Ultratrace eNose Sensing of VOCs toward Breath Analysis Applications Utilizing an eNose-Based Analyzer.

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection.

Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters).

Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants.

Breath Sensor Technology for the Use in Mechanical Lung Ventilation Equipment for Monitoring Critically Ill Patients.

Background: The need for mechanical lung ventilation is common in critically ill patients, either with COVID-19 infection or due to other causes. Monitoring of patients being ventilated is essential for timely and improved management. We here propose the use of a novel breath volatile organic compound sensor technology to be used in a mechanical lung ventilation machine for this purpose; the technology was evaluated in critically ill COVID-19 patients on mechanical lung ventilation.

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies.

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays.

Sensing gastric cancer via point-of-care sensor breath analyzer.

Background: Detection of disease by means of volatile organic compounds from breath samples using sensors is an attractive approach to fast, noninvasive and inexpensive diagnostics. However, these techniques are still limited to applications within the laboratory settings. Here, we report on the development and use of a fast, portable, and IoT-connected point-of-care device (so-called, SniffPhone) to detect and classify gastric cancer to potentially provide new qualitative solutions for cancer screening.

An eNose-based method performing drift correction for online VOC detection under dry and humid conditions.

Our recently demonstrated innovative concept of electronic nose (eNose) based on a combination of gas sensors is further tested and benchmarked in the present study. The system is a test bed for gas sensors of different principal technologies distributed within three compartments, which share a compact, very low volume, temperature-controlled sensing chamber. Here, the eNose-based analyser contains three sensing arrays of commercially available semiconducting metal oxide (MOX) gas sensors: one compartment contains 8 analog MOX sensors, while the other two compartments comprise 10 digital MOX sensors.

An Innovative Modular eNose System Based on a Unique Combination of Analog and Digital Metal Oxide Sensors.

An innovative concept for an electronic nose (eNose) system based on a unique combination of analog and digital sensors for online monitoring is presented. The developed system consists of small sensing arrays of commercially available semiconducting metal oxide (MOX) gas sensors in a compact, modular, low sample volume, temperature-controlled sensing chamber. The sensing chamber comprises three compartments, each of which may contain several analog and/or digital MOX sensors.

Infectious endocarditis due to Streptococcus pneumoniae in a cardiac surgery patient: a new form of clinical presentation.

High mortality associated with pneumococcal endocarditis is due to late diagnosis and the frequency of complications, which usually require early diagnostic and intensive therapeutic measures. We present the first reported case of pneumococcal endocarditis with simultaneous infection of an aortic prosthetic valve, native tricuspid valve, and permanent pacemaker lead.

Muscle uncoupling protein 3 overexpression mimics endurance training and reduces circulating biomarkers of incomplete β-oxidation.

Exercise substantially improves metabolic health, making the elicited mechanisms important targets for novel therapeutic strategies. Uncoupling protein 3 (UCP3) is a mitochondrial inner membrane protein highly selectively expressed in skeletal muscle. Here we report that moderate UCP3 overexpression (roughly 3-fold) in muscles of UCP3 transgenic (UCP3 Tg) mice acts as an exercise mimetic in many ways.

Estimators of the local false discovery rate designed for small numbers of tests.

Histogram-based empirical Bayes methods developed for analyzing data for large numbers of genes, SNPs, or other biological features tend to have large biases when applied to data with a smaller number of features such as genes with expression measured conventionally, proteins, and metabolites. To analyze such small-scale and medium-scale data in an empirical Bayes framework, we introduce corrections of maximum likelihood estimators (MLEs) of the local false discovery rate (LFDR). In this context, the MLE estimates the LFDR, which is a posterior probability of null hypothesis truth, by estimating the prior distribution.