early lung cancer in patients with COPD? BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis. Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P <.01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95). Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD. [ABSTRACT FROM AUTHOR]

IntroductionProstate cancer (PCa) is known for its highly diverse clinical behavior, ranging from low-risk, slow-growing tumors to aggressive and life-threatening forms. To avoid over-treatment of low-risk PCa patients, it would be very important prior to any therapeutic intervention to appropriately classify subjects based on tumor aggressiveness. Unfortunately, there is currently no reliable test available for this purpose. The aim of the present study was to evaluate the ability of risk stratification of PCa subjects using an electronic nose (eNose) detecting PCa-specific volatile organic compounds (VOCs) in urine samples.MethodsThe study involved 120 participants who underwent diagnostic prostate biopsy followed by robot assisted radical prostatectomy (RARP). PCa risk was categorized as low, intermediate, or high based on the D’Amico risk classification and the pathological grade (PG) assessed after RARP. The eNose’s ability to categorize subjects for PCa risk stratification was evaluated based on accuracy and recall metrics.ResultsThe study population comprised 120 participants. When comparing eNose predictions with PG an accuracy of 79.2% (95%CI 70.8 – 86%) was found, while an accuracy of 74.2% (95%CI 65.4 – 81.7%) was found when compared to D’Amico risk classification system. Additionally, if compared low- versus -intermediate-/high-risk PCa, the eNose achieved an accuracy of 87.5% (95%CI 80.2-92.8%) based on PG or 90.8% (95%CI 84.2–95.3%) based on D’Amico risk classification. However, when using low-/-intermediate versus -high-risk PCa for PG, the accuracy was found to be 91.7% (95%CI 85.2-95.9%). Finally, an accuracy of 80.8% (95%CI72.6-87.4%) was found when compared with D’Amico risk classification.DiscussionThe findings of this study indicate that eNose may represent a valid alternative not only for early and non-invasive diagnosis of PCa, but also to categorize patients based on tumor aggressiveness. Further studies including a wider sample population will be necessary to confirm the potential clinical impact of this new technology.

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data. [ABSTRACT FROM AUTHOR]

Since 1982, the olfactory system of creatures has piqued the interest of academics who seek to create a comparable system. Despite its mysterious nature, the first stage has been successfully completed with the development of the ENose. Its extended applications have opened new doors for researchers, ranging from food quality testing to bomb detection and even, more recently, identifying those infected with the coronavirus. In this talk, we will review the structure and sensor behaviour of the ENose, as well as its applications, such as odour source localization and various applications in agriculture. The challenge of odour identification has prompted researchers to employ robots with sensors to investigate and locate odour sources. The present study aims to synthesize documented research and provide a fresh perspective on odour localization research efforts and tests conducted. The study highlights previous attempts to equip robots with sensors to explore the real indoor or outdoor environment. Initially, a review was conducted to investigate various aspects of the sector and the obstacles involved. [ABSTRACT FROM AUTHOR]

The advancements of chemical sensors over the past 50 years have significantly expanded the diversity of available sensors making them more affordable, portable, sensitive, and partially chemically selective. Despite these advancements, the diversity and capability of sensors used in breath analysis has remained limited due to challenges that persist in this field. This encompasses fluctuations in humidity, temperature, and oxygen concentration within breath that all pose challenges that can significantly affect sensor output. This review article aims to introduce and compare the techniques used in current chemical sensor arrays for breath analysis. A brief overview of the discrete chemical sensors and arrays employed in breath research, at both commercial and research levels, is discussed. Furthermore, current trends in data analysis for chemical sensor arrays in breath analysis is described. Finally, a detailed outlook of recent diagnostic results from studies implementing sensor arrays from the last 5 years is outlined. [Display omitted] • Concise overview on the working principles of discrete chemical sensors utilised in breath analysis. • A discussion on the application of sensor arrays, both commercial and custom, in breath analysis. • Diagnostic outputs from studies implementing sensors in breath analysiswithin the last five years. • Elucidation of the major hurdles in sensor-based breath analysis. [ABSTRACT FROM AUTHOR]

