Pregnant women appear to be more susceptible to infectious diseases than women in reproductive age. According to the California Department of Public Health pregnant women were 9.6-folds more likely to be hospitalized during the 2009 influenza outbreak when compared to non-pregnant women in reproductive age. In contrast, it was reported that of 16,749 COVID-19 patients that were hospitalized in the UK, the probability for pregnant women to require in-patient care due to infection by SARS-CoV-2 was 0.95 versus non-pregnant women. Therefore 9.6/0.95 = 10.10, which brings us to the conclusion that pregnant women are 10.10-folds less likely to be hospitalized for a SARS-CoV-2 infection than for the 2009 H1N1 pandemic. Folic acid supplementation during pregnancy could be the factor that is protecting these patients against SARS-CoV-2 infection. Two independent papers that used informatic simulation proved that folic acid reduced the replication of this virus. One of them showed that folic acid inhibits the furin protease which the virus needs in order to enter its host cell, while the other one explained that folic acid inactivates protease 3CL^pro, a protein that the virus needs to replicate. Nonetheless the probability that folic acid blocks two different proteins is very low, therefore the mechanism by which folic acid has apparently protected pregnant women during the COVID-19 pandemic has not been determined.

Adipose tissue presents structural and functional changes in obesity and type 1 diabetes mellitus (T1DM). In obesity, the size and number of adipocytes and adipokine secretion increases. In T1DM, a loss of adipose tissue suggests changes in the metabolic activity of this tissue. A significant challenge is to find alternative noninvasive methods to evaluate molecular changes in adipose tissue related to obesity and T1DM. Recently, Raman spectroscopy and chemometrics techniques have emerged as a tool for biological tissue analysis. In this work, we propose the use of Raman spectroscopy to characterize spectral differences in adipose tissue from different rat groups (control, obese, and T1DM). The Raman spectra were analyzed using direct band analysis, ratiometric analysis, and chemometric methods (principal component analysis (PCA) and support vector machines (SVMs)). We found that the Raman spectra of obese rats showed significant spectral differences compared to control and diabetic groups related to fatty acids Raman bands. Also, the obese group has a significant decrease in the degree of unsaturation of lipids. The PCA–SVM models showed classification performance ranging from 71.43% to 71.79% accuracy for brown and white adipose tissue samples, respectively. In conclusion, the results demonstrate that Raman spectroscopy can be used as a nondestructive method to assess adipose tissue according to a metabolic condition.

Excess fat in abdominal deposits is a risk factor for multiple conditions, including metabolic syndrome (MetS); lipid metabolism plays an essential role in these pathologies; fatty acid-binding proteins (FABPs) are dedicated to the cytosolic transport of fat. FABP4, whose primary source is adipose tissue, is released into the circulation, acting as an adipokine, while FABP5 also accompanies the adverse effects of MetS. FABP4 and 5 are potential biomarkers of MetS, but their behavior during syndrome evolution has not been determined. Raman spectroscopy has been applied as an alternative method to disease biomarker detection. In this work, we detected spectral changes related to FABP4 and 5 in the serum at different points of time, using an animal model of a high-fat diet-induced MetS. FABP4 and 5 spectral changes show a contribution during the evolution of MetS, which indicates alteration to a molecular level that predisposes to established MetS. These findings place FABPs as potential biomarkers of MetS and Raman spectroscopy as an alternative method for MetS assessment.

Raman spectroscopy, coupled with machine learning, can be used to effectively assess liver health. Our models achieved up to 91.6% accuracy in classifying spectral data, showing high potential for NAFLD diagnosis and monitoring.