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Exhaled nitric oxide in intubated ICU patients on mechanical ventilation-a feasibility study. ICU机械通气插管患者呼出一氧化氮的可行性研究。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-09-25 DOI: 10.1088/1752-7163/acf607
Andreas Kofoed, Mathias Hindborg, Jeppe Hjembæk-Brandt, Christian Dalby Sørensen, Mette Kolpen, Morten H Bestle

It can be a clinical challenge to distinguish inflammation from infection in critically ill patients. Therefore, valid and conclusive surrogate markers for infections are desired. Nitric oxide (NO) might be that marker since concentrations of exhaled NO have shown to change in the presence of various diseases. This observational, prospective, single-center feasibility study aimed to investigate if fractional exhaled NO (FeNO) can be measured in intubated patients with or without infection, pneumonia and septic shock in a standardized, reliable setting. 20 intubated patients in the intensive care unit (ICU) were included for analysis. FeNO mean values were measured in the endotracheal tube via the suction channel using a chemiluminescence based analyzer. We developed a pragmatic method to measure FeNO repeatedly and reliably in intubated patients using a chemiluminescence based analyzer. We found a median of 0.98 (0.59-1.44) FeNO mean (ppb) in exhaled breath from all 20 intubated patient. Intubated patient with suspected infection had a significantly lower median FeNO mean compared with the intubated patients without suspected infection. Similarly did patients with septic shock demonstrate a significantly lower median FeNO mean than without septic shock. We found no statistical difference in median FeNO mean for intubated patients with pneumonia. It was feasible to measure FeNO in intubated patients in the ICU. Our results indicate decreased levels of FeNO in infected intubated patients in the ICU. The study was not powered to provide firm conclusions, so larger trials are needed to confirm the results and to prove FeNO as a useful biomarker for distinguishment between infection and inflammation in the ICU.

区分危重患者的炎症和感染可能是一项临床挑战。因此,需要有效和决定性的替代感染标志物。一氧化氮(NO)可能是该标志物,因为呼出的NO浓度已显示出在各种疾病的存在下会发生变化。这项观察性、前瞻性、单中心的可行性研究旨在调查是否可以在标准化、可靠的环境中测量有或没有感染、肺炎和感染性休克的插管患者的呼出NO(FeNO)分数。纳入20名重症监护室(ICU)插管患者进行分析。使用基于化学发光的分析仪通过抽吸通道在气管插管中测量FeNO平均值。我们开发了一种实用的方法,使用基于化学发光的分析仪在插管患者中重复可靠地测量FeNO。我们发现,所有20名插管患者呼出的FeNO平均值(ppb)的中位数为0.98(0.59-1.44)。与未经疑似感染的插管患者相比,经疑似感染插管患者的FeNO平均中位数显著较低。同样,感染性休克患者的FeNO中位数明显低于无感染性休克的患者。我们发现插管的肺炎患者的FeNO中位数没有统计学差异。在ICU中测量插管患者的FeNO是可行的。我们的研究结果表明,ICU中受感染插管患者的FeNO水平降低。这项研究无法提供确切的结论,因此需要更大规模的试验来证实结果,并证明FeNO是区分重症监护室感染和炎症的有用生物标志物。
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引用次数: 0
Determining the clinical utility of a breath test for screening an asbestos-exposed population for pleural mesothelioma: baseline results. 确定呼吸测试筛查石棉暴露人群胸膜间皮瘤的临床效用:基线结果。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-09-21 DOI: 10.1088/1752-7163/acf7e3
Kathleen Zwijsen, Eline Schillebeeckx, Eline Janssens, Joris Van Cleemput, Tom Richart, Veerle F Surmont, Kristiaan Nackaerts, Elly Marcq, Jan P van Meerbeeck, Kevin Lamote