This paper assesses a custom single-type electronic nose (eNose) applied to differentiate the complex aromas generated by the caffeinated and decaffeinated versions of one encapsulated espresso coffee mixture type. The eNose used is composed of 16 single-type (identical) metal–oxide semiconductor (MOX) gas sensors based on microelectromechanical system (MEMS). This eNose proposal takes advantage of the small but inherent sensing variability of MOX gas sensors in order to provide a multisensorial description of volatiles or aromas. Results have shown that the information provided with this eNose processed using LDA is able to successfully discriminate the complex aromas of one caffeinated and decaffeinated encapsulated espresso coffee type. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the accuracy of a new electronic nose to recognize prostate cancer in urine samples. Methods: A blind, prospective study on consecutive patients was designed. Overall, 174 subjects were included in the study: 88 (50.6%) in prostate cancer group, and 86 (49.4%) in control group. Electronic nose performance for prostate cancer was assessed using sensitivity and specificity. The diagnostic accuracy of electronic nose was reported as area under the receiver operating characteristic curve. Results: The electronic nose in the study population reached a sensitivity 85.2% (95% confidence interval 76.1–91.9; 13 false negatives out of 88), a specificity 79.1% (95% confidence interval 69.0–87.1; 18 false positives out of 86). The accuracy of the electronic nose represented as area under the receiver operating characteristic curve 0.821 (95% confidence interval 0.764–0.879). Conclusions: The diagnostic accuracy of electronic nose for recognizing prostate cancer in urine samples is high, promising and susceptible to supplemental improvement. Additionally, further studies will be necessary to design a clinical trial to validate electronic nose application in diagnostic prostate cancer nomograms. [ABSTRACT FROM AUTHOR]

Context: Prostate-specific antigen is non-specific for prostate cancer. This is improved by multiparametric MRI but a significant amount of indolent prostate cancer is detected by the current MRI pathway and data is emerging that clinically significant cancers maybe missed using a standard PSA threshold. Volatile organic compound (VOC) analysis may offer novel biomarkers for prostate cancer and clinically significant disease., Objective: To perform a systematic review of the literature to evaluate the current evidence for the use of VOCs as novel biomarkers for prostate cancer and clinically significant prostate cancer., Evidence Acquisition: A systematic search of MEDLINE, Scopus, Web of Science and the Cochrane Library was undertaken by two independent reviewers and papers were assessed for inclusion in the review. Study characteristics, sensitivity and specificity of GC-MS or eNose were extracted. Risk of bias and applicability issues were determined using QUADAS 2 and the quality of reporting using the STARD checklist., Evidence Synthesis: Nineteen studies were included, of which 6 utilised eNose and 13 GC-MS. eNose sensitivity and specificity were 0.71-0.95 and 0.79-0.96, respectively, and GC-MS found a sensitivity and specificity of 0.66-1.00 and 0.53-0.97, respectively. There were concerns about bias in patient recruitment due to differences in the timing of the index test relative to the reference standard., Conclusion: This review has found promising early results for urinary metabolomics in the detection of prostate cancer. However, there is a need for larger, high-quality studies to validate this. Future work should focus on the detection of clinically significant prostate cancer., Competing Interests: We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property., (© 2024 The Author(s). BJUI Compass published by John Wiley & Sons Ltd on behalf of BJU International Company.)

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

The use of electronic noses (eNoses) as analysis tools are growing in popularity; however, the lack of a comprehensive, visual representation of how the different classes are organized and distributed largely complicates the interpretation of the classification results, thus reducing their practicality. The new contributions of this paper are the assessment of the multivariate classification performance of a custom, low-cost eNose composed of 16 single-type (identical) MOX gas sensors for the classification of three volatiles, along with a proposal to improve the visual interpretation of the classification results by means of generating a detailed 2D class-map representation based on the inverse of the orthogonal linear transformation obtained from a PCA and LDA analysis. The results showed that this single-type eNose implementation was able to perform multivariate classification, while the class-map visualization summarized the learned features and how these features may affect the performance of the classification, simplifying the interpretation and understanding of the eNose results. [ABSTRACT FROM AUTHOR]

This paper assesses a custom single-type electronic nose (eNose) applied to differentiate the complex aromas generated by the caffeinated and decaffeinated versions of one encapsulated espresso coffee mixture type. The eNose used is composed of 16 single-type (identical) metal–oxide semiconductor (MOX) gas sensors based on microelectromechanical system (MEMS). This eNose proposal takes advantage of the small but inherent sensing variability of MOX gas sensors in order to provide a multisensorial description of volatiles or aromas. Results have shown that the information provided with this eNose processed using LDA is able to successfully discriminate the complex aromas of one caffeinated and decaffeinated encapsulated espresso coffee type.

