RESUMEN
It is well known that vitamin D's general immunomodulatory actions are helpful in viral infections and that a shortage is linked to a more serious prognosis for Covid-19. In this sistematic review, we examined the existing literature on evidence as to whether there is also link between vitamin D range levels in pediatric population and the outcome of the Covid-19 infection. We looked for studies that measured vitamin D blood concentrations and examined the effects of vitamin D supplementation in young infected patients. Vitamin D may decrease the risk of respiratory infections in a number of ways through its interactions with numerous cells, including by decreasing viral survival and replication, reducing the cytokine storm, raising angiotensin-converting enzyme 2 concentrations (ACE2) while not damaging the endothelial integrity. The incidence or severity of Covid-19 is linked with blood 25-hydroxyvitamin D concentrations, according to many observational studies. However experimental verification is still needed. Given their safety and broad therapeutic window, vitamin D supplements seem to be an effective way for individuals and doctors to prevent or treat Covid-19. Nonetheless, the outcomes of significant vitamin D randomized controlled trials are further needed.Copyright © 2022 Maria Nicolae et al., published by Sciendo.
RESUMEN
The inference of disease transmission networks is an important problem in epidemiology. One popular approach for building transmission networks is to reconstruct a phylogenetic tree using sequences from disease strains sampled from infected hosts and infer transmissions based on this tree. However, most existing phylogenetic approaches for transmission network inference are highly computationally intensive and cannot take within-host strain diversity into account. Here, we introduce a new phylogenetic approach for inferring transmission networks, TNet, that addresses these limitations. TNet uses multiple strain sequences from each sampled host to infer transmissions and is simpler and more accurate than existing approaches. Furthermore, TNet is highly scalable and able to distinguish between ambiguous and unambiguous transmission inferences. We evaluated TNet on a large collection of 560 simulated transmission networks of various sizes and diverse host, sequence, and transmission characteristics, as well as on 10 real transmission datasets with known transmission histories. Our results show that TNet outperforms two other recently developed methods, phyloscanner and SharpTNI, that also consider within-host strain diversity. We also applied TNet to a large collection of SARS-CoV-2 genomes sampled from infected individuals in many countries around the world, demonstrating how our inference framework can be adapted to accurately infer geographical transmission networks. TNet is freely available from https://compbio.engr.uconn.edu/software/TNet/.