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Exhaled breath condensate (EBC) in respiratory diseases: Recent advances, and future perspectives in the age of Omic sciences. 呼吸系统疾病中的呼出气体冷凝物(EBC):Omic 科学时代的最新进展和未来展望。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-13 DOI: 10.1088/1752-7163/ad7a9a
Mauro Maniscalco,Claudio Candia,Salvatore Fuschillo,Pasquale Ambrosino,Debora Paris,Andrea Motta
Exhaled breath condensate (EBC) is used as a promising noninvasive diagnostic tool in the field of respiratory medicine. EBC is achieved by cooling exhaled air, which contains aerosolized particles and volatile compounds present in the breath. This method provides useful information on the biochemical and inflammatory state of the airways. In respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD) and cystic fibrosis, EBC analysis can reveal elevated levels of biomarkers such as hydrogen peroxide, nitric oxide and various cytokines, which correlate with oxidative stress and inflammation. Furthermore, the presence of certain volatile organic compounds (VOCs) in EBC has been linked to specific respiratory conditions, potentially serving as disease-specific fingerprints. The noninvasive nature of EBC sampling makes it particularly useful for repeated measures and for use in vulnerable populations, including children and the elderly. Despite its potential, the standardization of collection methods, analytical techniques and interpretation of results currently limits its use in clinical practice. Nonetheless, EBC holds significant promise for improving the diagnosis, monitoring and therapy of respiratory diseases. In this tutorial we will present the latest advances in EBC research in airway diseases and future prospects for clinical applications of EBC analysis, including the application of the Omic sciences for its analysis. .
呼出气体冷凝物(EBC)是呼吸医学领域一种前景广阔的无创诊断工具。EBC 是通过冷却呼出的空气实现的,呼出的空气中含有气溶胶颗粒和挥发性化合物。这种方法可提供有关气道生化和炎症状态的有用信息。在哮喘、慢性阻塞性肺病 (COPD) 和囊性纤维化等呼吸系统疾病中,EBC 分析可揭示过氧化氢、一氧化氮和各种细胞因子等生物标记物水平的升高,这些标记物与氧化应激和炎症相关。EBC 采样的非侵入性使其特别适用于重复测量,也适用于包括儿童和老人在内的易感人群。尽管EBC具有很大的潜力,但其采集方法、分析技术和结果解释的标准化目前限制了它在临床实践中的应用。 在本教程中,我们将介绍气道疾病EBC研究的最新进展以及EBC分析临床应用的未来前景,包括应用Omic科学进行分析。
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Furthermore, the presence of certain volatile organic compounds (VOCs) in EBC has been linked to specific respiratory conditions, potentially serving as disease-specific fingerprints. The noninvasive nature of EBC sampling makes it particularly useful for repeated measures and for use in vulnerable populations, including children and the elderly. Despite its potential, the standardization of collection methods, analytical techniques and interpretation of results currently limits its use in clinical practice. 
Nonetheless, EBC holds significant promise for improving the diagnosis, monitoring and therapy of respiratory diseases.
In this tutorial we will present the latest advances in EBC research in airway diseases and future prospects for clinical applications of EBC analysis, including the application of the Omic sciences for its analysis.&#xD.","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262032","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}
引用次数: 0
Rapid point-of-care breath test predicts breast cancer and abnormal mammograms in symptomatic women. 快速护理点呼气试验可预测有症状妇女的乳腺癌和异常乳房 X 光检查。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-12 DOI: 10.1088/1752-7163/ad7a20
Michael Phillips,Therese Bevers,Linda Larsen,Nadine Pappas,Sonali Pathak
Previous studies have reported volatile organic compounds (VOCs) in the breath as biomarkers of breast cancer. These biomarkers may be derived from cancer-associated fibroblasts, in which oxidative stress degrades polyunsaturated fatty acids to volatile alkanes and methylated alkane derivatives that are excreted in the breath. We evaluated a rapid point-of-care test for breath VOC biomarkers as predictors of breast cancer and abnormal mammograms. Methods: We studied 593 women aged ≥ 18 yr referred to three sites for mammography for a symptomatic breast-related concern (e.g. breast mass, nipple discharge). A rapid point-of-care breath testing system collected and concentrated alveolar breath VOCs on a sorbent trap and analyzed them with gas chromatography and surface acoustic wave detection in < 6 min. Breath VOC chromatograms were randomly assigned to a training set or to a validation set. Monte Carlo analysis identified significant breath VOC biomarkers of breast cancer and abnormal mammograms in the training set, and these biomarkers were incorporated into a multivariate algorithm to predict disease in the validation set. Results: Prediction of breast cancer: 50 women had biopsy-proven breast cancer (invasive cancer 41, ductal non-invasive cancer 9) Unsplit data set: Breath VOCs identified breast cancer with 83% accuracy (area under curve of receiver operating characteristic), 82% sensitivity and 77.1% specificity. Split data sets: Training set breath VOCs identified breast cancer with 80.3% accuracy, 84% sensitivity and 74.3% specificity. Corresponding values in the validation set were 68%% accuracy, 72.4% sensitivity and 61.5% specificity. Prediction of BIRADS 4 and 5 mammograms (versus BIRADS 1, 2 and 3): Unsplit data set: Breath VOCs identified abnormal mammograms with 76.2% accuracy. Split data sets: Breath VOCs identified abnormal mammograms with 74.2% accuracy, 73.3% sensitivity and 60% specificity. Corresponding values in the validation set were 60.5% accuracy, 64.2% sensitivity and 51% specificity. Conclusions: A rapid point-of-care test for breath VOC biomarkers predicted risk of breast cancer and abnormal mammograms in women with breast-related symptoms. .