Pleural mesothelioma (PM) is an aggressive cancer of the serosal lining of the thoracic cavity, predominantly caused by asbestos exposure. Due to nonspecific symptoms, PM is characterized by an advanced-stage diagnosis, resulting in a dismal prognosis. However, early diagnosis improves patient outcome. Currently, no diagnostic biomarkers or screening tools are available. Therefore, exhaled breath was explored as this can easily be obtained and contains volatile organic compounds, which are considered biomarkers for multiple (patho)physiological processes. A breath test, which differentiates asbestos-exposed (AEx) individuals from PM patients with 87% accuracy, was developed. However, before being implemented as a screening tool, the clinical utility of the test must be determined. Occupational AEx individuals underwent annual breath tests using multicapillary column/ion mobility spectrometry. A baseline breath test was taken and their individual risk of PM was estimated. PM patients were included as controls. In total, 112 AEx individuals and six PM patients were included in the first of four screening rounds. All six PM patients were correctly classified as having mesothelioma (100% sensitivity) and out of 112 AEx individuals 78 were classified by the breath-based model as PM patients (30% specificity). Given the large false positive outcome, the breath test will be repeated annually for three more consecutive years to adhere to the 'test, re-test' principle and improve the false positivity rate. A low-dose computed tomography scan in those with two consecutive positive tests will correlate test positives with radiological findings and the possible growth of a pleural tumor. Finally, the evaluation of the clinical value of a breath-based prediction model may lead to the initiation of a screening program for early detection of PM in Aex individuals, which is currently lacking. This clinical study received approval from the Antwerp University Hospital Ethics Committee (B300201837007).

胸膜间皮瘤(PM)是一种侵袭性的胸腔浆膜癌症,主要由石棉暴露引起。由于非特异性症状,PM以晚期诊断为特征,导致预后不佳。然而,早期诊断可以改善患者的预后。目前,没有可用的诊断生物标志物或筛查工具。因此,对呼出气体进行了探索,因为它可以很容易地获得,并且含有挥发性有机化合物,这些化合物被认为是多种(病理)生理过程的生物标志物。开发了一种呼吸测试,将接触石棉(AEx)的个体与PM患者区分开来,准确率为87%。然而,在作为筛查工具实施之前,必须确定该测试的临床实用性。使用多毛细管柱/离子迁移率光谱法对职业性AEx个体进行年度呼吸测试。进行了基线呼吸测试,并估计了他们患PM的个人风险。纳入PM患者作为对照。总共有112名AEx患者和6名PM患者参加了四轮筛查中的第一轮。所有6名PM患者均被正确归类为间皮瘤(100%敏感性),在112名AEx患者中,78人被基于呼吸的模型归类为PM患者(30%特异性)。鉴于假阳性结果较大,呼吸测试将每年重复一次,连续三年,以遵守“测试,重新测试”原则,提高假阳性率。对连续两次检测呈阳性的患者进行低剂量计算机断层扫描,将检测呈阳性与放射学检查结果和胸膜肿瘤的可能生长相关。最后,对基于呼吸的预测模型的临床价值的评估可能会导致启动一项筛查计划,用于在Aex个体中早期检测PM,这是目前缺乏的。这项临床研究获得了安特卫普大学医院伦理委员会的批准(B300201837007)。
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引用次数: 0
Prediction of systemic free and total valproic acid by off-line analysis of exhaled breath in epileptic children and adolescents. 通过对癫痫儿童和青少年呼出气体的离线分析预测全身游离丙戊酸和总丙戊酸。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-09-19 DOI: 10.1088/1752-7163/acf782
Mo Awchi, Kapil Dev Singh, Patricia E Dill, Urs Frey, Alexandre N Datta, Pablo Sinues

Therapeutic drug monitoring (TDM) of medications with a narrow therapeutic window is a common clinical practice to minimize toxic effects and maximize clinical outcomes. Routine analyses rely on the quantification of systemic blood concentrations of drugs. Alternative matrices such as exhaled breath are appealing because of their inherent non-invasive nature. This is especially the case for pediatric patients. We have recently showcased the possibility of predicting systemic concentrations of valproic acid (VPA), an anti-seizure medication by real-time breath analysis in two real clinical settings. This approach, however, comes with the limitation of the patients having to physically exhale into the mass spectrometer. This restricts the possibility of sampling from patients not capable or available to exhale into the mass spectrometer located on the hospital premises. In this work, we developed an alternative method to overcome this limitation by collecting the breath samples in customized bags and subsequently analyzing them by secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). A total ofn= 40 patients (mean ± SD, 11.5 ± 3.5 y.o.) diagnosed with epilepsy and taking VPA were included in this study. The patients underwent three measurements: (i) serum concentrations of total and free VPA, (ii) real-time breath analysis and (iii) off-line analysis of exhaled breath collected in bags. The agreement between the real-time and the off-line breath analysis methods was evaluated using Lin's concordance correlation coefficient (CCC). CCC was computed for ten mass spectral predictors of VPA concentrations. Lin's CCC was >0.6 for all VPA-associated features, except for two low-signal intensity isotopic peaks. Finally, free and total serum VPA concentrations were predicted by cross validating the off-line data set. Support vector machine algorithms provided the most accurate predictions with a root mean square error of cross validation of 29.0 ± 7.4 mg l-1and 3.9 ± 1.4 mg l-1for total and free VPA (mean ± SD), respectively. As a secondary analysis, we explored whether exhaled metabolites previously associated with side-effects and response to medication could be rendered by the off-line analysis method. We found that five features associated with side effects showed a CCC > 0.6, whereas none of the drug response-associated peaks reached this cut-off. We conclude that the clinically relevant free fraction of VPA can be predicted by this combination of off-line breath collection with rapid SESI-HRMS analysis. This opens new possibilities for breath based TDM in clinical settings.

