Pub Date : 2024-03-28DOI: 10.1088/1752-7163/ad3572
Teny M John, Nabin K Shrestha, Leen Hasan, Kirk Pappan, Owen Birch, David Grove, Billy Boyle, Max Allsworth, Priyanka Shrestha, Gary W Procop, Raed A Dweik
Clostridioides difficileinfection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity andClostridioides difficilepolymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negativeC.difficiletesting (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.
{"title":"Detection of<i>Clostridioides difficile</i>infection by assessment of exhaled breath volatile organic compounds.","authors":"Teny M John, Nabin K Shrestha, Leen Hasan, Kirk Pappan, Owen Birch, David Grove, Billy Boyle, Max Allsworth, Priyanka Shrestha, Gary W Procop, Raed A Dweik","doi":"10.1088/1752-7163/ad3572","DOIUrl":"10.1088/1752-7163/ad3572","url":null,"abstract":"<p><p><i>Clostridioides difficile</i>infection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity and<i>Clostridioides difficile</i>polymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negative<i>C.difficile</i>testing (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140174930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-21DOI: 10.1088/1752-7163/ad324f
Linda Mezmale, Daria Ślefarska-Wolak, Manohar Prasad Bhandari, Clemens Ager, Viktors Veliks, Veronika Patsko, Andrii Lukashenko, Emmanuel Dias-Neto, Diana Noronha Nunes, Thais Fernanda Bartelli, Adriane Graicer Pelosof, Claudia Zitron Sztokfisz, Raúl Murillo, Agnieszka Królicka, Chris A Mayhew, Marcis Leja, Hossam Haick, Pawel Mochalski
Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer.
{"title":"Volatilomic profiles of gastric juice in gastric cancer patients.","authors":"Linda Mezmale, Daria Ślefarska-Wolak, Manohar Prasad Bhandari, Clemens Ager, Viktors Veliks, Veronika Patsko, Andrii Lukashenko, Emmanuel Dias-Neto, Diana Noronha Nunes, Thais Fernanda Bartelli, Adriane Graicer Pelosof, Claudia Zitron Sztokfisz, Raúl Murillo, Agnieszka Królicka, Chris A Mayhew, Marcis Leja, Hossam Haick, Pawel Mochalski","doi":"10.1088/1752-7163/ad324f","DOIUrl":"10.1088/1752-7163/ad324f","url":null,"abstract":"<p><p>Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140101659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-13DOI: 10.1088/1752-7163/ad2b6e
Ruth P Cusack, Robyn Larracy, Christian B Morrell, Maral Ranjbar, Jennifer Le Roux, Christiane E Whetstone, Maxime Boudreau, Patrick F Poitras, Thiviya Srinathan, Eric Cheng, Karen Howie, Catie Obminski, Tim O'Shea, Rebecca J Kruisselbrink, Terence Ho, Erik Scheme, Stephen Graham, Gisia Beydaghyan, Gail M Gavreau, MyLinh Duong
Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%-81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%-74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.
背景
严重急性呼吸系统综合症冠状病毒-2(SARS-CoV-2)的检测依赖于鼻咽拭子上的实时逆转录酶聚合酶链反应(RT-PCR)。当病毒负荷和感染位于下呼吸道和肺实质的远端时,RT-PCR 的假阴性率会很高。我们需要一种安全、简单、方便的下呼吸道取样方法,以帮助早期快速诊断 COVID-19 肺炎。健康人或 RT-PCR 阴性且无呼吸道症状的住院患者被纳入对照组。收集的呼吸样本通过激光吸收光谱 (LAS) 分析挥发性有机化合物 (VOC),并通过机器学习 (ML) 方法进行分类,以识别 SARS-CoV-2 的独特 LAS 光谱模式(呼吸样本)。使用 LAS 呼吸指纹训练 ML 分类器模型,在区分 SARS-CoV2 阳性组和阴性组方面的准确率为 72-2-81-7%。在不同年龄、性别、体重指数、SARS-CoV-2 变体、发病时间和需氧量的亚组中,准确率保持一致。总体性能高于 VOC 训练的分类器模型,后者的准确率为 63-74-7%。该技术和 ML 方法只需少量培训即可在任何环境中轻松部署。这将极大地提高可及性和可扩展性,以满足激增的容量;允许早期和快速检测,为治疗提供信息;并为快速开发新的分类器模型以应对未来的爆发提供了极大的通用性。
{"title":"Machine learning enabled detection of COVID-19 pneumonia using exhaled breath analysis: a proof-of-concept study.","authors":"Ruth P Cusack, Robyn Larracy, Christian B Morrell, Maral Ranjbar, Jennifer Le Roux, Christiane E Whetstone, Maxime Boudreau, Patrick F Poitras, Thiviya Srinathan, Eric Cheng, Karen Howie, Catie Obminski, Tim O'Shea, Rebecca J Kruisselbrink, Terence Ho, Erik Scheme, Stephen Graham, Gisia Beydaghyan, Gail M Gavreau, MyLinh Duong","doi":"10.1088/1752-7163/ad2b6e","DOIUrl":"10.1088/1752-7163/ad2b6e","url":null,"abstract":"<p><p>Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%-81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%-74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139931321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-12DOI: 10.1088/1752-7163/ad23f5
Hsuan Chou, Kayleigh Arthur, Elen Shaw, Chad Schaber, Billy Boyle, Max Allsworth, Eli F Kelley, Glenn M Stewart, Courtney M Wheatley, Jesse Schwartz, Caitlin C Fermoyle, Briana L Ziegler, Kay A Johnson, Paul Robach, Patrick Basset, Bruce D Johnson
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 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. The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjustedP-value < .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.