以往的研究报告称,呼气中的挥发性有机化合物(VOC)是乳腺癌的生物标志物。这些生物标志物可能来自与癌症相关的成纤维细胞,在成纤维细胞中,氧化应激将多不饱和脂肪酸降解为挥发性烷烃和甲基化烷烃衍生物,并随呼吸排出体外。我们评估了一种快速的呼气挥发性有机化合物生物标志物床旁检测方法,该方法可预测乳腺癌和乳房 X 光检查异常:我们对 593 名年龄≥ 18 岁的女性进行了研究,她们因乳房相关症状(如乳房肿块、乳头溢液)而被转诊到三个地点进行乳房 X 光检查。快速护理点呼气检测系统在吸附剂捕集器上收集并浓缩肺泡呼气中的挥发性有机化合物,并在 < 6 分钟内用气相色谱法和表面声波检测法对其进行分析。呼气挥发性有机化合物色谱图被随机分配到训练集或验证集。蒙特卡洛分析确定了训练集中乳腺癌和乳房 X 线照片异常的重要呼出气体挥发性有机化合物生物标志物,并将这些生物标志物纳入多元算法,以预测验证集中的疾病:乳腺癌预测:50 名妇女经活检证实患有乳腺癌(浸润性癌症 41 例,导管非浸润性癌症 9 例) 未拆分数据集:呼吸挥发性有机化合物识别乳腺癌的准确率为 83%(接收者操作特征曲线下面积),灵敏度为 82%,特异性为 77.1%:训练集呼气 VOCs 鉴定乳腺癌的准确率为 80.3%,灵敏度为 84%,特异性为 74.3%。验证集的相应准确率为 68%%,灵敏度为 72.4%,特异性为 61.5%。 预测 BIRADS 4 和 5 乳房 X 线照片(相对于 BIRADS 1、2 和 3): 未拆分数据集:呼吸 VOC 识别异常乳房 X 光照片的准确率为 76.2%:呼吸 VOCs 识别异常乳房 X 光照片的准确率为 74.2%,灵敏度为 73.3%,特异性为 60%。验证集中的相应值为:准确率 60.5%、灵敏度 64.2%、特异性 51%:呼出挥发性有机化合物生物标记物的快速床旁检测可预测有乳腺相关症状的妇女罹患乳腺癌的风险和乳房 X 光检查异常的情况。
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引用次数: 0
Volatile organic compound analysis of malignant pleural mesothelioma chorioallantoic membrane xenografts. 恶性胸膜间皮瘤绒毛膜异种移植物的挥发性有机化合物分析。
IF 3.7 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-11 DOI: 10.1088/1752-7163/ad7166
Liam D Little, Sarah E Barnett, Theo Issitt, Sam Bonsall, Vikki A Carolan, Elizabeth Allen, Laura M Cole, Neil A Cross, Judy M Coulson, Sarah L Haywood-Small

Malignant pleural mesothelioma (MPM) is an aggressive cancer associated with asbestos exposure. MPM is often diagnosed late, at a point where limited treatment options are available, but early intervention could improve the chances of successful treatment for MPM patients. Biomarkers to detect MPM in at-risk individuals are needed to implement early diagnosis technologies. Volatile organic compounds (VOCs) have previously shown diagnostic potential as biomarkers when analysed in MPM patient breath. In this study, chorioallantoic membrane (CAM) xenografts of MPM cell lines were used as models of MPM tumour development for VOC biomarker discovery with the aim of generating targets for investigation in breath, biopsies or other complex matrices. VOC headspace analysis of biphasic or epithelioid MPM CAM xenografts was performed using solid-phase microextraction and gas chromatography-mass spectrometry. We successfully demonstrated the capture, analysis and separation of VOC signatures from CAM xenografts and controls. A panel of VOCs was identified that showed discrimination between MPM xenografts generated from biphasic and epithelioid cells and CAM controls. This is the first application of the CAM xenograft model for the discovery of VOC biomarkers associated with MPM histological subtypes. These findings support the potential utility of non-invasive VOC profiling from breath or headspace analysis of tissues for detection and monitoring of MPM.

