Background: SARS-CoV-2 has caused a tremendous threat to global health. PCR and antigen testing have played a prominent role in the detection of SARS-CoV-2-infected individuals and disease control. An efficient, reliable detection tool is still urgently needed to halt the global COVID-19 pandemic. Recently, FDA emergency approved VOC as an alternative test for COVID-19 detection. Methods and materials: In this case-control study, we prospectively and consecutively recruited 95 confirmed COVID-19 patients and 106 healthy controls in the designated hospital for treatment of COVID-19 patients in Shenzhen, China. Exhaled breath samples were collected and stored in customized bags and then detected by HPPI-TOFMS for volatile organic components (VOCs). Machine learning (ML) algorithms were employed for COVID-19 detection model construction. Participants were randomly assigned in a 5:2:3 ratio to the training, validation, and blinded test sets. The sensitivity (SEN), specificity (SPE), and other general metrics were employed for the VOCs based COVID-19 detection model performance evaluation. Results: The VOCs based COVID-19 detection model achieved good performance, with a SEN of 92.2% (95% CI: 83.8%, 95.6%), a SPE of 86.1% (95% CI: 74.8%, 97.4%) on blinded test set. Five potential VOC ions related to COVID-19 infection were discovered, which are significantly different between COVID-19 infected patients and controls. Conclusions: This study evaluated a simple, fast, non-invasive VOCs-based COVID-19 detection method and demonstrated that it has good sensitivity and specificity in distinguishing COVID-19 infected patients from controls. It has great potential for fast and accurate COVID-19 detection.

Background Current clinical tests for mycobacterial pulmonary diseases (MPD), such as pulmonary tuberculosis (PTB) and non-tuberculous mycobacteria pulmonary diseases (NTM-PD), are inaccurate, time-consuming, sputum-dependent, and/or costly. We aimed to develop a simple, rapid and accurate breath test for screening and differential diagnosis of MPD patients in clinical settings. Methods Exhaled breath samples were collected from 142 PTB, 68 NTM-PD and 9 PTB&NTM-PD patients, 93 patients with other pulmonary diseases (OPD) and 181 healthy controls (HC), and tested using the online high-pressure photon ionisation time-of-flight mass spectrometer (HPPI-TOF-MS). Machine learning models were trained and blindly tested for the detection of MPD, PTB, NTM-PD, and the discrimination between PTB and NTM-PD, respectively. Diagnostic performance was evaluated by metrics of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). Results The breath PTB detection model achieved a sensitivity of 81.8%, a specificity of 94.3%, an accuracy of 90.7%, and an AUC of 0.957 in the blinded test set (n=150). The corresponding metrics for the NTM-PD detection model were 95.5%, 86.7%, 88.0% and 0.947, respectively. For distinguishing PTB from NTM-PD, the model also achieved good performance with sensitivity, specificity, accuracy, and AUC of 95.5%, 90.9%, 93.9% and 0.974, respectively. 24 potential breath biomarkers associated with MPD were putatively identified and discussed, which included 2-picoline, ethanol, 1-Pentene, etc. Conclusions The developed breathomics-based MPD detection method was demonstrated for the first time with good performance for potential screening and diagnosis of PTB and NTM-PD using a refined operating procedure on the HPPI-TOF-MS platform.

IntroductionWe explored whether volatile organic compound (VOC) detection can serve as a screening tool to distinguish cognitive dysfunction (CD) from cognitively normal (CN) individuals.MethodsThe cognitive function of 1467 participants was assessed and their VOCs were detected. Six machine learning algorithms were conducted and the performance was determined. The plasma neurofilament light chain (NfL) was measured.ResultsDistinguished VOC patterns existed between CD and CN groups. The CD detection model showed good accuracy with an area under the receiver‐operating characteristic curve (AUC) of 0.876. In addition, we found that 10 VOC ions showed significant differences between CD and CN individuals (p < 0.05); three VOCs were significantly related to plasma NfL (p < 0.005). Moreover, a combination of VOCs with NfL showed the best discriminating power (AUC = 0.877).DiscussionDetection of VOCs from exhaled breath samples has the potential to provide a novel solution for the dilemma of CD screening.

It is estimated that Mycobacterium tuberculosis (M.tb) infected a quarter of the world's population (1). Latent tuberculosis infection (LTBI) constitutes a broad spectrum of infection states that differ by the degree of pathogen replication, host immune response, and inflammation (2). Approximately 5-10% of those with LTBI will progress to active tuberculosis (ATB) (3). WHO recommends immunodiagnostic tests for LTBI detection, either a tuberculin skin test (TST) or interferon-gamma (IFN-γ) release assays (IGRAs) (4). However, these tests are not precise enough. In certain situations, TB exposure can be used as a surrogate for LTBI (5). Furthermore, TST and IGRAs can not differentiate LTBI from ATB (6). Thus, a more precise tool is urgently needed for the consecutive management of uninfected status, LTBI, and ATB.Recent studies indicate that breathomics may be a useful rule-in or rule-out tool for diagnosing ATB (7), which uncovers the host-pathogen interaction via comprehensive exhaled breath analysis. Breathomics may hold promise to distinguish healthy subjects, LTBI and ATB (8) if a breath test can find the trace and tell the difference of M.tb in consecutive states in the host (9). High-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) is designed and developed by our team, which can directly detect volatile organic compounds (VOCs) in exhaled breath (10). In our previous studies, this breath detection platform has been verified in lung cancer (11,12), esophagus cancer (13), and Corona Virus Disease 2019 . In this study, we explored the use of this novel, rapid, simple, and inexpensive breath test to detect LTBI.We conducted a cross-sectional study (Chinese Clinical Trials Registry number: ChiCTR2200058346) in which a breath sample was collected from 435 participants with informed consent signed at the Third