Recent clinical trials with immune checkpoint inhibitors (ICIs) have shown that a subgroup of patients with malignant pleural mesothelioma (MPM) could benefit from these agents. However, there are no accurate biomarkers to predict who will respond. The aim of this study was to assess the accuracy of exhaled breath analysis using electronic technology (eNose) for discriminating between responders to ICI and non-responders. This proof-of-concept prospective observational study was part of an intervention study (INITIATE) in patients with recurrent MPM who were treated with nivolumab (anti-PD-1) plus ipilimumab (anti-CTLA-4). At baseline and after six weeks of treatment, breath profiles were collected by an eNose. Modified Response Evaluation Criteria in Solid Tumors were used to assess efficacy at 6-month follow-up. For data processing and statistics, we used independent t-test analyses followed by linear discriminant and receiver-operating characteristic (ROC) analysis. Exhaled breath data of 31 MPM patients who received nivolumab plus ipilimumab were available at baseline. There were 16 with and 15 without a response after 6 months of treatment. At baseline, breath profiles significantly differed between responders and non-responders, with a cross validation value of 71%. The ROC-AUC after internal cross-validation was 0.90 (confidence interval: 0.80–1.00). An eNose is able to discriminate at baseline between responders and non-responders to nivolumab plus ipilimumab in MPM, thereby potentially identifying a subgroup of patients that will benefit from ICI treatment. • An eNose is able to discriminate responders to ICI treatment in MPM. • Breath profile changes during ICI treatment in MPM. • An eNose could be a tool for predicting response to nivolumab plus ipilimumab in patients with MPM. [ABSTRACT FROM AUTHOR]

Lung cancer is generally diagnosed at advanced stages when surgical resection is not possible. Late diagnosis, along with development of chemoresistance, results in high mortality. Preventive approaches, including smoking cessation, chemoprevention and early detection are needed to improve survival. Smoking cessation combined with low-dose computed tomography screening has modestly improved survival. Chemoprevention has also shown some promise. Despite these successes, most lung cancer cases remain undetected until advanced stages. Additional early detection strategies may further improve survival and treatment outcome. Molecular alterations taking place during lung carcinogenesis have the potential to be used in early detection via noninvasive methods and may also serve as biomarkers for success of chemopreventive approaches. This review focuses on the utilization of molecular biomarkers to increase the efficacy of various preventive approaches. [ABSTRACT FROM AUTHOR]

The use of electronic noses (eNoses) as analysis tools are growing in popularity; however, the lack of a comprehensive, visual representation of how the different classes are organized and distributed largely complicates the interpretation of the classification results, thus reducing their practicality. The new contributions of this paper are the assessment of the multivariate classification performance of a custom, low-cost eNose composed of 16 single-type (identical) MOX gas sensors for the classification of three volatiles, along with a proposal to improve the visual interpretation of the classification results by means of generating a detailed 2D class-map representation based on the inverse of the orthogonal linear transformation obtained from a PCA and LDA analysis. The results showed that this single-type eNose implementation was able to perform multivariate classification, while the class-map visualization summarized the learned features and how these features may affect the performance of the classification, simplifying the interpretation and understanding of the eNose results.