恶性胸膜间皮瘤(MPM)是一种与石棉暴露有关的侵袭性癌症。恶性胸膜间皮瘤通常诊断较晚,治疗方案有限,但早期干预可提高恶性胸膜间皮瘤患者治疗成功的几率。要实施早期诊断技术,就需要检测高危人群中 MPM 的生物标志物。在分析 MPM 患者呼气时,挥发性有机化合物 (VOC) 已显示出作为生物标记物的诊断潜力。在这项研究中,将 MPM 细胞系的绒毛膜(CAM)异种移植作为 MPM 肿瘤发展的模型,用于发现挥发性有机化合物生物标记物,目的是在呼气、活检或其他复杂基质中生成调查目标。我们采用固相微萃取和气相色谱-质谱法对双相MESO-7T、上皮样MESO-8T和MESO-12T MPM CAM异种移植物进行了挥发性有机化合物顶空分析。我们成功展示了从 CAM 异种移植物和对照组中捕获、分析和分离挥发性有机化合物特征的方法。鉴定出的一组挥发性有机化合物可区分由双相和上皮样细胞生成的 MPM 异种移植物和 CAM 对照组。这是首次应用 CAM 异种移植物模型发现与 MPM 组织学亚型相关的挥发性有机化合物生物标志物。这些发现支持了通过组织呼气或顶空分析进行非侵入性挥发性有机化合物分析以检测和监测 MPM 的潜在用途。
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引用次数: 0
Generating pooled quality control samples of volatile organic compounds. 生成挥发性有机化合物的集合质量控制样本。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-11 DOI: 10.1088/1752-7163/ad7977
Waqar Ahmed,Max Wilkinson,Stephen J Fowler
Untargeted analysis of volatile organic compounds (VOCs) from exhaled breath and culture headspace can be influenced by several confounding factors that are not reflected in reference standards. In this study, we propose a method to generating pooled quality control (QC) samples for untargeted VOC studies using a split-recollection workflow for thermal desorption tubes. Sample tubes were desorbed with a 10% split from each sample and recollected onto a single tube, generating a pooled QC sample. This QC sample was then repeatedly desorbed and recollected with a sequentially lower split ratio allowing injection of up to ten QC samples. We found pooled QC samples to be representative of complex mixtures using principal component analysis (PCA) and may be useful in future longitudinal, multi-centre, and validation studies to assess data quality and adjust for batch effects.
对呼出气体和培养顶空气中的挥发性有机化合物(VOCs)进行非靶向分析可能会受到参考标准中未反映的多种干扰因素的影响。在本研究中,我们提出了一种方法,利用热脱附试管的分割采集工作流程,为非目标挥发性有机化合物研究生成集合质控(QC)样品。样品管从每个样品中分离出 10%进行解吸,然后重新收集到单个样品管上,生成一个集合质控样品。然后重复解吸该质控样本,并以依次降低的分样率重新收集,最多可注入 10 个质控样本。通过主成分分析(PCA),我们发现汇集的质控样本能够代表复杂的混合物,在未来的纵向、多中心和验证研究中可用于评估数据质量和调整批次效应。
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引用次数: 0
Discovery and Analysis of the Relationship between Organic Components in Exhaled Breath and Bronchiectasis. 发现和分析呼出气体中的有机成分与支气管扩张症之间的关系
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-11 DOI: 10.1088/1752-7163/ad7978
Lichao Fan,Yan Chen,Yang Chen,Ling Wang,Shuo Liang,Kebin Cheng,Yue Pei,Yong Feng,Qingyun Li,Mengqi He,Ping Jiang,Haibin Chen,Jinfu Xu
The prevalence of patients with bronchiectasis (BE) has been rising in recent years, which increases the substantial burden on the family and society. Exploring a convenient, effective, and low-cost screening tool for the diagnosis of BE is urgent. We expect to identify the accuracy of breath biomarkers(BBs) for the diagnosis of BE through breathomics testing and explore the association between BBs and clinical features of BE. Method: Exhaled breath samples were collected and detected by high-pressure photon ionization time-of-flight mass spectrometry(HPPI-TOF MS) in a cross-sectional study. Exhaled breath samples were from 215 patients with BE and 295 control individuals. The potential BBs were selected via the machine learning method. The overall performance was assessed for the BBs-based BE detection model. The significant BBs between different subgroups such as the severity of BE, acute or stable stage, combined with hemoptysis or not, with or without Nontuberculous Mycobacterium (NTM), Pseudomonas aeruginosa (P.a) isolation or not, and the BBs related to the number of involved lung lobes and lung function were discovered and analyzed. Results: The top 10 BBs based machine learning model achieved an area under the curve (AUC) of 0.940, sensitivity of 90.7%, specificity of 85%, and accuracy of 87.4% in BE diagnosis. Except for the top ten BBs, other BBs were found also related to the severity, acute/stable status, hemoptysis or not, NTM infection, P.a isolation, the number of involved lobes, and three lung functional paramters in BE patients. Conclusions: BBs-based BE detection model showed good accuracy for diagnosis. BBs have a close relationship with the clinical features of BE. The breath test method may provide a new strategy for bronchiectasis screening and personalized management. Clinical Trail Number: NCT05293314 .
