Biology subjects

Matos de Oliveira, L.

Publications and source records attributed to Matos de Oliveira, L..

4 recordsLinked to original sources

bioRxiv2022

Molecular Characterization in 3D Structure of MicroRNA Expressed in Leprosy

IntroductionHansens disease, or leprosy, is a major public health problem in developing countries, caused by Mycobacterium leprae, and affecting the skin and peripheral nerves. However, M. leprae can also affect bone tissue, mucous membranes, liver, eyes, and testicles, producing a variety of clinical phenotypes. MicroRNAs (miRNAs) have been expressed in the various clinical forms of leprosy and could potentially be used for its diagnosis. ObjectiveIn silico design of the molecular structure of miRNAs expressed in leprosy. MethodWe performed a nucleotide sequence search of 17 miRNAs expressed in leprosy, designing in silico the molecular structure of the following miRNAs: miRNA-26a, miRNA-27a, miRNA-27b, miRNA-29c, miRNA-34c, miRNA-92a-1, miRNA-99a-2, miRNA-101-1, miRNA-101-2, miRNA-125b-1, miRNA-196b, miRNA-425-5p, miRNA-452, miRNA-455, miRNA-502, miRNA-539, and miRNA-660. We extracted the nucleotides were from the GenBank of National Center for Biotechnology Information genetic sequence database. We aligned the extracted sequences with the RNA Folding Form, and the three-dimensional molecular structure design was performed with the RNAComposer. ResultsWe demonstrate the nucleotide sequences, and molecular structure projection of miRNAs expressed in leprosy, and produces a tutorial on the molecular model of the 17 miRNAs expressed in leprosy through in silico projection processing of their molecular structures. ConclusionWe demonstrate in silico design of selected molecular structures of 17 miRNAs expressed in leprosy through computational biology.

bioinformatics↗

bioRxiv2021

Estimation of Average Blood Glucose Values Based on Fructosamine Values

IntroductionThe fructosamine is originated of the glycation of plasmatic proteins, especially albumin, in addition to immunoglobulins and proteins diverse. It constitutes an alternative biomarker of glycemic control when glycated hemoglobin is not indicated for this purpose. ObjectiveTo define the mathematical relationship between fructosamine and average glucose values. MethodThe study comprised the laboratorial data collected of 1227 diabetic subjects (type 1 and type 2). Fructosamine levels obtained at the end of three weeks and measured were compared with the average glucose levels of the three previous weeks. The average glucose levels were determined by the weighted mean of the daily fasting capillary glucose results performed during the study period, and the plasma glucose taken at the time of the fructosamine. ResultsA total of 9,450 glucoses were performed. Linear regression analysis between the fructosamine and average glucose levels showed that each increase of 1.0 {micro}mol/L in fructosamine increase 0.5mg/dL in the average glucose levels as evidenced in the equation forward: Average glucose levels = 0.5157 x Fructosamine - 20. According to the coefficient of determination (r2 = 0.353492, P < 0.006881), making it possible to calculate the estimated average glucose according to the frutosamine values. ConclusionFructosamine levels can be expressed as average glucose levels for assessing the metabolic control of diabetic patients.

biochemistry↗

bioRxiv2021

microRNAs Over-expressed in Diabetic Foot Ulcers Healing - Computational Modeling of Molecular Structure

BackgroundVasculopathy associated with diabetic neuropathy are important risk factors for the diabetic foot ulcers development. Diabetic foot ulcers is severe complication that occur in about 15% of people with diabetes, being able require hospitalization and amputation in its treatment. ObjectiveDesign in silico the molecular structure of micro-ribonucleic acid (miRNA) overexpressed in diabetic foot ulcers healing. MethodWe performed a careful search of the nucleotide sequence of 8 miRNAs over-expressed in diabetic foot ulcers, designing in silico the molecular structure of following miRNAs: miRNA-146a, miRNA-155, miRNA-132, miRNA-191, miRNA-21, miRNA-203a, miRNA-203b, and miRNA-210. The nucleotides were taken from GenBank of National Center for Biotechnology Information genetic sequence database. The sequences acquired were aligned with the Clustal W multiple alignment algorithms. The molecular modeling of structures was built using the RNAstructure, an automated miRNAs structure modelling server. ResultsWe showed a search for nucleotide sequence and the design of the molecular structure of following miRNA over-expressed in diabetic foot ulcers healing: miRNA-146a, miRNA-155, miRNA-132, miRNA-191, miRNA-21, miRNA-203a, miRNA-203b, and miRNA-210. We produced a tutorial on a molecular model of the 8 miRNAs overexpressed in the diabetic foot by processing in silico projection of their molecular structures. ConclusionWe show in silico secondary structures design of selected of 8 miRNAs over-expressed in diabetic foot ulcers healing by means of computational biology.

molecular biology↗

bioRxiv2021

Computational modeling of molecular structure of microRNA inhibitors selected against microRNA over-expressed in thyroid cancer

IntroductionThyroid cancer is the most prevalent malignant neoplasm of endocrine system and advances in thyroid molecular biology studies demonstrate that microRNAs (miRNAs) seem to play a fundamental role in tumor triggering and progression. The miRNAs inhibitors are nucleic acid-based molecules that blockade miRNAs function, making unavailable for develop their usual function, also acting as gene expression controlling molecules. ObjectiveTo develop in silico projection of molecular structure of miRNA inhibitors against miRNA over-expressed in thyroid cancer. MethodsWe conducted a search of the nucleotide sequence of 12 miRNAs already defined as inhibitors against miRNA over-expressed in thyroid cancer, realizing in silico projection of the molecular structure of following miRNAs: miRNA-101, miRNA-126, miRNA-126-3p, miRNA-141, miRNA-145, miRNA-146b, miRNA-206, miRNA-3666, miRNA-497, miRNA-539, miRNA-613, and miRNA-618. The nucleotides were selected using GenBank that is the NIH genetic sequence database. The sequences obtained were aligned with the Clustal W multiple alignment algorithms. For the molecular modeling, the structures were generated with the RNAstructure, a fully automated miRNAs structure modelling server, accessible via the Web Servers for RNA Secondary Structure Prediction. ResultsWe demonstrated a search for nucleotide sequence and the projection of the molecular structure of the following miRNA inhibitors against miRNA over-expressed in thyroid cancer: miRNA-101, miRNA-126, miRNA-126-3p, miRNA-141, miRNA-145, miRNA-146b, miRNA-206, miRNA-3666, miRNA-497, miRNA-539, miRNA-613, and miRNA-618. ConclusionIn this study we show in silico secondary structures projection of selected of 12 miRNA inhibitors against miRNA over-expressed in thyroid cancer through computational biology.

cancer biology↗