Identification of circulating biomarkers of Crohn's disease and spondyloarthritis using Fourier transform infrared spectroscopy

Prada, Pierre; Brunel, Benjamin; Moulin, David; Rouillon, Lise; Netter, Patrick; Lœuille, Damien; Slimano, Florian; Bouché, Olivier; Peyrin-Biroulet, L; Jouzeau, Jean‐Yves; Piot, Olivier

doi:10.1002/jbio.202200200

Cited by 2 publications

(1 citation statement)

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“…The characteristic peaks of serum spectra are mainly at 1078, 1245, 1400, 1542, 1641, 2940, and 3280 cm -1 , and Table 2 lists the tentative material assignments for these major FTIR peaks. The most substantial characteristic peaks are at 1641 cm -1 (amide I) and 1542 cm -1 (amide II) (37,38), which represent the stretching and bending vibrations on the amide C=O as well as the N-H groups of proteins, respectively (39), the broadband at 3280 cm -1 also corresponds to the N-H group, but it is a stretching vibration (40) and is referred to as the amide A mode; the vicinity of 1420 cm -1 represents the region of proteins, phosphate molecules, and fatty acids (16), and it has been illustrated that this wavelength is related to proline and tryptophan in proteins; 1245 cm -1 represents amide III, the asymmetric P=O stretching in PO 2 (25); 1078 cm -1 represents the region of carbohydrates (15); and the area of 2940 cm -1 is dominated by fatty acids and cholesterol, among other substances (41).…”

Section: Spectral Analysismentioning

confidence: 99%

Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms

Wu,

Shuai,

Chen

et al. 2023

Front. Immunol.

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IntroduceAnkylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in severe cases, they can cause lifelong disability and even death. Studies have shown that early diagnosis and treatment are key to improving patient outcomes. Therefore, a rapid and accurate method for rapid diagnosis of diseases has been established, which is of great clinical significance for realizing early diagnosis of diseases and improving patient prognosis.MethodsThis study was based on Fourier transform infrared spectroscopy (FTIR) combined with a deep learning model to achieve non-invasive, rapid, and accurate differentiation of AS, RA, OA, and healthy control group. In the experiment, 320 serum samples were collected, 80 in each group. AlexNet, ResNet, MSCNN, and MSResNet diagnostic models were established by using a machine learning algorithm.ResultThe range of spectral wave number measured by four sets of Fourier transform infrared spectroscopy is 700-4000 cm-1. Serum spectral characteristic peaks were mainly at 1641 cm-1(amide I), 1542 cm-1(amide II), 3280 cm-1(amide A), 1420 cm-1(proline and tryptophan), 1245 cm-1(amide III), 1078 cm-1(carbohydrate region). And 2940 cm-1 (mainly fatty acids and cholesterol). At the same time, AlexNet, ResNet, MSCNN, and MSResNet diagnostic models are established by using machine learning algorithms. The multi-scale MSResNet classification model combined with residual blocks can use convolution modules of different scales to extract different scale features and use resblocks to solve the problem of network degradation, reduce the interference of spectral measurement noise, and enhance the generalization ability of the network model. By comparing the experimental results of the other three models AlexNet, ResNet, and MSCNN, it is found that the MSResNet model has the best diagnostic performance and the accuracy rate is 0.87.ConclusionThe results prove the feasibility of serum Fourier transform infrared spectroscopy combined with a deep learning algorithm to distinguish AS, RA, OA, and healthy control group, which can be used as an effective auxiliary diagnostic method for these rheumatic immune diseases.

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