近年来,支气管扩张症(BE)患者的发病率不断上升,这加重了家庭和社会的沉重负担。探索一种方便、有效、低成本的支气管扩张症筛查工具迫在眉睫。我们希望通过呼吸组学检测确定呼吸生物标志物(BBs)诊断 BE 的准确性,并探讨 BBs 与 BE 临床特征之间的关联:在一项横断面研究中收集呼出气体样本,并通过高压光子电离飞行时间质谱(HPPI-TOF MS)进行检测。呼气样本来自 215 名 BE 患者和 295 名对照组个体。通过机器学习方法筛选出潜在的BB。评估了基于BBs的BE检测模型的整体性能。发现并分析了不同亚组(如 BE 的严重程度、急性期或稳定期、是否合并咯血、有无非结核分枝杆菌(NTM)、铜绿假单胞菌(P.a)分离与否)之间的重要 BBs,以及与受累肺叶数量和肺功能相关的 BBs:基于前 10 个 BBs 的机器学习模型在 BE 诊断中的曲线下面积(AUC)为 0.940,灵敏度为 90.7%,特异度为 85%,准确率为 87.4%。除排名前十的 BBs 外,其他 BBs 也与 BE 患者的严重程度、急性/稳定状态、咯血与否、NTM 感染、P.a 分离、受累肺叶数和三个肺功能参数有关:基于 BBs 的 BE 检测模型显示出良好的诊断准确性。BBs与BE的临床特征有密切关系。呼气试验方法可为支气管扩张症筛查和个性化管理提供新策略:NCT05293314 .
{"title":"Discovery and Analysis of the Relationship between Organic Components in Exhaled Breath and Bronchiectasis.","authors":"Lichao Fan,Yan Chen,Yang Chen,Ling Wang,Shuo Liang,Kebin Cheng,Yue Pei,Yong Feng,Qingyun Li,Mengqi He,Ping Jiang,Haibin Chen,Jinfu Xu","doi":"10.1088/1752-7163/ad7978","DOIUrl":"https://doi.org/10.1088/1752-7163/ad7978","url":null,"abstract":"The prevalence of patients with bronchiectasis (BE) has been rising in recent years, which increases the substantial burden on the family and society. Exploring a convenient, effective, and low-cost screening tool for the diagnosis of BE is urgent. We expect to identify the accuracy of breath biomarkers(BBs) for the diagnosis of BE through breathomics testing and explore the association between BBs and clinical features of BE.&#xD;Method: Exhaled breath samples were collected and detected by high-pressure photon ionization time-of-flight mass spectrometry(HPPI-TOF MS) in a cross-sectional study. Exhaled breath samples were from 215 patients with BE and 295 control individuals. The potential BBs were selected via the machine learning method. The overall performance was assessed for the BBs-based BE detection model. The significant BBs between different subgroups such as the severity of BE, acute or stable stage, combined with hemoptysis or not, with or without Nontuberculous Mycobacterium (NTM), Pseudomonas aeruginosa (P.a) isolation or not, and the BBs related to the number of involved lung lobes and lung function were discovered and analyzed.&#xD;Results: The top 10 BBs based machine learning model achieved an area under the curve (AUC) of 0.940, sensitivity of 90.7%, specificity of 85%, and accuracy of 87.4% in BE diagnosis. Except for the top ten BBs, other BBs were found also related to the severity, acute/stable status, hemoptysis or not, NTM infection, P.a isolation, the number of involved lobes, and three lung functional paramters in BE patients.&#xD;Conclusions: BBs-based BE detection model showed good accuracy for diagnosis. BBs have a close relationship with the clinical features of BE. The breath test method may provide a new strategy for bronchiectasis screening and personalized management.&#xD;&#xD;Clinical Trail Number: NCT05293314&#xD.","PeriodicalId":15306,"journal":{"name":"Journal of breath research","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223804","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}
引用次数: 0
Performance of empirical and model-based classifiers for detecting sucrase-isomaltase inhibition using the13C-sucrose breath test. 利用 13C 蔗糖呼气试验检测蔗糖酶-异麦芽糖酶抑制的经验分类器和基于模型的分类器的性能。
IF 3.7 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-09-10 DOI: 10.1088/1752-7163/ad748d
Hannah Van Wyk, Gwenyth O Lee, Robert J Schillinger, Christine A Edwards, Douglas J Morrison, Andrew F Brouwer