在狭窄的治疗窗口内对药物进行治疗药物监测(TDM)是一种常见的临床实践,可以最大限度地减少毒性影响并最大限度地提高临床结果。常规分析依赖于药物全身血液浓度的定量。呼气等替代基质因其固有的非侵入性而具有吸引力。儿科患者尤其如此。我们最近展示了在两种真实的临床环境中通过实时呼吸分析预测丙戊酸(VPA)全身浓度的可能性,丙戊酸是一种抗癫痫药物。然而,这种方法的局限性在于患者必须向质谱仪呼气。这限制了从无法或无法呼气的患者身上采样到位于医院内的质谱仪中的可能性。在这项工作中,我们开发了一种替代方法来克服这一限制,方法是将呼吸样本收集在定制的袋子中,然后通过二次电喷雾电离结合高分辨率质谱(SESI-HRMS)进行分析。本研究共纳入了40名被诊断为癫痫并服用VPA的患者(平均值±SD,11.5±3.5 y.o.)。患者接受了三项测量:(i)总VPA和游离VPA的血清浓度,(ii)实时呼吸分析和(iii)收集在袋中的呼出气体的离线分析。使用林一致性相关系数(CCC)评估实时和离线呼吸分析方法之间的一致性。计算了10个VPA浓度质谱预测因子的CCC。除两个低信号强度同位素峰外,所有VPA相关特征的林CCC均>0.6。最后,通过交叉验证离线数据集来预测游离和总血清VPA浓度。支持向量机算法提供了最准确的预测,总VPA和游离VPA的交叉验证均方根误差分别为29.0±7.4 mg l-1和3.9±1.4 mg l-1(平均值±SD)。作为二次分析,我们探讨了以前与副作用和药物反应相关的呼出代谢物是否可以通过离线分析方法呈现。我们发现,与副作用相关的五个特征显示CCC>0.6,而与药物反应相关的峰值均未达到该临界值。我们的结论是,通过离线呼吸采集和快速SESI-HRMS分析的结合,可以预测临床相关的VPA游离分数。这为基于呼吸的TDM在临床环境中开辟了新的可能性。
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引用次数: 0
Effect of ambient temperature and respiration rate on nasal dominance: preliminary findings from a nostril-specific wearable. 环境温度和呼吸速率对鼻腔优势的影响:鼻孔专用可穿戴设备的初步发现。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-09-07 DOI: 10.1088/1752-7163/acf339
Amit Kumar, Deepak Joshi

The nasal dominance (ND) determination is crucial for nasal synchronized ventilator, optimum nasal drug delivery, identifying brain hemispheric dominance, nasal airway obstruction surgery, mindfulness breathing, and for possible markers of a conscious state. Given these wider applications of ND, it is interesting to understand the patterns of ND with varying temperature and respiration rates. In this paper, we propose a method which measures peak-to-peak temperature oscillations (difference between end-expiratory and end-inspiratory temperature) for the left and right nostrils during nasal breathing. These nostril-specific temperature oscillations are further used to calculate the nasal dominance index (NDI), nasal laterality ratio (NLR), inter-nostril correlation, and mean of peak-to-peak temperature oscillation for inspiratory and expiratory phase at (1) different ambient temperatures of 18 °C, 28 °C, and 38 °C and (2) at three different respiration rate of 6 bpm, 12 bpm, and 18 bpm. The peak-to-peak temperature (Tpp) oscillation range (averaged across participants;n= 8) for the left and right nostril were 3.80 ± 0.57 °C and 2.34 ± 0.61 °C, 2.03 ± 0.20 °C and 1.40 ± 0.26 °C, and 0.20 ± 0.02 °C and 0.29 ± 0.03 °C at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively (averaged across participants and respiration rates). The NDI and NLR averaged across participants and three different respiration rates were 35.67 ± 5.53 and 2.03 ± 1.12; 8.36 ± 10.61 and 2.49 ± 3.69; and -25.04 ± 14.50 and 0.82 ± 0.54 at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively. The Shapiro-Wilk test, and non-parametric Friedman test showed a significant effect of ambient temperature conditions on both NDI and NLR. No significant effect of respiration rate condition was observed on both NDI and NLR. The findings of the proposed study indicate the importance of ambient temperature while determining ND during the diagnosis of breathing disorders such as septum deviation, nasal polyps, nosebleeds, rhinitis, and nasal fractions, and in the intensive care unit for nasal synchronized ventilator.

