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The relationship between serum 25-hydroxyvitamin D (25(OH)D) levels, genomic response to vitamin D (Vit.D), and positivity to SARS-CoV-2 remains understudied. In this pilot study, during the follow-up of patients with Inflammatory Bowel Disease (IBD) and COVID-19, we investigated this issue by analyzing the molecular contents of serum extracellular vesicles (EVs) from six groups of IBD patients (n = 32), classified according to anti-SARS-CoV-2 status, 25(OH)D level, and Vit.D supplementation, by small RNA-seq. This analysis revealed differentially expressed miRNAs, PIWI-RNA, transfer RNA, small nucleolar RNAs, and protein-coding RNAs in the EVs obtained from these cohorts of IBD patients. Experimental validation evidenced a statistically significant increase in miR30d-5p, miR150-5p, Let-7f-5p, and Let-7a-5p in the anti-SARS-CoV-2-positive and low 25(OH)D and Vit.D supplemented groups with respect to the non-Vit.D supplemented group, indicating their responsiveness to Vit.D treatment. Bioinformatics analysis highlighted the regulation of these validated miRNAs by oxidative stress and inflammation, hallmarks of IBD and COVID-19. Our study reports an unprecedented panel of circulating EV-enclosed inflammation- and oxidative stress-related miRNAs, the potentiality of which, as biomarkers for Vit.D responsivity in IBD patients, needs to be explored in future studies on larger cohorts in order to allow clinicians to optimize current treatment strategies upon viral infection.

This article discussed the influence of activation energy on MHD flow of third-grade nanofluid model (MHD-TGNFM) along with the convective conditions and used the technique of backpropagation in artificial neural network using Levenberg-Marquardt technique (BANN-LMT). The PDEs representing (MHD-TGNFM) transformed into the system of ODEs. The dataset for BANN-LMT is computed for the six scenarios by using the Adam numerical method by varying the local Hartman number (Ha), Prandtl number (Pr), local chemical reaction parameter ( σ ), Schmidt number (Sc), concentration Biot number ( γ 2 ) and thermal Biot number ( γ 1 ). By testing, validation and training process of (BANN-LMT), the estimated solutions are interpreted for (MHD-TGNFM). The validation of the performance of (BANN-LMT) is done through the MSE, error histogram and regression analysis. The concentration profile increases when there is an increase in Biot number and the local Hartmann number; meanwhile, it decreases for the higher values of Schmidt number and the local chemical reaction parameter., Competing Interests: Conflict of interestThe authors declare that they have no competing interests., (© King Fahd University of Petroleum & Minerals 2021.)