{"title":"Metabolic insights at the finish line: deciphering physiological changes in ultramarathon runners through breath VOC analysis.","authors":"Hsuan Chou, Kayleigh Arthur, Elen Shaw, Chad Schaber, Billy Boyle, Max Allsworth, Eli F Kelley, Glenn M Stewart, Courtney M Wheatley, Jesse Schwartz, Caitlin C Fermoyle, Briana L Ziegler, Kay A Johnson, Paul Robach, Patrick Basset, Bruce D Johnson","doi":"10.1088/1752-7163/ad23f5","DOIUrl":"10.1088/1752-7163/ad23f5","url":null,"abstract":"<p><p>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 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. The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjusted<i>P</i>-value < .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.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139642169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1088/1752-7163/ad2002
Robert van Vorstenbosch, Alex Mommers, Daniëlle Pachen, Frederik-Jan van Schooten, Agnieszka Smolinska
Disease detection and monitoring using volatile organic compounds (VOCs) is becoming increasingly popular. For a variety of (gastrointestinal) diseases the microbiome should be considered. As its output is to large extent volatile, faecal volatilomics carries great potential. One technical limitation is that current faecal headspace analysis requires specialized instrumentation which is costly and typically does not work in harmony with thermal desorption units often utilized in e.g. exhaled breath studies. This lack of harmonization hinders uptake of such analyses by the Volatilomics community. Therefore, this study optimized and compared two recently harmonized faecal headspace sampling platforms:High-capacity Sorptive extraction (HiSorb) probesand theMicrochamber thermal extractor (Microchamber). Statistical design of experiment was applied to find optimal sampling conditions by maximizing reproducibility, the number of VOCs detected, and between subject variation. To foster general applicability those factors were defined using semi-targeted as well as untargeted metabolic profiles. HiSorb probes were found to result in a faster sampling procedure, higher number of detected VOCs, and higher stability. The headspace collection using the Microchamber resulted in a lower number of detected VOCs, longer sampling times and decreased stability despite a smaller number of interfering VOCs and no background signals. Based on the observed profiles, recommendations are provided on pre-processing and study design when using either one of both platforms. Both can be used to perform faecal headspace collection, but altogether HiSorb is recommended.