鼻优势(ND)的确定对于鼻同步呼吸机、最佳鼻腔给药、识别大脑半球优势、鼻气道阻塞手术、正念呼吸以及意识状态的可能标记至关重要。鉴于ND的这些更广泛的应用,了解ND随温度和呼吸速率变化的模式是很有趣的。在本文中,我们提出了一种测量鼻呼吸过程中左右鼻孔峰间温度振荡(呼气末和吸气末温度之差)的方法。这些鼻孔特异性温度振荡进一步用于计算鼻腔优势指数(NDI)、鼻偏侧比(NLR)、鼻孔间相关性以及吸气期和呼气期在(1)不同环境温度18°C、28°C和38°C和(2)6 bpm、12 bpm和18 bpm三种不同呼吸速率下的峰间温度振荡平均值。在环境温度为18°C、28°C和38°C时,左鼻孔和右鼻孔的峰对峰温度振荡范围(n= 8)分别为3.80±0.57°C和2.34±0.61°C, 2.03±0.20°C和1.40±0.26°C,以及0.20±0.02°C和0.29±0.03°C(参与者和呼吸速率的平均值)。三种不同呼吸速率下的NDI和NLR平均值分别为35.67±5.53和2.03±1.12;8.36±10.61和2.49±3.69;在环境温度为18℃、28℃和38℃时,分别为-25.04±14.50和- 0.82±0.54。夏皮罗-威尔克检验和非参数弗里德曼检验表明,环境温度条件对NDI和NLR均有显著影响。呼吸速率条件对NDI和NLR均无显著影响。本研究的结果表明,在诊断呼吸障碍(如鼻中隔偏曲、鼻息肉、流鼻血、鼻炎和鼻分离)以及重症监护病房使用鼻同步呼吸机时,环境温度在确定ND时的重要性。
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引用次数: 0
Changes in lung epithelial cell volatile metabolite profile induced by pro-fibrotic stimulation with TGF-β1. TGF-β1促纤维化刺激对肺上皮细胞挥发性代谢物谱的影响
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-09-07 DOI: 10.1088/1752-7163/acf391
Conal Hayton, Waqar Ahmed, Peter Cunningham, Karen Piper-Hanley, Laurence Pearmain, Nazia Chaudhuri, Colm Leonard, John F Blaikley, Stephen J Fowler

Volatile organic compounds (VOCs) have shown promise as potential biomarkers in idiopathic pulmonary fibrosis. Measuring VOCs in the headspace ofin vitromodels of lung fibrosis may offer a method of determining the origin of those detected in exhaled breath. The aim of this study was to determine the VOCs associated with two lung cell lines (A549 and MRC-5 cells) and changes associated with stimulation of cells with the pro-fibrotic cytokine, transforming growth factor (TGF)-β1. A dynamic headspace sampling method was used to sample the headspace of A549 cells and MRC-5 cells. These were compared to media control samples and to each other to identify VOCs which discriminated between cell lines. Cells were then stimulated with the TGF-β1 and samples were compared between stimulated and unstimulated cells. Samples were analysed using thermal desorption-gas chromatography-mass spectrometry and supervised analysis was performed using sparse partial least squares-discriminant analysis (sPLS-DA). Supervised analysis revealed differential VOC profiles unique to each of the cell lines and from the media control samples. Significant changes in VOC profiles were induced by stimulation of cell lines with TGF-β1. In particular, several terpenoids (isopinocarveol, sativene and 3-carene) were increased in stimulated cells compared to unstimulated cells. VOC profiles differ between lung cell lines and alter in response to pro-fibrotic stimulation. Increased abundance of terpenoids in the headspace of stimulated cells may reflect TGF-β1 cell signalling activity and metabolic reprogramming. This may offer a potential biomarker target in exhaled breath in IPF.