The artificial replication of an olfactory system is currently an open problem. The development of a portable and low-cost artificial olfactory system, also called electronic nose or eNose, is usually based on the use of an array of different gas sensors types, sensitive to different target gases. Low-cost Metal-Oxide semiconductor (MOX) gas sensors are widely used in such arrays. MOX sensors are based on a thin layer of silicon oxide with embedded heaters that can operate at different temperature set points, which usually have the disadvantages of different volatile sensitivity in each individual sensor unit and also different crossed sensitivity to different volatiles (unspecificity). This paper presents and eNose composed by an array of 16 low-cost BME680 digital miniature sensors embedding a miniature MOX gas sensor proposed to unspecifically evaluate air quality. In this paper, the inherent variability and unspecificity that must be expected from the 16 embedded MOX gas sensors, combined with signal processing, are exploited to classify two target volatiles: ethanol and acetone. The proposed eNose reads the resistance of the sensing layer of the 16 embedded MOX gas sensors, applies PCA for dimensional reduction and k-NN for classification. The validation results have shown an instantaneous classification success higher than 94% two days after the calibration and higher than 70% two weeks after, so the majority classification of a sequence of measures has been always successful in laboratory conditions. These first validation results and the low-power consumption of the eNose (0.9 W) enables its future improvement and its use in portable and battery-operated applications.

Electronic noses (eNoses) are emerging point-of-care tools that may help in the subphenotyping of chronic respiratory diseases such as asthma. We aimed to investigate whether eNoses can classify atopy in pediatric and adult patients with asthma. Participants with asthma and/or wheezing from 4 independent cohorts were included; BreathCloud participants (n = 429), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adults (n = 96), Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes pediatric participants (n = 100), and Pharmacogenetics of Asthma Medication in Children: Medication with Anti-Inflammatory Effects 2 participants (n = 30). Atopy was defined as a positive skin prick test result (≥3 mm) and/or a positive specific IgE level (≥0.35 kU/L) for common allergens. Exhaled breath profiles were measured by using either an integrated eNose platform or the SpiroNose. Data were divided into 2 training and 2 validation sets according to the technology used. Supervised data analysis involved the use of 3 different machine learning algorithms to classify patients with atopic versus nonatopic asthma with reporting of areas under the receiver operating characteristic curves as a measure of model performance. In addition, an unsupervised approach was performed by using a bayesian network to reveal data-driven relationships between eNose volatile organic compound profiles and asthma characteristics. Breath profiles of 655 participants (n = 601 adults and school-aged children with asthma and 54 preschool children with wheezing [68.2% of whom were atopic]) were included in this study. Machine learning models utilizing volatile organic compound profiles discriminated between atopic and nonatopic participants with areas under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and validation sets, respectively. The unsupervised approach revealed that breath profiles classifying atopy are not confounded by other patient characteristics. eNoses accurately detect atopy in individuals with asthma and wheezing in cohorts with different age groups and could be used in asthma phenotyping. [ABSTRACT FROM AUTHOR]

Colorectal carcinoma (CRC) has a worldwide incidence of 1.4 million patients and a large share in cancer-related mortality. After curative treatment, the risk of recurrence is 30–65%. Early detection may result in curative treatment. However, current follow-up (FU) examinations have low sensitivity ranging from 49 to 85% and are associated with high costs. Therefore, the search for a new diagnostic tool is justified. Analysis of volatile organic compound in exhaled air through an electronic nose (eNose) is a promising new patient-friendly diagnostic tool. We studied whether the eNose under investigation, the Aeonose™, is able to detect local recurrence or metastases of CRC. In this cross-sectional study we included 62 patients, all of whom underwent curative treatment for CRC in the past 5 years. Thirty-six of them had no metastases and 26 had extraluminal local recurrence or metastases of CRC, detected during FU. Breath testing was performed and machine learning was used to predict extraluminal recurrences or metastases, and based on the receiver operating characteristics (ROC)-curve both sensitivity and specificity were calculated. The eNose identified extra luminal local recurrences or metastases of CRC with a sensitivity and specificity of 0.88 (CI 0.69–0.97) and 0.75 (CI 0.57–0.87), respectively, with an overall accuracy of 0.81. This eNose may be a promising tool in detecting extraluminal local recurrences or metastases in the FU of curatively treated CRC. However, a well-designed prospective study is warranted to show its accuracy and predictive value before it can be used in clinical practice. [ABSTRACT FROM AUTHOR]