The13C-sucrose breath test (13C-SBT) has been proposed to estimate sucrase-isomaltase (SIM) activity and is a promising test for SIM deficiency, which can cause gastrointestinal symptoms, and for intestinal mucosal damage caused by gut dysfunction or chemotherapy. We previously showed how various summary measures of the13C-SBT breath curve reflect SIM inhibition. However, it is uncertain how the performance of these classifiers is affected by test duration. We leveraged13C-SBT data from a cross-over study in 16 adults who received 0, 100, and 750 mg of Reducose, an SIM inhibitor. We evaluated the performance of a pharmacokinetic-model-based classifier,ρ, and three empirical classifiers (cumulative percent dose recovered at 90 min (cPDR90), time to 50% dose recovered, and time to peak dose recovery rate), as a function of test duration using receiver operating characteristic (ROC) curves. We also assessed the sensitivity, specificity, and accuracy of consensus classifiers. Test durations of less than 2 h generally failed to accurately predict later breath curve dynamics. The cPDR90 classifier had the highest ROC area-under-the-curve and, by design, was robust to shorter test durations. For detecting mild SIM inhibition,ρhad a higher sensitivity. We recommend13C-SBT tests run for at least a 2 h duration. Although cPDR90 was the classifier with highest accuracy and robustness to test duration in this application, concerns remain about its sensitivity to misspecification of the CO2production rate. More research is needed to assess these classifiers in target populations.

背景:13C-蔗糖呼气试验(13C-SBT)已被提出来估测蔗糖酶-异麦芽糖酶(SIM)的活性,它是检测SIM缺乏症(SIM缺乏症可引起胃肠道症状)以及肠道功能障碍或化疗引起的肠粘膜损伤的一种很有前途的方法。我们以前曾展示过 13C-SBT 呼气曲线的各种汇总指标如何反映 SIM 的抑制情况。但是,这些分类器的性能如何受到测试持续时间的影响尚不确定:我们利用了一项交叉研究中的 13C-SBT 数据,研究对象是 16 名成人,他们分别服用了 0、100 和 750 毫克的 SIM 抑制剂红糖。我们利用接收器操作特征曲线(ROC)评估了基于药代动力学模型的分类器 ρ 和三种经验分类器(90 分钟内累积剂量恢复百分比(cPDR90)、剂量恢复到 50% 的时间和剂量恢复率达到峰值的时间)的性能与试验持续时间的关系。我们还评估了共识分类器的灵敏度、特异性和准确性:测试持续时间少于 2 小时通常无法准确预测后期的呼吸曲线动态。cPDR90 分类器具有最高的 ROC 曲线下面积,并且在设计上对较短的测试时间具有稳健性。对于检测轻度 SIM 抑制,ρ 的灵敏度更高:我们建议 13C-SBT 检测至少持续 2 小时。虽然 cPDR90 是本应用中准确率最高、对测试持续时间最稳健的分类器,但它对二氧化碳产生率的错误指定的敏感性仍然令人担忧。需要进行更多的研究来评估这些分类器在目标人群中的应用。
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引用次数: 0
Systematic study of polymer gas sampling bags for offline analysis of exhaled breath. 用于离线分析呼出气体的聚合物气体采样袋的系统研究
IF 3.7 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-08-13 DOI: 10.1088/1752-7163/ad6a31
Mateusz Fido, Simone Hersberger, Andreas T Güntner, Renato Zenobi, Stamatios Giannoukos

Polymeric bags are a widely applied, simple, and cost-effective method for the storage and offline analysis of gaseous samples. Various materials have been used as sampling bags, all known to contain impurities and differing in their cost, durability, and storage capabilities. Herein, we present a comparative study of several well-known bag materials, Tedlar (PVF), Kynar (PVDF), Teflon (PTFE), and Nalophan (PET), as well as a new material, ethylene vinyl copolymer (EVOH), commonly used for storing food. We investigated the influences of storage conditions, humidity, bag cleaning, and light exposure on volatile organic compound concentration (acetone, acetic acid, isoprene, benzene, limonene, among others) in samples of exhaled human breath stored in bags for up to 48 h. Specifically, we show high losses of short-chain fatty acids (SCFAs) in bags of all materials (for most SCFAs, less than 50% after 8 h of storage). We found that samples in Tedlar, Nalophan, and EVOH bags undergo changes in composition when exposed to UV radiation over a period of 48 h. We report high initial impurity levels in all the bags and their doubling after a period of 48 h. We compare secondary electrospray ionization and proton transfer reaction mass spectrometry in the context of offline analysis after storage in sampling bags. We provide an analytical perspective on the temporal evolution of bag contents by presenting the intensity changes of all significantm/zfeatures. We also present a simple, automated, and cost-effective offline sample introduction system, which enables controlled delivery of collected gaseous samples from polymeric bags into the mass spectrometer. Overall, our findings suggest that sampling bags exhibit high levels of impurities, are sensitive to several environmental factors (e.g. light exposure), and provide low recoveries for some classes of compounds, e.g. SCFAs.