挥发性有机化合物(VOCs)作为特发性肺纤维化的潜在生物标志物已显示出前景。在肺纤维化的体外模型中测量挥发性有机化合物的顶空可能提供一种确定呼出气体中检测到的挥发性有机化合物来源的方法。本研究的目的是确定两种肺细胞系(A549和MRC-5细胞)的VOCs相关以及促纤维化细胞因子转化生长因子(TGF)-β1刺激细胞的相关变化。采用动态顶空取样法对A549细胞和MRC-5细胞顶空取样。将这些样品与培养基对照样品进行比较,并相互比较,以确定区分细胞系的挥发性有机化合物。然后用TGF-β1刺激细胞,比较刺激细胞和未刺激细胞的样品。样品采用热解吸-气相色谱-质谱分析,监督分析采用稀疏偏最小二乘判别分析(sPLS-DA)。监督分析揭示了不同细胞系和培养基对照样品中独特的VOC特征。TGF-β1刺激细胞株后,VOC谱发生显著变化。特别是,与未受刺激的细胞相比,受刺激的细胞中几种萜类化合物(异皂荚醇、皂荚烯和3-蒈烯)增加。挥发性有机化合物谱在肺细胞系之间不同,并在促纤维化刺激下改变。受刺激细胞顶空中萜类化合物丰度的增加可能反映了TGF-β1细胞信号转导活性和代谢重编程。这可能为IPF呼出气体提供一个潜在的生物标志物靶点。
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引用次数: 0
Diagnostic accuracy of eNose 'breathprints' for therapeutic drug monitoring of Tacrolimus trough levels in lung transplantation. eNose“呼吸指纹”在肺移植中监测他克莫司谷水平的诊断准确性。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-09-05 DOI: 10.1088/1752-7163/acf066
Nynke Wijbenga, Marjolein M Muller, Rogier A S Hoek, Bas J Mathot, Leonard Seghers, Joachim G J V Aerts, Brenda C M de Winter, Daniel Bos, Olivier C Manintveld, Merel E Hellemons

In order to prevent long-term immunity-related complications after lung transplantation, close monitoring of immunosuppressant levels using therapeutic drug monitoring (TDM) is paramount. Novel electronic nose (eNose) technology may be a non-invasive alternative to the current invasive procedures for TDM. We investigated the diagnostic and categorization capacity of eNose breathprints for Tacrolimus trough blood plasma levels (TACtrough) in lung transplant recipients (LTRs). We performed eNose measurements in stable LTR attending the outpatient clinic. We evaluated (1) the correlation between eNose measurements and TACtrough, (2) the diagnostic capacity of eNose technology for TACtrough, and (3) the accuracy of eNose technology for categorization of TACtroughinto three clinically relevant categories (low: <7µg ml-1, medium: 7-10µg ml-1, and high: >10µg ml-1). A total of 186 measurements from 86 LTR were included. There was a weak but statistically significant correlation (r= 0.21,p= 0.004) between the eNose measurements and TACtrough. The root mean squared error of prediction for the diagnostic capacity was 3.186 in the training and 3.131 in the validation set. The accuracy of categorization ranged between 45%-63% for the training set and 52%-69% in the validation set. There is a weak correlation between eNose breathprints and TACtroughin LTR. However, the diagnostic as well as categorization capacity for TACtroughusing eNose breathprints is too inaccurate to be applicable in TDM.

为了预防肺移植术后长期的免疫相关并发症,使用治疗性药物监测(TDM)密切监测免疫抑制剂水平至关重要。新型电子鼻(eNose)技术可能是目前创伤性TDM手术的非侵入性替代方案。我们研究了肺移植受者(LTRs)呼气指纹通过血浆水平(TACtrough)对他克莫司的诊断和分类能力。我们对在门诊就诊的稳定LTR患者进行了eNose测量。我们评估了(1)eNose测量值与tac槽之间的相关性,(2)eNose技术对tac槽的诊断能力,以及(3)eNose技术将tac槽分为三个临床相关类别的准确性(低:µg ml-1,中:7-10µg ml-1,高:>10µg ml-1)。共纳入86个LTR的186个测量值。eNose测量值与tacthrough之间存在微弱但有统计学意义的相关性(r= 0.21,p= 0.004)。训练集预测诊断能力的均方根误差为3.186,验证集预测诊断能力的均方根误差为3.131。训练集的分类准确率为45%-63%,验证集的分类准确率为52%-69%。eNose呼气指纹与TACtroughin LTR之间的相关性较弱,然而,TACtroughin呼气指纹的诊断和分类能力太不准确,无法应用于TDM。
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引用次数: 0
Alveolar gradients in breath analysis. A pilot study with comparison of room air and inhaled air by simultaneous measurements using ion mobility spectrometry. 呼吸分析中的肺泡梯度。通过离子迁移率光谱法同时测量室内空气和吸入空气的比较初步研究。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-09-04 DOI: 10.1088/1752-7163/acf338
M Westhoff, M Keßler, J I Baumbach

Analyzing exhaled breath samples, especially using a highly sensitive method such as MCC/IMS (multi-capillary column/ion mobility spectrometry), may also detect analytes that are derived from exogenous production. In this regard, there is a discussion about the optimal interpretation of exhaled breath, either by considering volatile organic compounds (VOCs) only in exhaled breath or by additionally considering the composition of room air and calculating the alveolar gradients. However, there are no data on whether the composition and concentration of VOCs in room air are identical to those in truly inhaled air directly before analyzing the exhaled breath. The current study aimed to determine whether the VOCs in room air, which are usually used for the calculation of alveolar gradients, are identical to the VOCs in truly inhaled air. For the measurement of inhaled air and room air, two IMS, each coupled with an MCC that provided a pre-separation of the VOCs, were used in parallel. One device was used for sampling room air and the other for sampling inhaled air. Each device was coupled with a newly invented system that cleaned room air and provided a clean carrier gas, whereas formerly synthetic air had to be used as a carrier gas. In this pilot study, a healthy volunteer underwent three subsequent runs of sampling of inhaled air and simultaneous sampling and analysis of room air. Three of the selected 11 peaks (P4-unknown, P5-1-Butanol, and P9-Furan, 2-methyl-) had significantly higher intensities during inspiration than in room air, and four peaks (P1-1-Propanamine, N-(phenylmethylene), P2-2-Nonanone, P3-Benzene, 1,2,4-trimethyl-, and P11-Acetyl valeryl) had higher intensities in room air. Furthermore, four peaks (P6-Benzaldehyde, P7-Pentane, 2-methyl-, P8-Acetone, and P10-2-Propanamine) showed inconsistent differences in peak intensities between inhaled air and room air. To the best of our knowledge, this is the first study to compare simultaneous sampling of room air and inhaled air using MCC/IMS. The simultaneous measurement of inhaled air and room air showed that using room air for the calculation of alveolar gradients in breath analysis resulted in different alveolar gradient values than those obtained by measuring truly inhaled air.

分析呼出气体样本,特别是使用高灵敏度的方法,如MCC/IMS(多毛细管柱/离子迁移谱法),也可以检测到来自外源性生产的分析物。在这方面,有关于呼气的最佳解释的讨论,要么只考虑呼出气体中的挥发性有机化合物(VOCs),要么额外考虑室内空气的组成并计算肺泡梯度。然而,在分析呼出气体之前,没有数据表明室内空气中挥发性有机化合物的成分和浓度与直接吸入的空气中的成分和浓度是否相同。目前的研究旨在确定通常用于计算肺泡梯度的室内空气中的VOCs是否与实际吸入空气中的VOCs相同。为了测量吸入空气和室内空气,平行使用两个IMS,每个IMS都与MCC相结合,提供VOCs的预分离。一个装置用于采样室内空气,另一个装置用于采样吸入空气。每个设备都与一个新发明的系统相结合,该系统可以净化房间空气并提供清洁的载气,而以前必须使用合成空气作为载气。在这项初步研究中,一名健康的志愿者随后进行了三次吸入空气采样和同时对室内空气采样和分析。在所选的11个峰中,有3个峰(p4 -未知、p5 -1-丁醇和p9 -呋喃、2-甲基)在吸入时的强度显著高于室内空气,而4个峰(p1 -1-丙胺、N-(苯基亚甲基)、p2 -2-壬壬酮、p3 -苯、1,2,4-三甲基和p11 -乙酰戊酰)在室内空气中的强度更高。此外,四个峰(p6 -苯甲醛、p7 -戊烷、2-甲基、p8 -丙酮和p10 -2-丙胺)在吸入空气和室内空气之间的峰强度差异不一致。据我们所知,这是第一个比较使用MCC/IMS同时采样室内空气和吸入空气的研究。同时测量吸入空气和室内空气表明,在呼吸分析中使用室内空气计算肺泡梯度与测量真实吸入空气得到的肺泡梯度值不同。
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引用次数: 0
A low-cost internal standard loader for solid-phase sorbing tools. 用于固相吸附工具的低成本内部标准装载机。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-08-29 DOI: 10.1088/1752-7163/acef4b
F M Vivaldi, S Reale, S Ghimenti, D Biagini, A Lenzi, T Lomonaco, F Di Francesco