Asthma is a chronic respiratory disease that can lead to exacerbations, defined as acute episodes of worsening respiratory symptoms and lung function. Predicting the occurrence of these exacerbations is an important goal in asthma management. The measurement of exhaled breath by electronic nose (eNose) may allow for the monitoring of clinically unstable asthma and exacerbations. However, data on its ability to perform this is lacking. We aimed to evaluate whether eNose could identify patients that recently had asthma exacerbations. We performed a cross-sectional study, measuring exhaled breath using the SpiroNose in adults with a physician-reported diagnosis of asthma. Patients were randomly divided into a training (n = 252) and validation (n = 109) set. For the analysis of eNose signals, principal component (PC) and linear discriminant analysis (LDA) were performed. LDA, based on PC1-4, reliably discriminated between patients who had a recent exacerbation from those who had not (training receiver operating characteristic (ROC)–area under the curve (AUC) = 0.76,95% CI 0.69–0.82), (validation AUC = 0.76, 95% CI 0.64–0.87). Our study showed that, exhaled breath analysis using eNose could accurately identify asthma patients who recently had an exacerbation, and could indicate that asthma exacerbations have a specific exhaled breath pattern detectable by eNose.

Aim: Early detection and removal of colorectal cancer (CRC) and advanced adenomas (AAs) decreases the incidence of and mortality from the disease. We aimed to evaluate the potential of faecal volatile organic compounds (VOCs) for detection and follow‐up of colorectal adenoma using advanced electronic nose technology. Method: This was a prospective multi‐centre case–control cohort including two district hospitals and one tertiary referral hospital. Patients undergoing colonoscopy were instructed to collect a faecal sample prior to bowel cleansing and were included in the study when CRC, AAs, large adenomas (LAs; 0.5–1.0 cm), small adenomas (SAs; 0.1–0.5 cm) or no endoscopic abnormalities (controls) were observed. Patients undergoing polypectomy and controls were asked for a second sample after 3 months. Faecal VOCs were measured with gas chromatography–ion mobility spectrometry. Random forest, support vector machine, Gaussian process and neural net classification were used to evaluate accuracy. Results: In total, 14 patients with CRC, 64 with AAs, 69 with LAs, 127 with SAs and 227 controls were included. A second sample was collected from 32 polypectomy patients and 32 controls. Faecal VOCs discriminated CRC and adenomas from control [AUC (95% CI): CRC vs control 0.96 (0.89–1); AA vs control 0.96 (0.93–1); LA vs control 0.96 (0.92–0.99); SA vs control 0.96 (0.94–0.99)]. There were no significant differences between CRC and adenoma groups. Patients with adenomas and controls were discriminated prior to polypectomy, whereas 3 months after polypectomy VOC profiles were similar [T0 adenoma vs control 0.98 (0.95–1); T1 adenoma vs control 0.55 (0.40–0.69)]. Conclusions: Faecal VOC profiles may be useful for early detection of CRC and adenomas and the timing of polyp surveillance as polypectomy led to a normalization of the VOC profile. [ABSTRACT FROM AUTHOR]

Thioglycosides and C‐glycosides represent pharmacologically useful classes of glycomimetics that possess a high degree of biological stability. One emerging tool for the stereoselective synthesis of thioglycosides is the photoinitiated addition of thiols to unsaturated sugars. Moreover, thiyl radical‐mediated reactions of exo‐glycals and 1‐substituted endo‐glycals offer facile routes to β‐C‐glycosidic structures. This Concept article summarizes the thiol‐ene coupling strategies developed recently by our group and Somsák's group for the synthesis of several kinds of glycomimetics which are difficult to synthesize by conventional methods. One unusual characteristic of the thiol‐ene reactions of endo‐glycals is that heating inhibits, whereas cooling promotes the reaction. This unique temperature dependence as well as the effects of the enose structures and thiol configurations on the efficacy and stereoselectivity of the reactions are also discussed. [ABSTRACT FROM AUTHOR]

