Pulmonary infection, and not systemic inflammation, accounts for increased concentrations of exhaled nitric oxide in patients with septic shock

Exhaled nitric oxide in intubated ICU patients on mechanical ventilation-a feasibility study

It can be a clinical challenge to distinguish inflammation from infection in critically ill patients. Therefore, valid and conclusive surrogate markers for infections are desired. Nitric oxide (NO) might be that marker since concentrations of exhaled NO have shown to change in the presence of various diseases. This observational, prospective, single-center feasibility study aimed to investigate if fractional exhaled NO (FeNO) can be measured in intubated patients with or without infection, pneumonia and septic shock in a standardized, reliable setting. 20 intubated patients in the intensive care unit (ICU) were included for analysis. FeNO mean values were measured in the endotracheal tube via the suction channel using a chemiluminescence based analyzer. We developed a pragmatic method to measure FeNO repeatedly and reliably in intubated patients using a chemiluminescence based analyzer. We found a median of 0.98 (0.59-1.44) FeNO mean (ppb) in exhaled breath from all 20 intubated patient. Intubated patient with suspected infection had a significantly lower median FeNO mean compared with the intubated patients without suspected infection. Similarly did patients with septic shock demonstrate a significantly lower median FeNO mean than without septic shock. We found no statistical difference in median FeNO mean for intubated patients with pneumonia. It was feasible to measure FeNO in intubated patients in the ICU. Our results indicate decreased levels of FeNO in infected intubated patients in the ICU. The study was not powered to provide firm conclusions, so larger trials are needed to confirm the results and to prove FeNO as a useful biomarker for distinguishment between infection and inflammation in the ICU.

Perioperative Exhaled Nitric Oxide as an Indicator for Postoperative Pneumonia in Surgical Lung Cancer Patients: A Prospective Cohort Study Based on 183 Cases

Introduction

This study is conducted to investigate the correlation between perioperative fractional exhaled nitric oxide and postoperative pneumonia (POP) and the feasibility of perioperative FeNO for predicting POP in surgical lung cancer patients.

Methods

Patients who were diagnosed with non-small-cell lung cancer (NSCLC) were prospectively analyzed, and the relationship between perioperative FeNO and POP was evaluated based on patients' basic characteristics and clinical data in the hospital.

Results

There were 218 patients enrolled in this study. Finally, 183 patients were involved in the study, with 19 of them in the POP group and 164 in the non-POP group. The POP group had significantly higher postoperative FeNO (median: 30.0 vs. 19.0 ppb, P < 0.001) as well as change in FeNO (median: 10.0 vs. 0.0 ppb, P < 0.001) before and after the surgery. For predicting POP based on the receiver operating characteristic (ROC) curve, a cutoff value of 25 ppb for postoperative FeNO (Youden's index: 0.515, sensitivity: 78.9%, and specificity: 72.6%) and 4 ppb for change in FeNO (Youden's index: 0.610, sensitivity: 84.2%, specificity: 76.8%) were selected. Furthermore, according to the bivariate regression analysis, FEV1/FVC (OR = 0.948, 95% CI: 0.899–0.999, P=0.048), POD1 FeNO (OR = 1.048, 95% CI: 1.019–1.077, P=0.001), and change in FeNO (OR = 1.087, 95% CI: 1.044–1.132, P < 0.001) were significantly associated with occurrence of POP.

Conclusions

This prospective study revealed that a high postoperative FeNO (>25 ppb), as well as an increased change in FeNO (>4 ppb), may have the potential in detecting the occurrence of POP in surgical lung cancer patients.

polyHWG: 3D Printed Substrate-Integrated Hollow Waveguides for Mid-Infrared Gas Sensing

Gas analysis via mid-infrared (MIR) spectroscopic techniques has gained significance due to its inherent molecular selectivity and sensitivity probing pronounced vibrational, rotational, and roto-vibrational modes. In addition, MIR gas sensors are suitable for real-time monitoring in a wide variety of sensing scenarios. Our research team has recently introduced so-called substrate-integrated hollow waveguides (iHWGs) fabricated by precision milling, which have been demonstrated to be useful in online process monitoring, environmental sensing, and exhaled breath analysis especially if low sample volumes (i.e., few hundreds of microliters) are probed with rapid signal transients. A logical next step is to establish ultralightweight, potentially disposable, and low-cost substrate-integrated hollow waveguides, which may be readily customized and tailored to specific applications using 3D printing techniques. 3D printing provides access to an unprecedented variety of thermoplastic materials including biocompatible polylactides, readily etchable styrene copolymers, and magnetic or conductive materials. Thus, the properties of the waveguide may be adapted to suit its designated application, e.g., drone-mounted ultralightweight waveguides for environmental monitoring or biocompatible disposable sensor interfaces in medical/clinical applications.

Assessment of the exhalation kinetics of volatile cancer biomarkers based on their physicochemical properties

The current review provides an assessment of the exhalation kinetics of volatile organic compounds (VOCs) that have been linked with cancer. Towards this end, we evaluate various physicochemical properties, such as 'breath:air' and 'blood:fat' partition coefficients, of 112 VOCs that have been suggested over the past decade as potential markers of cancer. With these data, we show that the cancer VOC concentrations in the blood and in the fat span over 12 and 8 orders of magnitude, respectively, in order to provide a specific counterpart concentration in the exhaled breath (e.g., 1 ppb). This finding suggests that these 112 different compounds have different storage compartments in the body and that their exhalation kinetics depends on one or a combination of the following factors: (i) the VOC concentrations in different parts of the body; (ii) the VOC synthesis and metabolism rates; (iii) the partition coefficients between tissue(s), blood and air; and (iv) the VOCs' diffusion constants. Based on this analysis, we discuss how this knowledge allows modeling and simulating the behavior of a specific VOC under different sampling protocols (with and without exertion of effort). We end this review by a brief discussion on the potential role of these scenarios in screening and therapeutic monitoring of cancer.