Exhaled breath and gastric-endoluminal gas (volatile products of diseased tissues) contain a large number of volatile organic compounds (VOCs), which are valuable for early diagnosis of upper gastrointestinal (UGI) cancer. In this study, exhaled breath and gastric-endoluminal gas of patients with UGI cancer and benign disease were analyzed by gas chromatography-mass spectrometry (GC-MS) and ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOFMS) to construct UGI cancer diagnostic models. Breath samples of 116 UGI cancer and 77 benign disease subjects and gastric-endoluminal gas samples of 114 UGI cancer and 76 benign disease subjects were collected. Machine learning (ML) algorithms were used to construct UGI cancer diagnostic models. Classification models based on exhaled breath for distinguishing UGI cancer from the benign group have area under the curve (AUC) of receiver operating characteristic (ROC) curve values of 0.959 and 0.994 corresponding to GC-MS and UVP-TOFMS analysis, respectively. The AUC values of models based on gastric-endoluminal gas for UGI cancer and benign group classification are 0.935 and 0.929 corresponding to GC-MS and UVP-TOFMS analysis, respectively. This work indicates that volatolomics analysis of exhaled breath and gastric-endoluminal diseased tissues have great potential in early screening of UGI cancer. Moreover, gastric-endoluminal gas can be a means of gas biopsy to provide auxiliary information for the examination of tissue lesions during gastroscopy.

Continuous monitoring for immunosuppressive status, infection and complications are a must for kidney transplantation (KTx) recipients. Traditional monitoring including blood sampling and kidney biopsy, which caused tremendous medical cost and trauma. Therefore, a cheaper and less invasive approach was urgently needed. We thought that a breath test has the potential to become a feasible tool for KTx monitoring. A prospective-specimen collection, retrospective-blinded assessment strategy was used in this study. Exhaled breath samples from 175 KTx recipients were collected in West China Hospital and tested by online ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOF–MS). The classification models based on breath test performed well in classifying normal and abnormal values of creatinine, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN) and tacrolimus, with AUC values of 0.889, 0.850, 0.849 and 0.889, respectively. Regression analysis also demonstrated the predictive ability of breath test for clinical creatinine, eGFR, BUN, tacrolimus level, as the predicted values obtained from the regression model correlated well with the clinical true values (p < 0.05). The findings of this investigation implied that a breath test by using UVP-TOF–MS for KTx recipient monitoring is possible and accurate, which might be useful for future clinical screenings.

Foodborne bacteria are widespread contaminated sources of food; hence, the real-time monitoring of pathogenic bacteria in food production is important for the food industry. In this study, a novel rapid detection method based on microbial volatile organic compounds (MVOCs) emitted from foodborne bacteria was established by using ultraviolet photoionization timeof-flight mass spectrometry (UVP-TOF-MS). The results showed obvious differences of MVOCs among the five species of bacteria, and the characteristic MVOCs for each bacterium were selected by a feature selection algorithm. Online monitoring of MVOCs during bacterial growth displayed distinct metabolomic patterns of the five species. MVOCs were most abundant and varied among species during the logarithmic phase. Finally, MVOC production by bacteria in different food matrixes was explored. The machine learning models for bacteria cultured in different matrixes showed a good classification performance for the five species with an accuracy of over 0.95. This work based on MVOC analysis by online UVP-TOF-MS achieved effective rapid detection of bacteria and showed its great application potential in the food industry for bacterial monitoring.

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