The photoinitiated thiol ene addition reaction is a highly stereo- and regioselective, and environmentally friendly reaction proceeding under mild conditions, hence it is ideally suited for the synthesis of carbohydrate mimetics. A comprehensive study on UV-light-induced reactions of 2,3- unsaturated O-, C-, S- and N-glycosides with various thiols was performed. The effect of experimental parameters and structural variations of the alkenes and thiols on the efficacy and regio- and stereoselectivity of the reactions was systematically studied and optimized. The type of anomeric heteroatom was found to profoundly affect the reactivity of 2,3-unsaturated sugars in the thiol ene couplings. Hydrothiolation of 2,3-dideoxy O-glycosyl enosides efficiently produced the axially C2-S-substituted addition products with high to complete regioselectivity. Moderate efficacy and varying regio- and stereoselectivity were observed with 2,3- unsaturated N-glycosides and no addition occurred onto the endocyclic double bond of C-glycosides. Upon hydrothiolation of 2,3-unsaturated S-glycosides, the addition of thiyl radicals was followed by elimination of the thiyl aglycone resulting in 3-S-substituted glyca [ABSTRACT FROM AUTHOR]

Background: Cyclosporine A (CsA) is an immunosuppressant with therapeutic indications in various immunological diseases; however, its use is associated with chronic nephropathy. Oxidative stress has a crucial role in CsA-induced nephrotoxicity. The present study evaluates the protective effect of edaravone on CsA-induced chronic nephropathy and investigates its antioxidant and nitric oxide modulating property. Methods: Male Sprague-Dawley rats (n=66) were distributed into nine groups, including a control (group 1) (n=7). Eight groups received CsA (15 mg/kg) for 28 days while being treated. The groups were categorized as: • Group 2: Vehicle (n=10) • Groups 3, 4, and 5: Edaravone (1, 5, and 10 mg/kg) (n=7 each) • Group 6: Diphenyliodonium chloride, a specific endothelial nitric oxide synthase (eNOS) inhibitor (n=7) • Group 7: Aminoguanidine, a specific inducible nitric oxide synthase (iNOS) inhibitor (n=7) • Group 8: Edaravone (10 mg/kg) plus diphenyliodonium chloride (n=7) • Group 9: Edaravone (10 mg/kg) plus aminoguanidine (n=7) Blood urea nitrogen and serum creatinine levels, malondialdehyde, superoxide dismutase, and glutathione reductase enzyme activities were measured using standard kits. Renal histopathological evaluations and measurements of eNOS and iNOS gene expressions by RT-PCR were also performed. Data were analyzed using one-way analysis of variance (ANOVA) followed by Tukey’s test (SPSS software version 18.0). Results: Edaravone (10 mg/kg) significantly attenuated CsA-induced oxidative stress, renal dysfunction, and kidney tissue injury. Aminoguanidine improved the renoprotective effect of edaravone. Edaravone reduced the elevated mRNA level of iNOS, but could not alter the level of eNOS mRNA significantly. Conclusion: Edaravone protects against CsA-induced chronic nephropathy using antioxidant property and probably through inhibiting iNOS gene expression.

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking 24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking 24 h. The area under the receiver–operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked

Fecal volatile organic compounds (VOC) reflect human and gut microbiota metabolic pathways and their interaction. VOC behold potential as non-invasive preclinical diagnostic biomarkers in various diseases, e.g., necrotizing enterocolitis and late onset sepsis. There is a need for standardization and assessment of the influence of clinical and environmental factors on the VOC outcome before this technique can be applied in clinical practice. The aim of this study was to investigate the influence of gestational age (GA) and mode of delivery on the fecal VOC pattern in preterm infants born below 30 weeks of gestation. Longitudinal fecal samples, collected on days 7, 14, and 21 postnatally, were analyzed by an electronic nose device (Cyranose 320®). In total, 58 preterm infants were included (29 infants born at GA 24–26 weeks vs. 29 at 27–29 completed weeks, 24 vaginally born vs. 34 via C-section). No differences were identified at any predefined time point in terms of GA and delivery mode (p > 0.05). We, therefore, concluded that correction for these factors in this population is not warranted when performing fecal VOC analysis in the first three weeks of life.