{"title":"The optimization and comparison of two high-throughput faecal headspace sampling platforms: the microchamber/thermal extractor and hi-capacity sorptive extraction probes (HiSorb).","authors":"Robert van Vorstenbosch, Alex Mommers, Daniëlle Pachen, Frederik-Jan van Schooten, Agnieszka Smolinska","doi":"10.1088/1752-7163/ad2002","DOIUrl":"10.1088/1752-7163/ad2002","url":null,"abstract":"<p><p>Disease detection and monitoring using volatile organic compounds (VOCs) is becoming increasingly popular. For a variety of (gastrointestinal) diseases the microbiome should be considered. As its output is to large extent volatile, faecal volatilomics carries great potential. One technical limitation is that current faecal headspace analysis requires specialized instrumentation which is costly and typically does not work in harmony with thermal desorption units often utilized in e.g. exhaled breath studies. This lack of harmonization hinders uptake of such analyses by the Volatilomics community. Therefore, this study optimized and compared two recently harmonized faecal headspace sampling platforms:<i>High-capacity Sorptive extraction (HiSorb) probes</i>and the<i>Microchamber thermal extractor (Microchamber)</i>. Statistical design of experiment was applied to find optimal sampling conditions by maximizing reproducibility, the number of VOCs detected, and between subject variation. To foster general applicability those factors were defined using semi-targeted as well as untargeted metabolic profiles. HiSorb probes were found to result in a faster sampling procedure, higher number of detected VOCs, and higher stability. The headspace collection using the Microchamber resulted in a lower number of detected VOCs, longer sampling times and decreased stability despite a smaller number of interfering VOCs and no background signals. Based on the observed profiles, recommendations are provided on pre-processing and study design when using either one of both platforms. Both can be used to perform faecal headspace collection, but altogether HiSorb is recommended.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139491511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01DOI: 10.1088/1752-7163/ad2213
Xiao Xian Qian
Characteristics of extra-oral halitosis induced by functional constipation (FC) have never been revealed. To address this, this prospective cohort was conducted with 100 FC patients, who were divided into a halitosis group and a negative group. Organoleptic score (OLS) ⩾ 2 in nose breath was diagnosed as extra-oral halitosis. Concentration of overall volatile sulfur compounds (VSCs) measured by Halimeter, concentration of hydrogen sulfide (HS), methanethiol (MT), dimethyl sulfide (DMS) and their total amount measured by OralChroma in nose breath was recorded asC-VSC,C-HS,C-MT,C-DMS andC-sum respectively. We found that 82% (82/100) of the FC patients had extra-oral halitosis. However, only 12.5% (3/82) and 1.22% (1/82) of halitosis group were correctly diagnosed with the current diagnostic threshold ofC-VSC ⩾ 110 parts per billion (ppb) and ⩾150 ppb.C-VSC,C-DMS andC-sum were significantly higher in the halitosis group compared to the negative group (allP< 0.001), with ratios of about 2.2 times, 3.1 times and 2.1 times respectively.C-HS andC-MT were low and not significantly different between the groups. Positive correlations were observed among OLS,C-VSC,C-DMS andC-sum. The area under curve of receiver operating characteristics ofC-VSC, C-DMS andC-sum for predicting FC-induced halitosis was 0.909, 0.9073 and 0.962 respectively, with the threshold values of ⩾36 ppb, ⩾52 ppb and ⩾75 ppb respectively. Therefore, we conclude that: (1) DMS is the primary contributor to FC-induced extra-oral halitosis. (2) OLS, Halimeter and OralChroma are consistent in detecting FC-induced extra-oral halitosis. (3) The diagnostic threshold for Halimeter should be adjusted toC-VSC ⩾ 36 ppb and the diagnostic threshold for OralChroma should be set asC-DMS ⩾ 52 ppb for diagnosing FC-induced extra-oral halitosis.