Solid-phase sorption is widely used for the analysis of gaseous specimens as it allows at the same time to preconcentrate target analytes and store samples for relatively long periods. The addition of internal standards (ISs) in the analytical workflow can greatly reduce the variability of the analyses and improve the reliability of the protocols. In this work, we describe the development and testing of a portable system for the reliable production of gaseous mixture of8D-Toluene in a 1L Silonite canister as well as its reproducible loading into solid-phase sorbing tools as ISs. The portable system was tested using needle trap microextraction, solid-phase extraction, and thin-film microextraction techniques commonly employed for the analysis of gaseous samples. Even though our specific interest is in breath analysis, the system can also be used for the collection of any kind of gaseous specimen. A microcontroller allows the fine control of the sampling flow by a digital mass flow controller. Flow rate and sample volume could be set either through a rotary encoder mounted onto the control board or through a dedicated android app. The variability of the airflow is in the range 5-200 ml min-1and it is lower than 1%, whereas the variability of the IS (8D-Toluene) concentration dispensed over time by the loader measured by selected-ion flow-tube mass spectrometry (MS) is <3%. This combination resulted in intra- and inter-day precision of the amount loaded in the sorbent tools lower than 15%. No carry-over was detected in the loader after the delivery of the8D-Toluene measured by gas chromatography-MS. The8D-Toluene concentration in the canister was stable for up to three weeks at room temperature.

固相吸附法广泛用于气体样品的分析,因为它同时允许对目标分析物进行预浓缩,并将样品储存相对较长的时间。在分析工作流程中加入内部标准(ISs)可以大大减少分析的可变性,提高协议的可靠性。在这项工作中,我们描述了一种便携式系统的开发和测试,该系统用于在1L硅土罐中可靠地生产8d -甲苯的气体混合物,并将其可重复加载到固相吸附工具中作为ISs。便携式系统使用针阱微萃取、固相萃取和薄膜微萃取技术进行了测试,这些技术通常用于分析气体样品。尽管我们对呼气分析感兴趣,但该系统也可用于收集任何种类的气体样本。微控制器允许通过数字质量流量控制器对采样流量进行精细控制。流速和样本量可以通过安装在控制板上的旋转编编器或通过专用的android应用程序进行设置。气流的变异性在5-200 ml min-1范围内,低于1%,而通过选择离子流管质谱(MS)测量的装载机分配的is (8d -甲苯)浓度随时间的变异性是通过气相色谱-MS测量的8d -甲苯。在室温下,罐内的8d -甲苯浓度可稳定达三周。
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引用次数: 0
Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis. 利用电子鼻数据评估不同分类方法诊断结节病。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-08-29 DOI: 10.1088/1752-7163/acf1bf
Iris G van der Sar, Nynke van Jaarsveld, Imme A Spiekerman, Floor J Toxopeus, Quint L Langens, Marlies S Wijsenbeek, Justin Dauwels, Catharina C Moor

Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patterns can be used to diagnose medical conditions. Sarcoidosis is an often difficult to diagnose disease, as no standard procedure or conclusive test exists. An accurate diagnostic model based on eNose data could therefore be helpful in clinical decision-making. The aim of this paper is to evaluate the performance of various dimensionality reduction methods and classifiers in order to design an accurate diagnostic model for sarcoidosis. Various methods of dimensionality reduction and multiple hyperparameter optimised classifiers were tested and cross-validated on a dataset of patients with pulmonary sarcoidosis (n= 224) and other interstitial lung disease (n= 317). Best performing methods were selected to create a model to diagnose patients with sarcoidosis. Nested cross-validation was applied to calculate the overall diagnostic performance. A classification model with feature selection and random forest (RF) classifier showed the highest accuracy. The overall diagnostic performance resulted in an accuracy of 87.1% and area-under-the-curve of 91.2%. After comparing different dimensionality reduction methods and classifiers, a highly accurate model to diagnose a patient with sarcoidosis using eNose data was created. The RF classifier and feature selection showed the best performance. The presented systematic approach could also be applied to other eNose datasets to compare methods and select the optimal diagnostic model.