聚合袋是一种应用广泛、操作简单、成本效益高的气体样本存储和离线分析方法。各种材料都被用作采样袋,它们都含有杂质,在成本、耐用性和存储能力方面也各不相同。在此,我们对几种著名的袋子材料进行了比较研究,包括 Tedlar(PVF)、Kynar(PVDF)、Teflon(PTFE)和 Nalophan(PET),以及一种常用于储存食品的新型材料乙烯-乙烯共聚物(EVOH)。我们研究了储存条件、湿度、袋子清洁和光照对储存在袋子中长达 48 小时的人体呼出气体样本中挥发性有机化合物(丙酮、乙酸、异戊二烯、苯、柠檬烯等)浓度的影响。具体来说,我们发现短链脂肪酸(SCFAs)在所有材料的袋子中都有较高的损失(对于大多数 SCFAs 来说,储存 8 小时后的损失率低于 50%)。我们发现,Tedlar、Nalophan 和 EVOH 袋中的样品在暴露于紫外线辐射 48 小时后,其成分会发生变化。我们发现所有袋子中的初始杂质含量都很高,48 小时后杂质含量会翻倍。我们比较了二级电喷雾离子化 (SESI) 和质子转移反应 (PTR) 质谱法在采样袋储存后的离线分析中的应用。我们通过展示所有重要 m/z 特征的强度变化,从分析角度展示了采样袋内容物的时间演变。我们还介绍了一种简单、自动化且经济高效的离线样品引入系统,该系统可将收集的气态样品从聚合物袋中可控地送入质谱仪。总之,我们的研究结果表明,采样袋中的杂质含量很高,对一些环境因素(如光照)很敏感,对某些类别的化合物(如短链脂肪酸)的回收率很低。
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引用次数: 0
Evaluation of tetrachloroethylene (PCE) and its degradation products in human exhaled breath and indoor air in a community setting. 评估社区环境中人体呼出气体和室内空气中的四氯乙烯 (PCE) 及其降解产物。
IF 3.7 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-08-08 DOI: 10.1088/1752-7163/ad67fd
Jung Hyun Lee, Alaina K Bryant, Marwan Alajlouni, Brandon E Boor, Antonios Tasoglou, Sa Liu

Tetrachloroethylene (PCE) is a widely utilized volatile chemical in industrial applications, including dry cleaning and metal degreasing. Exposure to PCE potentially presents a significant health risk to workers as well as communities near contamination sites. Adverse health effects arise not only from PCE, but also from PCE degradation products, such as trichloroethylene (TCE) and vinyl chloride (VC). PCE, TCE, and VC can contaminate water, soil, and air, leading to exposure through multiple pathways, including inhalation, ingestion, and dermal contact. This study focused on a community setting in Martinsville, Indiana, a working-class Midwestern community in the United States, where extensive PCE contamination has occurred due to multiple contamination sites (referring to 'plumes'), including a Superfund site. Utilizing proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS), PCE, TCE, and VC concentrations were measured in the exhaled breath of 73 residents from both within and outside the plume areas. PCE was detected in 66 samples, TCE in 26 samples, and VC in 68 samples. Our results revealed a significant positive correlation between the concentrations of these compounds in exhaled breath and indoor air (Pearson correlation coefficients: PCE = 0.75, TCE = 0.71, and VC = 0.89). This study confirms the presence of PCE and its degradation products in exhaled breath in a community exposure investigation, demonstrating the potential of using exhaled breath analysis in monitoring exposure to environmental contaminants. This study showed the feasibility of utilizing PTR-TOF-MS in community investigations to assess exposure to PCE and its degradation products by measuring these compounds in exhaled breath and indoor air.