{"title":"Characteristics of extra-oral halitosis induced by functional constipation: a prospective cohort study.","authors":"Xiao Xian Qian","doi":"10.1088/1752-7163/ad2213","DOIUrl":"10.1088/1752-7163/ad2213","url":null,"abstract":"<p><p>Characteristics of extra-oral halitosis induced by functional constipation (FC) have never been revealed. To address this, this prospective cohort was conducted with 100 FC patients, who were divided into a halitosis group and a negative group. Organoleptic score (OLS) ⩾ 2 in nose breath was diagnosed as extra-oral halitosis. Concentration of overall volatile sulfur compounds (VSCs) measured by Halimeter, concentration of hydrogen sulfide (HS), methanethiol (MT), dimethyl sulfide (DMS) and their total amount measured by OralChroma in nose breath was recorded as<i>C</i>-VSC,<i>C</i>-HS,<i>C</i>-MT,<i>C</i>-DMS and<i>C</i>-sum respectively. We found that 82% (82/100) of the FC patients had extra-oral halitosis. However, only 12.5% (3/82) and 1.22% (1/82) of halitosis group were correctly diagnosed with the current diagnostic threshold of<i>C</i>-VSC ⩾ 110 parts per billion (ppb) and ⩾150 ppb.<i>C</i>-VSC,<i>C</i>-DMS and<i>C</i>-sum were significantly higher in the halitosis group compared to the negative group (all<i>P</i>< 0.001), with ratios of about 2.2 times, 3.1 times and 2.1 times respectively.<i>C</i>-HS and<i>C</i>-MT were low and not significantly different between the groups. Positive correlations were observed among OLS,<i>C</i>-VSC,<i>C</i>-DMS and<i>C</i>-sum. The area under curve of receiver operating characteristics of<i>C</i>-VSC<i>, C</i>-DMS and<i>C</i>-sum for predicting FC-induced halitosis was 0.909, 0.9073 and 0.962 respectively, with the threshold values of ⩾36 ppb, ⩾52 ppb and ⩾75 ppb respectively. Therefore, we conclude that: (1) DMS is the primary contributor to FC-induced extra-oral halitosis. (2) OLS, Halimeter and OralChroma are consistent in detecting FC-induced extra-oral halitosis. (3) The diagnostic threshold for Halimeter should be adjusted to<i>C</i>-VSC ⩾ 36 ppb and the diagnostic threshold for OralChroma should be set as<i>C</i>-DMS ⩾ 52 ppb for diagnosing FC-induced extra-oral halitosis.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139546539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-25DOI: 10.1088/1752-7163/ad2003
Xiao Xian Qian
Some studies have examined the impact of intra-oral halitosis on quality of life (QOL), but the impact of enterogenous extra-oral halitosis (EOH) on QOL has not been previously studied. We conducted a retrospective analysis of data from 88 patients with enterogenous EOH who visited our online halitosis clinic. A specialized halitosis associated life-quality test (HALT) questionnaire was used to assess QOL of these patients. Spearman correlation analysis was performed to investigate the relationship between HALT score and age. We found that 21 (23.86%) patients were male and 67 (76.14%) patients were female. HALT scores in females were significantly higher than in males (57.6 ± 13.6vs.45.5 ± 11.9,P< 0.001). Additionally, 13 of the 20 items of the HALT questionnaire showed significant differences between the sexes. No correlation was identified between HALT score and age. Therefore, we conclude that: (1) enterogenous EOH has a more severe impact on QOL in females compared to males. (2) More females with EOH visit the offline halitosis clinic compared to males. (3) The QOL of patients with enterogenous EOH does not decline with age.
一些研究探讨了口内口臭对生活质量(QOL)的影响,但此前尚未研究过肠源性口外口臭(EOH)对生活质量的影响。我们对 88 名就诊于在线口臭门诊的肠源性口臭患者的数据进行了回顾性分析。我们使用专门的口臭相关生活质量测试(HALT)问卷来评估这些患者的 QOL。为了研究 HALT 分数与年龄之间的关系,我们进行了斯皮尔曼相关分析。我们发现,21 名(23.86%)患者为男性,67 名(76.14%)患者为女性。女性的 HALT 分数明显高于男性(57.6 ± 13.6 vs. 45.5 ± 11.9,P < 0.001)。此外,在 HALT 问卷的 20 个项目中,有 13 个项目在性别上有显著差异。HALT 分数与年龄之间没有相关性。因此,我们得出以下结论(1) 与男性相比,女性肠源性 EOH 对 QOL 的影响更为严重。(2)与男性相比,更多患有肠源性 EOH 的女性前往线下口臭诊所就诊。(3)肠源性 EOH 患者的 QOL 不会随年龄增长而下降。
{"title":"Enterogenous extra-oral halitosis has a more severe impact on quality of life in females compared to males.","authors":"Xiao Xian Qian","doi":"10.1088/1752-7163/ad2003","DOIUrl":"10.1088/1752-7163/ad2003","url":null,"abstract":"<p><p>Some studies have examined the impact of intra-oral halitosis on quality of life (QOL), but the impact of enterogenous extra-oral halitosis (EOH) on QOL has not been previously studied. We conducted a retrospective analysis of data from 88 patients with enterogenous EOH who visited our online halitosis clinic. A specialized halitosis associated life-quality test (HALT) questionnaire was used to assess QOL of these patients. Spearman correlation analysis was performed to investigate the relationship between HALT score and age. We found that 21 (23.86%) patients were male and 67 (76.14%) patients were female. HALT scores in females were significantly higher than in males (57.6 ± 13.6<i>vs.</i>45.5 ± 11.9,<i>P</i>< 0.001). Additionally, 13 of the 20 items of the HALT questionnaire showed significant differences between the sexes. No correlation was identified between HALT score and age. Therefore, we conclude that: (1) enterogenous EOH has a more severe impact on QOL in females compared to males. (2) More females with EOH visit the offline halitosis clinic compared to males. (3) The QOL of patients with enterogenous EOH does not decline with age.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139491508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-24DOI: 10.1088/1752-7163/ad1d65