电子鼻技术是一种新兴的诊断应用,利用人工智能对人类呼吸模式进行分类。这些模式可用于诊断医疗状况。结节病通常是一种难以诊断的疾病,因为没有标准的程序或决定性的测试。因此,基于eNose数据的准确诊断模型可能有助于临床决策。本文的目的是评估各种降维方法和分类器的性能,以便设计一个准确的结节病诊断模型。在肺结节病(n=224)和其他间质性肺病(n=317)患者的数据集上测试并交叉验证了各种降维方法和多个超参数优化分类器。选择表现最佳的方法来创建诊断结节病患者的模型。应用嵌套交叉验证来计算整体诊断性能。具有特征选择和随机森林(RF)分类器的分类模型显示出最高的准确度。总体诊断性能的准确率为87.1%,曲线下面积为91.2%。在比较了不同的降维方法和分类器后,创建了一个使用eNose数据诊断结节病患者的高准确度模型。RF分类器和特征选择显示出最佳的性能。所提出的系统方法也可以应用于其他eNose数据集,以比较方法并选择最佳诊断模型。
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引用次数: 1
Artificial intelligence can dynamically adjust strategies for auxiliary diagnosing respiratory diseases and analyzing potential pathological relationships. 人工智能可以动态调整策略,辅助诊断呼吸系统疾病,分析潜在的病理关系。
IF 3.8 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS Pub Date : 2023-08-25 DOI: 10.1088/1752-7163/acf065
Quan Zhang, Binyue Chen, Guohua Liu

Respiratory diseases are one of the leading causes of human death and exacerbate the global burden of non-communicable diseases. Finding a method to assist clinicians pre-diagnose these diseases is an urgent task. Existing artificial intelligence-based methods can improve the clinical diagnosis efficiency, but still face challenges. For example, the lack of interpretability, the problem of information redundancy or missing caused by only using static data, the difficulty of model to learn the interdependence between features, and the performance of model is limited by sparse datasets, etc. To alleviate these problems, we propose a novel RQPA-Net. It consists of Q&A diagnosis module (QAD) and pathological inference module (PI). The QAD is responsible for interacting with patients, adjusting inquiry strategies dynamically and collecting effective information for disease diagnosis. The designed multi-subspace network can alleviate the problem that classical method is difficult to understand the interdependence between features. The deep reinforcement learning designed also can alleviate the problem of classical methods lack of interpretability. The PI is responsible for reasoning potential pathological relationships between diseases or symptoms based on existing knowledge. Through integrating the advantages of deep learning and reinforcement learning techniques, PI can handle sparse datasets. Finally, for auxiliary diagnosis, the model achieves 0.9780 ± 0.0002 Recall, 0.9778 ± 0.0003 Acc, 0.9779 ± 0.0003 Precision and 0.9780 ± 0.0003 F1-score on the test set. In terms of assisting pathological analysis, compared with the end-to-end model, our model achieves higher comprehensive performance on different tasks and datasets with different degrees of sparsity. Even in sparse datasets, it can effectively infer potential associations between diseases or symptoms, and has higher potential clinical application. In this paper, we propose a novel network structure, which can not only assist doctors in diagnosing diseases, but also contribute to explore the potential disease mechanisms. It provides a new perspective for integrating AI technology and clinical practice.

呼吸系统疾病是人类死亡的主要原因之一,并加剧了全球非传染性疾病的负担。寻找一种方法来帮助临床医生预先诊断这些疾病是一项紧迫的任务。现有的基于人工智能的方法可以提高临床诊断效率,但仍面临挑战。例如,缺乏可解释性,仅使用静态数据导致的信息冗余或缺失问题,模型难以学习特征之间的相互依赖关系,模型的性能受到稀疏数据集的限制等。为了解决这些问题,我们提出了一种新的RQPA-Net。它由问答诊断模块(QAD)和病理推理模块(PI)组成。QAD负责与患者互动,动态调整问诊策略,收集疾病诊断的有效信息。所设计的多子空间网络可以缓解传统方法难以理解特征间相互依赖关系的问题。所设计的深度强化学习还可以缓解经典方法缺乏可解释性的问题。PI负责根据现有知识推理疾病或症状之间潜在的病理关系。通过融合深度学习和强化学习技术的优点,PI可以处理稀疏数据集。最后,对于辅助诊断,该模型在测试集上达到了0.9780±0.0002 Recall, 0.9778±0.0003 Acc, 0.9779±0.0003 Precision和0.9780±0.0003 f1得分。在辅助病理分析方面,与端到端模型相比,我们的模型在不同任务和不同稀疏度的数据集上取得了更高的综合性能。即使在稀疏数据集中,也能有效推断疾病或症状之间的潜在关联,具有较高的临床应用潜力。在本文中,我们提出了一种新的网络结构,它不仅可以帮助医生诊断疾病,而且有助于探索潜在的疾病机制。为人工智能技术与临床实践的结合提供了新的视角。
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引用次数: 0
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Journal of breath research
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