Breathing gas carries important physiological information. Technology for detection of breathing gas has become a research focus because of the advantages of nondestructive sampling and convenient operation. Proton transfer reaction mass spectrometry (PTR-MS) plays an irreplaceable role because of the advantages of high sensitivity, fast response and good specificity. In this paper, the principle of PTR-MS is introduced first, followed by research progress of PTR-MS in the field of breathing gas detection. Factors influencing the test results are analyzed. Finally, future prospects of development for PTR-MS in the field of breathing gas detection are discussed.
ObjectiveTo investigate the characteristic volatile organic compounds (VOCs) in exhaled breath and their diagnostic value in patients with early stage lung cancer.MethodsSolid-phase micro-extraction combined with gas chromatography mass spectrometry was used to analyze exhaled breath VOCs of 117 patients with early stage lung cancer (54 males and 63 females, with an average age of 61.9±6.8 years) and 130 healthy subjects (79 males and 51 females, with an average age of 63.3±6.6 years. The characteristic VOCs of early stage lung cancer were identified, and a diagnostic model was established.ResultsTen characteristic VOCs of early stage lung cancer were identified, including acetic acid, n-butanol, dimethylsilanol, toluene, 2,3,4-trimethylheptane, 3,4-dimethylbenzoic acid, 5-methyl-3-hexene-2-ketone, n-hexanol, methyl 2-oxoglutarate and 4-methoxyphenol. Gender and the 10 characteristic VOCs were included in the diagnostic model, with a sensitivity of 83.8% and a specificity of 96.2%.ConclusionAnalysis of exhaled breath VOCs is expected to be one of the potential methods used for early stage lung cancer diagnosis.
ObjectiveTo investigate the predictive value of volatile organic compounds (VOCs) on pulmonary nodules in people aged less than 50 years.MethodsThe 147 patients with pulmonary nodules and aged less than 50 years who were treated in the Department of Thoracic Surgery of Sichuan Cancer Hospital from August 1, 2019 to January 15, 2020 were divided into a lung cancer group and a lung benign disease group. The lung cancer group included 36 males and 68 females, with the age of 27-49 (43.54±5.73) years. The benign lung disease group included 23 males and 20 females, with the age of 22-49 (42.49±6.83) years. Clinical data and exhaled breath samples were collected prospectively from the two groups. Exhaled breath VOCs were analyzed by gas chromatography mass spectrometry. Binary logistic regression analysis was used to select variables and establish a prediction model. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve of the prediction model were calculated.ResultsThere were statistically significant differences in sex (P=0.034), smoking history (P=0.047), cyclopentane (P=0.002), 3-methyl pentane (P=0.043) and ethylbenzene (P=0.009) between the two groups. The sensitivity, specificity and area under the ROC curve of the prediction model with gender, cyclopentane, 3-methyl pentane, ethylbenzene and N,N-dimethylformamide as variables were 80.8%, 60.5% and 0.781, respectively.ConclusionThe combination of VOCs and clinical characteristics has a certain predictive value for the benign and malignant pulmonary nodules in people aged less than 50 years.
Objective To explore composition of volatile organic compounds (VOCs) in exhaled breath of low-dose radiation-damaged Sprague-Dawely (SD) rats by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS), and search for the differential metabolites of VOCs in the series of rats after radiation damage, and establish a noninvasive radiation damage detection method. Methods SD rats were randomly divided into five groups (a blank group, a 0.5-Gy group, a 1-Gy group, a 2-Gy group, and a 3-Gy group), with 8 rats in each group. A low-dose radiation injury model was established in rats. After the cobalt source radiation damage was performed, the body weight of rats was recorded, peripheral blood hematology was analyzed, and the exhaled breath of rats was collected on the 1st, 5th, 9th and 13th day. The composition of VOCs in the exhaled breath was analyzed by using the TD30-GC-MS technique, and multivariate statistical analyses were carried out to explore and obtain the differentiated metabolites after the radiation damage. Results After radiation damage, the rats showed a short-term decrease in body weight, peripheral blood and lung tissue sections were different, and the content of VOCs components in the exhaled breath of the damaged rats was significantly different from that of the rats in the blank group. Among them, four VOCs, acetophenone, nonanal, decanal and tetradecane increased, while heptane, chlorobenzene, paraxylene and m-dichlorobenzene decreased. Conclusions Through the GC-MS analysis of the exhaled breath of rats, eight components of VOCs in the exhaled breath of rats can be used as differential metabolites of radiation damage. This study lays a foundation for the establishment of a GC-MS analysis method for the components of VOCs in the exhaled breath of rats, as well as for the development of a nondestructive analytical assay for biological radiation damage.
ObjectiveTo explore the diagnostic value of exhaled volatile organic compounds (VOCs) for cystic fibrosis (CF). MethodsA systematic search was conducted in PubMed, EMbase, Web of Science, Cochrane Library, CNKI, Wanfang, VIP, and SinoMed databases up to August 7, 2024. Studies that met the inclusion criteria were selected for data extraction and quality assessment. The quality of included studies was assessed by the Newcastle-Ottawa Scale (NOS), and the risk of bias and applicability of included prediction model studies were assessed by the prediction model risk of bias assessment tool (PROBAST).ResultsA total of 10 studies were included, among which 5 studies only identified specific exhaled VOCs in CF patients, and another 5 developed 7 CF risk prediction models based on the identification of VOCs in CF. The included studies reported a total of 75 exhaled VOCs, most of which belonged to the categories of acylcarnitines, aldehydes, acids, and esters. Most models (n=6, 85.7%) only included exhaled VOCs as predictive factors, and only one model included factors other than VOCs, including forced expiratory flow at 75% of forced vital capacity (FEF75) and modified Medical Research Council scale for the assessment of dyspnea (mMRC). The accuracy of the models ranged from 77% to 100%, and the area under the receiver operating characteristic curve ranged from 0.771 to 0.988. None of the included studies provided information on the calibration of the models. The results of the Prediction Model Risk of Bias Assessment Tool (PROBAST) showed that the overall bias risk of all predictive model studies was high bias risk, and the overall applicability was unclear. ConclusionThe exhaled VOCs reported in the included studies showed significant heterogeneity, and more research is needed to explore specific compounds for CF. In addition, risk prediction models based on exhaled VOCs have certain value in the diagnosis of CF, but the overall bias risk is relatively high and needs further optimization from aspects such as model construction and validation.