Xiaoxiao Li, Pan Chang, Xing Liu, Yi Kang, Zhongjun Zhao, Yixiang Duan, Jin Liu, Wensheng Zhang
The correlation between propofol concentration in exhaled breath (CE) and plasma (CP) has been well-established, but its applicability for estimating the concentration in brain tissues (CB) remains unknown. Given the impracticality of directly sampling human brain tissues, rats are commonly used as a pharmacokinetic model due to their similar drug-metabolizing processes to humans. In this study, we measuredCE,CP, andCBin mechanically ventilated rats injected with propofol. Exhaled breath samples from the rats were collected every 20 s and analyzed using our team's developed vacuum ultraviolet time-of-flight mass spectrometry. Additionally, femoral artery blood samples and brain tissue samples at different time points were collected and measured using high-performance liquid chromatography mass spectrometry. The results demonstrated that propofol concentration in exhaled breath exhibited stronger correlations with that in brain tissues compared to plasma levels, suggesting its potential suitability for reflecting anesthetic action sites' concentrations and anesthesia titration. Our study provides valuable animal data supporting future clinical applications.
{"title":"Exhaled breath is found to be better than blood samples for determining propofol concentrations in the brain tissues of rats.","authors":"Xiaoxiao Li, Pan Chang, Xing Liu, Yi Kang, Zhongjun Zhao, Yixiang Duan, Jin Liu, Wensheng Zhang","doi":"10.1088/1752-7163/ad1d65","DOIUrl":"10.1088/1752-7163/ad1d65","url":null,"abstract":"<p><p>The correlation between propofol concentration in exhaled breath (<i>C</i><sub>E</sub>) and plasma (<i>C</i><sub>P</sub>) has been well-established, but its applicability for estimating the concentration in brain tissues (<i>C</i><sub>B</sub>) remains unknown. Given the impracticality of directly sampling human brain tissues, rats are commonly used as a pharmacokinetic model due to their similar drug-metabolizing processes to humans. In this study, we measured<i>C</i><sub>E</sub>,<i>C</i><sub>P</sub>, and<i>C</i><sub>B</sub>in mechanically ventilated rats injected with propofol. Exhaled breath samples from the rats were collected every 20 s and analyzed using our team's developed vacuum ultraviolet time-of-flight mass spectrometry. Additionally, femoral artery blood samples and brain tissue samples at different time points were collected and measured using high-performance liquid chromatography mass spectrometry. The results demonstrated that propofol concentration in exhaled breath exhibited stronger correlations with that in brain tissues compared to plasma levels, suggesting its potential suitability for reflecting anesthetic action sites' concentrations and anesthesia titration. Our study provides valuable animal data supporting future clinical applications.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-23DOI: 10.1088/1752-7163/ad1cf1
Zachary Joseph Sasiene, Erick Scott LeBrun, Eric Schaller, Phillip Michael Mach, Robert Taylor, Lionel Candelaria, Trevor Griffiths Glaros, Justin Baca, Ethan Matthew McBride
The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (p< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.
{"title":"Real-time breath analysis towards a healthy human breath profile.","authors":"Zachary Joseph Sasiene, Erick Scott LeBrun, Eric Schaller, Phillip Michael Mach, Robert Taylor, Lionel Candelaria, Trevor Griffiths Glaros, Justin Baca, Ethan Matthew McBride","doi":"10.1088/1752-7163/ad1cf1","DOIUrl":"10.1088/1752-7163/ad1cf1","url":null,"abstract":"<p><p>The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (<i>p</i>< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139417203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1088/1752-7163/ad1d64
Justin D M Martin, Falzone Claudia, Anne-Claude Romain
Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between 'healthy' samples (breath without additives) and 'cancer' samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.
{"title":"How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment.","authors":"Justin D M Martin, Falzone Claudia, Anne-Claude Romain","doi":"10.1088/1752-7163/ad1d64","DOIUrl":"10.1088/1752-7163/ad1d64","url":null,"abstract":"<p><p>Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between 'healthy' samples (breath without additives) and 'cancer' samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.</p>","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}