四氯乙烯(PCE)是一种广泛应用于工业领域的挥发性化学品,包括干洗和金属脱脂。接触 PCE 可能会对工人以及污染场地附近的社区造成严重的健康风险。对健康的不利影响不仅来自于 PCE,还来自于 PCE 的降解产物,如三氯乙烯 (TCE) 和氯乙烯 (VC)。PCE、TCE 和 VC 可污染水、土壤和空气,导致通过多种途径(包括吸入、摄入和皮肤接触)接触这些物质。这项研究的重点是印第安纳州马丁斯维尔的一个社区环境,这是美国中西部的一个工人阶级社区,由于存在多个污染点(指 "羽流"),包括一个超级基金场地,该社区受到了广泛的 PCE 污染。利用质子转移反应飞行时间质谱法(PTR-TOF-MS),测量了羽流区内外 73 位居民呼出气体中的 PCE、TCE 和 VC 浓度。在 66 个样本中检测到了 PCE,在 26 个样本中检测到了 TCE,在 68 个样本中检测到了 VC。我们的研究结果表明,这些化合物在呼出气体和室内空气中的浓度之间存在明显的正相关关系(皮尔逊相关系数:PCE = 0.75,TCE = 0.71,VC = 0.89)。这项研究证实,在一项社区接触调查中,呼出的气体中含有五氯乙烯及其降解产物,这证明了利用呼出气体分析监测环境污染物接触情况的潜力。这项研究表明,在社区调查中利用 PTR-TOF-MS 测量呼出气体和室内空气中的五氯乙烯及其降解产物是可行的。
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引用次数: 0
Volatile organic compounds in exhaled breath: a promising approach for accurate differentiation of lung adenocarcinoma and squamous cell carcinoma. 呼出气体中的挥发性有机化合物:准确区分肺腺癌和鳞癌的有效方法
IF 3.7 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-08-05 DOI: 10.1088/1752-7163/ad6474
Xian Li, Lin Shi, Yijing Long, Chunyan Wang, Cheng Qian, Wenwen Li, Yonghui Tian, Yixiang Duan

Lung cancer subtyping, particularly differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC), is paramount for clinicians to develop effective treatment strategies. In this study, we aimed: (i) to discover volatile organic compound (VOC) biomarkers for precise diagnosis of ADC and SCC, (ii) to investigated the impact of risk factors on ADC and SCC prediction, and (iii) to explore the metabolic pathways of VOC biomarkers. Exhaled breath samples from patients with ADC (n= 149) and SCC (n= 94) were analyzed by gas chromatography-mass spectrometry. Both multivariate and univariate statistical analysis method were employed to identify VOC biomarkers. Support vector machine (SVM) prediction models were developed and validated based on these VOC biomarkers. The impact of risk factors on ADC and SCC prediction was investigated. A panel of 13 VOCs was found to differ significantly between ADC and SCC. Utilizing the SVM algorithm, the VOC biomarkers achieved a specificity of 90.48%, a sensitivity of 83.50%, and an area under the curve (AUC) value of 0.958 on the training set. On the validation set, these VOC biomarkers attained a predictive power of 85.71% for sensitivity and 73.08% for specificity, along with an AUC value of 0.875. Clinical risk factors exhibit certain predictive power on ADC and SCC prediction. Integrating these risk factors into the prediction model based on VOC biomarkers can enhance its predictive accuracy. This work indicates that exhaled breath holds the potential to precisely detect ADCs and SCCs. Considering clinical risk factors is essential when differentiating between these two subtypes.

