Identifying chronic rhinosinusitis without nasal polyps by analyzing aspirated nasal air with an electronic nose based on differential mobility spectrometry

Identifying viral infections through analysis of head space volatile organic compounds

Volatile organic compounds (VOCs) produced by human respiratory cells reflect metabolic and pathophysiological processes which can be detected with the use of modern technology. Analysis of exhaled breath or indoor air may potentially play an important role in screening of upper respiratory tract infections such as COVID-19 or influenza in the future. In this experimental study, air samples were collected and analyzed from the headspace of anin vitrocell culture infected by selected pathogens (influenza A H1N1 and seasonal coronaviruses OC43 and NL63). VOCs were measured with a real-time proton-transfer-reaction time-of-flight mass spectrometer and a differential mobility spectrometer. Measurements were performed every 12 h for 7 d. Non-infected cells and cell culture media served as references. In H1N1 and OC43 we observed four different VOCs which peaked during the infection. Different, individual VOCs were also observed in both infections. Activity began to clearly increase after 2 d in all analyses. We did not see increased VOC production in cells infected with NL63. VOC analysis seems to be suitable to differentiate the infected cells from those which are not infected as well as different viruses, from another. In the future, this could have practical value in both individual diagnostics and indoor environment screening.

Pericyte loss leads to microvessel remodeling and nasal polyp formation

BACKGROUND

Chronic rhinosinusitis (CRS) may be caused by increased vascular permeability and inflammatory cell leakage in the subepithelial tissue.

AIMS/OBJECTIVES

The aim of this study is to clarify the role of pericytes in tissue edema, microvessel dysfunction and vascular remodeling mechanisms in patients of CRS with nasal polyps (CRSwNP).

MATERIAL AND METHODS

A total of 63 tissue samples were collected, including 42 CRSwNP samples (22 eosinophilic CRSwNP (eCRSwNP) and 20 non-eosinophilic CRSwNP (non-eCRSwNP) samples) and 21 samples of CRS without nasal polyps (CRSsNP). The samples were stained by immunofluorescence to measure microvessel density (MVD) and microvessel pericyte coverage index (MPI).

RESULTS

We found that the albumin expression in the eCRSwNP group was significantly increased (p < .05). The MPI was significantly decreased (p <.05). There was a significant negative correlation between the MPI and the plasma albumin level (r=-0.82, p < .05). The MPI was negatively correlated with eosinophilic count (r=-0.77, p < .05). In the eCRSwNP group, the expressions of IL-4, Ang-1 and Ang-2 were increased compared with those in the control group.

CONCLUSIONS AND SIGNIFICANCE

Pericyte loss may induce microvessel dysfunction, affect the development of interstitial edema and eosinophilic exosmosis in eCRSwNP, and contribute to the formation and maintenance of nasal polyps.

Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose

Background

Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect BC in LTRs. Therefore, we aimed to assess the diagnostic accuracy of an eNose to detect BC in LTRs.

Methods

We performed a cross-sectional analysis within a prospective, single-center cohort study assessing the diagnostic accuracy of detecting BC using eNose technology in LTRs. In the outpatient clinic, consecutive LTR eNose measurements were collected. We assessed and classified the eNose measurements for the presence of BC. Using supervised machine learning, the diagnostic accuracy of eNose for BC was assessed in a random training and validation set. Model performance was evaluated using receiver operating characteristic analysis.

Results

In total, 161 LTRs were included with 80 exclusions because of various reasons. Of the remaining 81 patients, 16 (20%) were classified as BC and 65 (80%) as non-BC. eNose-based classification of patients with and without BC provided an area under the curve of 0.82 in the training set and 0.97 in the validation set.

Conclusions

Exhaled breath analysis using eNose technology has the potential to noninvasively detect BC.