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

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.