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. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment …
Static magnetic field influence on rat brain function detected by heart rate monitoring.
The aim of the present study was to identify the effects of a static magnetic field (SMF) on rat brain structures that control autonomic functions, specifically heart rate and heart rhythmicity. The experiments were carried out on 44 male Wistar rats under ketamine-xylazine anesthesia. SMF was induced using samarium-cobalt fused magnets (20 x 20 x 10 mm in size) placed bitemporally. Magnetic induction intensity was 100 mT on the surface of the head. Duration of magnetic field application was 15 min. An electrocardiogram was recorded from limb lead II, and both heart rate (average duration of cardiac cycles) and heart rhythmicity were analyzed before and after SMF application. SMF evoked cha…
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. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These f…
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. Methods A validation study of patients with gastric cancer, patients with high-risk precancerous gastric lesions, and controls was conducted with 2 SniffPhone devices. Linear discriminant an…
Īslaicīga pastāvīgā magnētiskā lauka ietekmes uz laboratorijas dzīvnieku fizioloģiskām funkcijām atkarībā no indukcijas vektora virziena
Elektroniskā versija nesatur pielikumus