Characterization of Covid-19 infected pregnant women sera using laboratory indexes, vibrational spectroscopy, and machine learning classifications

Abstract

Herein, we show differences in blood serum of asymptomatic and symptomatic pregnant women infected with COVID-19 and correlate them with laboratory indexes, ATR FTIR and multivariate machine learning methods. We collected the sera of COVID-19 diagnosed pregnant women, in the second trimester (n = 12), third-trimester (n = 7), and second-trimester with severe symptoms (n = 7) compared to the healthy pregnant (n = 11) women, which makes a total of 37 participants. To assign the accuracy of FTIR spectra regions where peak shifts occurred, the Random Forest algorithm, traditional C5.0 single decision tree algorithm and deep neural network approach were used. We verified the correspondence between the FTIR results and the laboratory indexes such as: the count of peripheral blood cells, biochemical parameters, and coagulation indicators of pregnant women. CH 2 scissoring, amide II, amide I vibrations could be used to differentiate the groups. The accuracy calculated by machine learning methods was higher than 90%. We also developed a method based on the dynamics of the absorbance spectra allowing to determine the differences between the spectra of healthy and COVID-19 patients. Laboratory indexes of biochemical parameters associated with COVID-19 validate changes in the total amount of proteins, albumin and lipase.

Development of novel spectroscopic and machine learning methods for the measurement of periodic changes in COVID-19 antibody level

“…All spectra were analyzed using OPUS 7.0 software. For each spectrum, the baseline correction, vector normalization and smoothing with Savitzky–Golay filter were performed [20] .…”

“…Khan et al showed that Raman spectroscopy analysis of blood serum can distinguish (with 97% of precision, 100% of sensitivity and 95% of specificity) between a healthy patient and a patient infected by hepatitis B virus [19] . Furthermore, in the other work, the authors showed, that using FTIR spectroscopy it is possible to distinguish between females suffering from symptomatic and asymptomatic COVID-19 [20] . In the other work, machine learning methods showed, that using FTIR data it is possible to detect COVID-19 infection with 87% of sensitivity [21] .…”

Novel photonic methods for diagnosis of SARS‐CoV‐2 infection

“…Guleken et al studied the detection of COVID‐19 disease in the blood serum of symptomatic and asymptomatic and pregnant women. The samples from pregnant women with COVID‐19 disease and healthy pregnant women were compared [ 23 ]. The samples were analyzed using FTIR; the peak shifts were analyzed with multivariate machine learning approaches (eg, a Random Forest algorithm, a C5.0 single decision tree algorithm, and a deep neural network).…”

“…The biochemical levels, peripheral blood cell levels, and coagulation parameters for pregnant women are shown in Figure 2 . The machine learning techniques were able to differentiate among the groups using amide II vibrations, amide I vibrations, and CH 2 scissoring; an accuracy greater than 90% was demonstrated using this approach [ 23 ].…”

Use of Machine Learning to Identify Clinical Variables in Pregnant and Non-Pregnant Women with SARS-CoV-2 Infection

“…[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] Understanding the physiological changes in the cardiopulmonary and immunological systems that occur in pregnancy, ongoing research has shown the higher prevalence of severe SARS-CoV-2 infection among pregnant women. 20,21 With this in mind we hypothesized that pregnant and non-pregnant women would have different clinical variables predictive of a positive COVID-19 test.…”