肺癌亚型鉴定,尤其是腺癌(ADC)与鳞癌(SCC)的鉴别,对于临床医生制定有效的治疗策略至关重要。本研究旨在:(i) 发现用于精确诊断 ADC 和 SCC 的挥发性有机化合物生物标志物;(ii) 研究风险因素对 ADC 和 SCC 预测的影响;(iii) 探索挥发性有机化合物生物标志物的代谢途径。气相色谱-质谱法(GC-MS)分析了 ADC 患者(149 人)和 SCC 患者(94 人)的呼气样本。采用多变量和单变量统计分析方法确定挥发性有机化合物生物标志物。根据这些挥发性有机化合物生物标志物建立并验证了支持向量机(SVM)预测模型。研究了风险因素对 ADC 和 SCC 预测的影响。研究发现,13 种挥发性有机化合物在 ADC 和 SCC 之间存在显著差异。利用 SVM 算法,VOC 生物标记物在训练集上的特异性达到 90.48%,灵敏度达到 83.50%,AUC 值达到 0.958。在验证集上,这些 VOC 生物标记物的灵敏度和特异度分别达到了 85.71% 和 73.08%,AUC 值为 0.875。临床风险因素对 ADC 和 SCC 预测具有一定的预测能力。将这些风险因素整合到基于挥发性有机化合物生物标志物的预测模型中可提高其预测准确性。这项研究表明,呼出气体具有精确检测 ADC 和 SCC 的潜力。在区分这两种亚型时,考虑临床风险因素至关重要。
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引用次数: 0
Management of functional constipation-associated halitosis: a retrospective study. 功能性便秘相关口臭的治疗:一项回顾性研究。
IF 3.7 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-07-25 DOI: 10.1088/1752-7163/ad63c4
Xiao Xian Qian 钱孝先

The features of functional constipation (FC)-associated halitosis were identified in the author's previous report. In this report, the author aimed to further investigate its treatment and efficacy. This retrospective study reviewed 100 FC patients, including 82 (82%) halitosis patients and 18 (18%) non-halitosis patients. They underwent the organoleptic test (OLT) to diagnose halitosis, and the organoleptic score (OLS) (0-5) was used to evaluated halitosis severity. The Cleveland Clinical Constipation Score (CCCS) (0-30) was used to evaluate FC severity. Patients were treated with the laxative polyethylene glycol electrolyte powder (PGEP) for four weeks. These tests were performed before and after treatment. The author found that, before treatment, the CCCS was 20.00 (18.00-23.00) for all patients, 21.00 (19.00-24.00) for halitosis patients, and 18.00 (17.00-18.25) for non-halitosis patients. A significant difference was observed between halitosis patients and non-halitosis patients (P< 0.001). The OLS for halitosis patients was 3.00 (3.00-4.00). A positive correlation (r= 0.814, 95% CI: 0.732-0.872,P< 0.001) was found between OLS and CCCS. A CCCS ⩾18 predicted over 50% probability of halitosis. After treatment, the CCCS significantly decreased to 11.50 (6.00-14.75) (P< 0.001), and OLS significantly decreased to 1.00 (0.00-2.00) (P< 0.001). A positive correlation (r= 0.770, 95% CI: 0.673-0.841,P< 0.001) persisted between OLS and CCCS. A pre-treatment CCCS ⩾21 predicted over 50% probability of post-treatment halitosis, while a post-treatment CCCS ⩾12 predicted over 50% probability of post-treatment halitosis. The author concludes that the severity of FC parallels the severity of FC-associated halitosis, and can predict the probability of halitosis. Laxative treatment with PGEP is effective in improving FC-associated halitosis.

作者在之前的报告中指出了功能性便秘(FC)相关口臭的特征。在本报告中,作者旨在进一步研究其治疗和疗效。这项回顾性研究回顾了 100 例功能性便秘患者,包括 82 例(82%)口臭患者和 18 例(18%)非口臭患者。他们接受了感官测试(OLT)来诊断口臭,并用感官评分(OLS)(0-5)来评估口臭的严重程度。克利夫兰临床便秘评分(CCCS)(0-30)用于评估 FC 的严重程度。患者接受为期四周的泻药聚乙二醇电解质粉(PGEP)治疗。这些测试分别在治疗前后进行。作者发现,在治疗前,所有患者的 CCCS 为 20.00(18.00-23.00),口臭患者为 21.00(19.00-24.00),非口臭患者为 18.00(17.00-18.25)。口臭患者与非口臭患者之间存在明显差异(P < 0.001)。口臭患者的 OLS 为 3.00(3.00-4.00)。OLS 与 CCCS 之间呈正相关(r = 0.814,95% CI:0.732-0.872,P <0.001)。CCCS≥18 预测口臭的可能性超过 50%。治疗后,CCCS 显著降至 11.50(6.00-14.75)(P < 0.001),OLS 显著降至 1.00(0.00-2.00)(P < 0.001)。OLS 和 CCCS 之间持续存在正相关(r = 0.770,95% CI:0.673-0.841,P <0.001)。治疗前 CCCS≥21 预测治疗后口臭的概率超过 50%,而治疗后 CCCS≥12 预测治疗后口臭的概率超过 50%。作者的结论是,FC 的严重程度与 FC 相关口臭的严重程度相似,可以预测口臭的概率。使用 PGEP 进行通便治疗可有效改善 FC 相关性口臭。
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引用次数: 0
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Journal of breath research
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