Fecal volatile organic compound (VOC) analysis has shown great potential as a noninvasive diagnostic biomarker for a variety of diseases. Before clinical implementation, the factors influencing the outcome of VOC analysis need to be assessed. Recent studies found that the sampling conditions can influence the outcome of VOC analysis. However, the dietary influences remains unknown, especially in (preterm) infants. Therefore, we assessed the effects of feeding composition on fecal VOC patterns of preterm infants (born at <30 weeks gestation). Two subgroups were defined: (1) daily intake >75% breastmilk (BM) feeding and (2) daily intake >75% formula milk (FM) feeding. Fecal samples, which were collected at 7, 14 and 21 days postnatally, were analyzed by an electronic nose device (Cyranose 320®). In total, 30 preterm infants were included (15 FM, 15 BM). No differences in the fecal VOC patterns were observed at the three predefined time-points. Combining the fecal VOC profiles of these time-points resulted in a statistically significant difference between the two subgroups although this discriminative accuracy was only modest (AUC [95% CI]; p-value; sensitivity; and specificity of 0.64 [0.51⁻0.77]; 0.04; 68%; and 51%, respectively). Our results suggest that the influence of enteral feeding on the outcome of fecal VOC analysis cannot be ignored in this population. Furthermore, in both subgroups, the fecal VOC patterns showed a stable longitudinal course within the first month of life. [ABSTRACT FROM AUTHOR]

Acute rhinosinusitis (ARS) is a sudden, symptomatic inflammation of the nasal and paranasal mucosa. It is usually caused by respiratory virus infection, but bacteria complicate for a small number of ARS patients. The differential diagnostics between viral and bacterial pathogens is difficult and currently no rapid methodology exists, so antibiotics are overprescribed. The electronic nose (eNose) has shown the ability to detect diseases from gas mixtures. Differential mobility spectrometry (DMS) is a next-generation device that can separate ions based on their different mobility in high and low electric fields. Five common rhinosinusitis bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, and Pseudomonas aeruginosa) were analysed in vitro with DMS. Classification was done using linear discriminant analysis (LDA) and k-nearest neighbour (KNN). The results were validated using leave-one-out cross-validation and separate train and test sets. With the latter, 77% of the bacteria were classified correctly with LDA. The comparative figure with KNN was 79%. In one train-test set, P. aeruginosa was excluded and the four most common ARS bacteria were analysed with LDA and KNN; the correct classification rate was 83 and 85%, respectively. DMS has shown its potential in detecting rhinosinusitis bacteria in vitro. The applicability of DMS needs to be studied with rhinosinusitis patients. [ABSTRACT FROM AUTHOR]

In recent years pollution due to hydrocarbon spills has increased markedly as a result of the numerous advances in technologies and industrial processes. Anthropogenic activities (accidental or illegal) are responsible for most of these incidents. In some cases, the spills are not detected at the moment they occur and the contaminants are subjected to different degradation phenomena that may change the chemical composition of the hydrocarbon over time. An incorrect or ineffective identification of the spill could lead to significant consequences, bearing in mind that most spills are hazardous to the environment. In the present work the capacity of the analytical technique based on the Electronic Nose (eNose) combined with chemometrics in the identification and discrimination of different weathered petroleum-derived products (PDPs) was studied. Different volumes (40 μL and 80 μL) of PDPs (gasoline, diesel, and paraffin) were poured onto different supports (wood, cork, paper, and cotton sheet) and subjected to a natural weathering process by evaporation for one month. The porosity of the support was also studied. The application of linear discriminant analysis allowed the full discrimination of the samples according to the presence/absence of PDP and a 97.7% of correct discrimination of the different PDPs regardless of the weathering time, support or volume used. The results show that the system is capable of detecting and discriminating the presence of petroleum-derived products in any of the situations studied. [ABSTRACT FROM AUTHOR]

In a fire investigation the rapid detection of the presence of ignitable liquids like gasoline is of great importance as it allows appropriate treatment of the remains, the identification of prevention methods and detects the possible presence of an arsonist. In some cases, analysts cannot access the fire scene in the first few hours due to the dangers involved in the situation and, as a consequence, phenomena such as weathering start. Ignitable liquid weathering is an evaporation process that results in an increase in the abundance of non-volatile compounds relative to volatile compounds, and this process changes the chemical composition. In the present work, the weathering of samples of gasoline at different times (from 0 h to a month) has been studied using an electronic nose (eNose). The influence of the volume used (40 μL and 80 μL) and the type of support (cork, wood, paper and cotton sheet) has been studied. Chemometric tools have been used with the aim of ascertaining the weathering time for which the developed method is capable of detecting the presence of gasoline. The eNose was able to discriminate samples of weathered gasoline. The support used for the samples did not seem to have an influence on the detection and the system. [ABSTRACT FROM AUTHOR]

