Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (FTIR) and Artificial Neural Networks Applied to Investigate Quantitative Changes of Selected Soluble Biomarkers, Correlated with H. pylori Infection in Children and Presumable Consequent Delayed Growth

infections causing gastroduodenal disorders are a common medical problem. The aim of this study was to determine the specific motives of infrared spectroscopy (IR) spectra of sera from -infected and uninfected children applied to investigate quantitatively-selected soluble biomarkers correlated with...

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Bibliographic Details
Published in:Journal of clinical medicine 2020-11, Vol.9 (12), p.3852
Main Authors: Gonciarz, Weronika, Lechowicz, Łukasz, Urbaniak, Mariusz, Kaca, Wiesław, Chmiela, Magdalena
Format: Article
Language:English
Subjects:
Antigens
Clinical medicine
Fatty acids
Infections
Mathematical functions
Mathematical models
Neural networks
Neurons
Peptides
Proteins
Spectrum analysis
Ulcers
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Summary:infections causing gastroduodenal disorders are a common medical problem. The aim of this study was to determine the specific motives of infrared spectroscopy (IR) spectra of sera from -infected and uninfected children applied to investigate quantitatively-selected soluble biomarkers correlated with infection in children and presumable consequent delayed growth. Sera from 41 children infected with (Hp(+)) and 43 uninfected (Hp(-)) under the care of the Polish Mother's Hospital in Lodz, Poland, were analyzed. The status was confirmed by gastroscopy, C urea breath testing, and anti- IgG antibodies. Infrared spectra were measured using an FTIR/FT-NIR Spectrum 400 spectrometer (PerkinElmer). The IR spectrum was measured in the wavenumber range 3000-750 cm and subjected to mathematical calculation of the first derivative. Based on the chi-square test, 10 wavenumbers of spectra correlating with infection were selected for use in designing an artificial neural network. Ten parts of the IR spectra correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and the W4 window related to nucleic acids and hydrocarbons. Artificial neural networks for infection were developed based on chemometric data. By mathematical modeling, children were classified towards infection in conjunction with elevated levels of selected biomarkers in serum potentially related to growth retardation. The study concludes that IR spectroscopy and artificial neural networks may help to confirm -driven growth disorders in children.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm9123852