Understanding the evolution of chromatin conformation among species is fundamental to elucidate the architecture and plasticity of genomes. Nonrandom interactions of linearly distant loci regulate gene function in species-specific patterns, affecting genome function, evolution, and, ultimately, speciation. Yet, data from nonmodel organisms are scarce.
View Article and Find Full Text PDFEukaryotic genomes exhibit a dynamic interplay between single-copy sequences and repetitive DNA elements, with satellite DNA (satDNA) representing a substantial portion, mainly situated at telomeric and centromeric chromosomal regions. We utilized Illumina next-generation sequencing data from to investigate its satellitome. Cytogenetic mapping via fluorescence in situ hybridization was performed for the most abundant satDNA families.
View Article and Find Full Text PDFIntroduction: Diabetic Kidney Disease (DKD) is the most common cause of end-stage chronic kidney disease (CKD), conditioning these patients to a worse renal prognosis and higher cardiovascular mortality and/or requirement for renal replacement therapy. The use of novel information and communication technologies (ICTs) focused on the field of health, may facilitates a better quality of life and disease control in these patients. Our objective is to evaluate the effect of monitoring DKD patients using NORA-app.
View Article and Find Full Text PDFThe satellitome of the beetle Linneo, 1758 has been characterized through chromosomal analysis, genomic sequencing, and bioinformatics tools. C-banding reveals the presence of constitutive heterochromatin blocks enriched in A+T content, primarily located in pericentromeric regions. Furthermore, a comprehensive satellitome analysis unveils the extensive diversity of satellite DNA families within the genome of .
View Article and Find Full Text PDFBackground: Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence.
Methods: We analyzed clinical and socioeconomic data from a prospectively collected public health care-based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014-2020) in Catalonia-Spain.