In the present work, a novel, portable and innovative eNose composed of a surface acoustic wave (SAW) sensor array based on zeolitic imidazolate frameworks, ZIF-8 and ZIF-67 nanocrystals (pure and combined with gold nanoparticles), as sensitive layers has been tested as a non-invasive system to detect different disease markers, such as acetone, ethanol and ammonia, related to the diagnosis and control of diabetes mellitus through exhaled breath. The sensors have been prepared by spin coating, achieving continuous sensitive layers at the surface of the SAW device. Low concentrations (5 ppm, 10 ppm and 25 ppm) of the marker analytes were measured, obtaining high sensitivities, good reproducibility, short time response and fast signal recovery.

Electronic nose (eNose) systems are particularly appreciated for their portability, usability, relative low cost, and real-time or near real-time response. Their application finds space in several domains, including environmental monitoring. Within this field, marine monitoring is of particular scientific relevance due to the fragility of this specific environment, daily threatened by human activities that can potentially bring to catastrophic and irreversible consequences on marine wildlife. Under such considerations, a systematic review, complying with the PRISMA guidelines, was conducted covering the period up to 15 October 2018, in PubMed, ScienceDirect, and Google Scholar. Despite the relatively low number of articles published on this specific topic and the heterogeneity of the technological approaches employed, the results obtained by the various groups highlight the positive contribution eNose has given and can provide in near future for the monitoring and safeguarding of this delicate environment.

In this work, a new generation of eNose systems particularly suited for exhaled breath gas analysis is presented. The developed analyzer system comprises a compact modular, low volume, temperature controlled sensing chamber explicitly tested for the detection of acetone, isoprene, pentane and isopropanol. The eNose system sensing chamber consists of three compartments, each of which can contain 8 analog Metal Oxide (MOX) sensors or 10 digital MOX sensors. Additional sensors within the digital compartment allow for pressure, humidity and temperature measurements. The presented eNose system contains a sensor array with up to 30 physical sensors and provides the ability to discriminate between low VOC concentrations under dry and humid conditions. The MOX sensor signals were analyzed by pattern recognition methods.

Background: Cyclosporine A (CsA) is an immunosuppressant with therapeutic indications in various immunological diseases; however, its use is associated with chronic nephropathy. Oxidative stress has a crucial role in CsA-induced nephrotoxicity. The present study evaluates the protective effect of edaravone on CsA-induced chronic nephropathy and investigates its antioxidant and nitric oxide modulating property. Methods: Male Sprague-Dawley rats (n=66) were distributed into nine groups, including a control (group 1) (n=7). Eight groups received CsA (15 mg/kg) for 28 days while being treated. The groups were categorized as: * Group 2: Vehicle (n=10) * Groups 3, 4, and 5: Edaravone (1, 5, and 10 mg/kg) (n=7 each) * Group 6: Diphenyliodonium chloride, a specific endothelial nitric oxide synthase (eNOS) inhibitor (n=7) * Group 7: Aminoguanidine, a specific inducible nitric oxide synthase (iNOS) inhibitor (n=7) * Group 8: Edaravone (10 mg/kg) plus diphenyliodonium chloride (n=7) * Group 9: Edaravone (10 mg/kg) plus aminoguanidine (n=7) Blood urea nitrogen and serum creatinine levels, malondialdehyde, superoxide dismutase, and glutathione reductase enzyme activities were measured using standard kits. Renal histopathological evaluations and measurements of eNOS and iNOS gene expressions by RT-PCR were also performed. Data were analyzed using one-way analysis of variance (ANOVA) followed by Tukey's test (SPSS software version 18.0). Results: Edaravone (10 mg/kg) significantly attenuated CsA-induced oxidative stress, renal dysfunction, and kidney tissue injury. Aminoguanidine improved the renoprotective effect of edaravone. Edaravone reduced the elevated mRNA level of iNOS, but could not alter the level of eNOS mRNA significantly. Conclusion: Edaravone protects against CsA-induced chronic nephropathy using antioxidant property and probably through inhibiting iNOS gene expression. [ABSTRACT FROM AUTHOR]