Noninvasive Detection of Bacterial Infection in Children Using Piezoelectric E-Nose

Abstract

Currently, antibiotics are often prescribed to children without reason due to the inability to quickly establish the presence of a bacterial etiology of the disease. One way to obtain additional diagnostic information quickly is to study the volatile metabolome of biosamples using arrays of sensors. The goal of this work was to assess the possibility of using an array of chemical sensors with various sensitive coatings to determine the presence of a bacterial infection in children by analyzing the equilibrium gas phase (EGP) of urine samples. The EGP of 90 urine samples from children with and without a bacterial infection (urinary tract infection, soft tissue infection) was studied on the “MAG-8” device with seven piezoelectric sensors in a hospital. General urine analysis with sediment microscopy was performed using a Uriscan Pro analyzer and using an Olympus CX31 microscope. After surgical removal of the source of inflammation, the microbiological studies of the biomaterial were performed to determine the presence and type of the pathogen. The most informative output data of an array of sensors have been established for diagnosing bacterial pathology. Regression models were built to predict the presence of a bacterial infection in children with an error of no more than 15%. An indicator of infection is proposed to predict the presence of a bacterial infection in children with a high sensitivity of 96%.

Low-Cost Electronic Nose for the Determination of Urinary Infections

“…It is also known that many types of pathologies are characterized by a certain odor and consequently, by a set of volatile substances. Many substances that are metabolites in pathogenic processes are found in human secretions, and some authors state that ethanol, butanol and their oxidation products (acetic and butyric acids) are present in almost all samples [ 31 ]. Those authors, who also measured VOCs in urine with a different device, when analyzing their results to predict the bacterial contamination of urine samples, observed that the group of samples without bacteria in the urine was more dispersed, which is similar to the observations made in our study.…”

“…Nowadays, in clinical practice, it is quite common to prescribe antibiotics, even when there is no clear indication that it is a bacterial etiology. This is because there is no diagnostic tool that can quickly determine the presence of microorganisms and their sensitivity to various groups of antibiotics [ 31 ]. Unnecessary consumption of antibiotics leads to an increase in antimicrobial resistance (AMR) [ 32 ], which is a major health problem recognized by leading professional bodies around the world, including the World Health Organization (WHO).…”

Juice Quality Evaluation with Multisensor Systems—A Review

“…They can be used in medical research (e.g., in the detection of disease biomarkers, selection of therapy, metabolic disorders or organ dysfunctions, detection and recognition of eye bacteria or bacteria in the blood, detection of blood in the urine, etc.) [ 72 , 73 , 74 , 75 , 76 , 77 , 78 ], forensic and criminology [ 79 , 80 ], and in the cosmetics industry, in particular perfumery (e.g., for authenticity assessment, for the development of new fragrances) [ 81 , 82 ], pharmaceutical industry (for quality control, purity and homogeneity of drug composition, and product safety) [ 70 ], and food industry (e.g., for testing compliance with standards, marking product composition, getting to know the brand, marking impairment on the shelf, etc.) [ 60 , 83 , 84 ], as well as in agriculture (for assessing the maturity of crops, determining the date of harvest and storage time, assessing the degree of contamination, characterizing varieties, and diagnosing plant diseases) [ 69 , 85 , 86 , 87 , 88 ].…”

A Machine Olfaction-Based Overheating Diagnosis Method for Electrical Equipment

“…The second-order BP neural network works by first inputting the parameters of the feature vector Xi into the material judgment neural network to obtain the material vector named U. (12) shows the form of U.…”

“…Machine olfaction is a technology that enables gas detection by simulating the human olfactory system. It has been widely used and has shown significant advantages in scientific research fields such as gas composition analysis 8 VOLUME XX, 2017 [7], chemical analysis [8,9], agriculture [10,11], medicine [12], food [13][14][15], and criminal investigation [16]. In electrical equipment overheating detection, some scholars have used gas sensors combined with machine olfaction techniques to detect overheating of cables [17,18].…”