Next Article in Journal
Inflammatory Breast Carcinoma: Elevated microRNA miR-181b-5p and Reduced miR-200b-3p, miR-200c-3p, and miR-203a-3p Expression as Potential Biomarkers with Diagnostic Value
Next Article in Special Issue
The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning
Previous Article in Journal
The Constitutive Lack of α7 Nicotinic Receptor Leads to Metabolic Disorders in Mouse
Previous Article in Special Issue
Nerve Segmentation with Deep Learning from Label-Free Endoscopic Images Obtained Using Coherent Anti-Stokes Raman Scattering
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomolecules
Volume 10
Issue 7
10.3390/biom10071058
biomolecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans

by
Elke Korb
1,†,
Murat Bağcıoğlu
2,†,
Erika Garner-Spitzer
1,
Ursula Wiedermann
1,
Monika Ehling-Schulz
2,* and
Irma Schabussova
1,*
1
Institute of Specific Prophylaxis and Tropical Medicine, Medical University of Vienna, 1090 Vienna, Austria
2
Institute of Microbiology, Department of Pathobiology, University of Veterinary Medicine, 1210 Vienna, Austria
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biomolecules 2020, 10(7), 1058; https://doi.org/10.3390/biom10071058
Submission received: 15 June 2020 / Revised: 13 July 2020 / Accepted: 14 July 2020 / Published: 16 July 2020
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
Browse Figures
Versions Notes

Abstract

The unabated global increase of allergic patients leads to an unmet need for rapid and inexpensive tools for the diagnosis of allergies and for monitoring the outcome of allergen-specific immunotherapy (SIT). In this proof-of-concept study, we investigated the potential of Fourier-Transform Infrared (FTIR) spectroscopy, a high-resolution and cost-efficient biophotonic method with high throughput capacities, to detect characteristic alterations in serum samples of healthy, allergic, and SIT-treated mice and humans. To this end, we used experimental models of ovalbumin (OVA)-induced allergic airway inflammation and allergen-specific tolerance induction in BALB/c mice. Serum collected before and at the end of the experiment was subjected to FTIR spectroscopy. As shown by our study, FTIR spectroscopy, combined with deep learning, can discriminate serum from healthy, allergic, and tolerized mice, which correlated with immunological data. Furthermore, to test the suitability of this biophotonic method for clinical diagnostics, serum samples from human patients were analyzed by FTIR spectroscopy. In line with the results from the mouse models, machine learning-assisted FTIR spectroscopy allowed to discriminate sera obtained from healthy, allergic, and SIT-treated humans, thereby demonstrating its potential for rapid diagnosis of allergy and clinical therapeutic monitoring of allergic patients.
Keywords: FTIR spectroscopy; allergy; specific immunotherapy; allergic airway inflammation; serum; clinical diagnostics; metabolic fingerprinting; deep learning; convolutional neural networks; machine learning FTIR spectroscopy; allergy; specific immunotherapy; allergic airway inflammation; serum; clinical diagnostics; metabolic fingerprinting; deep learning; convolutional neural networks; machine learning

Share and Cite

MDPI and ACS Style

Korb, E.; Bağcıoğlu, M.; Garner-Spitzer, E.; Wiedermann, U.; Ehling-Schulz, M.; Schabussova, I. Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans. Biomolecules 2020, 10, 1058. https://doi.org/10.3390/biom10071058

AMA Style

Korb E, Bağcıoğlu M, Garner-Spitzer E, Wiedermann U, Ehling-Schulz M, Schabussova I. Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans. Biomolecules. 2020; 10(7):1058. https://doi.org/10.3390/biom10071058

Chicago/Turabian Style

Korb, Elke, Murat Bağcıoğlu, Erika Garner-Spitzer, Ursula Wiedermann, Monika Ehling-Schulz, and Irma Schabussova. 2020. "Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans" Biomolecules 10, no. 7: 1058. https://doi.org/10.3390/biom10071058

APA Style

Korb, E., Bağcıoğlu, M., Garner-Spitzer, E., Wiedermann, U., Ehling-Schulz, M., & Schabussova, I. (2020). Machine Learning-Empowered FTIR Spectroscopy Serum Analysis Stratifies Healthy, Allergic, and SIT-Treated Mice and Humans. Biomolecules, 10(7), 1058. https://doi.org/10.3390/biom10071058

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop