Objective: To define the mathematical relationship between fructosamine levels and average glucose values. Subjects and methods: The study comprised laboratory data of 1,227 patients with type 1 or 2 diabetes mellitus. Fructosamine levels measured at the end of a 3-week period were compared against the average blood glucose levels of the previous 3 weeks. Average glucose levels were determined by the weighted average of the daily fasting capillary glucose results performed during the study period, and the plasma glucose measured in the same sample collected for fructosamine measurement. Results: In total, 9,450 glucose measurements were performed. Linear regression analysis between fructosamine levels and average glucose levels showed that for each 1.0 µmol/L increase in fructosamine level there was a 0.5 mg/dL increase in average glucose level, as estimated by the equation Mean glucose level = (0.5157 x Fructosamine) -20. The coefficient of determination (r2 = 0.353492, p < 0.006881) allowed the calculation of the estimated average glucose based on fructosamine level. Conclusion: Our study demonstrated a linear correlation between fructosamine level and mean blood glucose level, suggesting that fructosamine levels can be a proxy for the average glucose level in assessing the metabolic control of patients with diabetes.

Objective To evaluate the possible similarity between the AA sequences of human insulin and human glutamic acid decarboxylase-65 (GAD65) with the SARS-CoV-2/COVID proteins to explain the possible trigger of DM1. Methods AA sequences of human insulin, GAD65 and SARS-CoV-2 were obtained from the Protein Data Bank archive information database (RCSB PDB). NetMHCpan v4.1 was used for epitope prediction. Sequences were compared using BLAST for epitope comparison and Pairwise Structure Alignment to assess protein similarity. The AA sequences of human insulin (4F0N) and GAD65 (2OKK) were compared with the sequences of the following SARS-CoV-2 proteins: SARS-Cov2 S protein at open state (7DDN), SARS-Cov2 S protein at close state (7DDD), SARS CoV-2 Spike protein (6ZB5), Crystal structure of SARS-CoV-2 nucleocapsid protein N-terminal RNA binding domain (6M3M), Crystal structure of SARS-CoV-2 nucleocapsid protein C-terminal RNA binding domain (7DE1), Crystal structure of NSP1 from SARS-CoV-2 (7K3N), and SARS-CoV-2 S trimer (7DK3)). Results The percent similarity between epitopes ranged from 45 to 60% (P 0.048) between both human insulin and SARS-CoV2 and for GAD 65 and SARS-CoV2, while the AA similarity of the evaluated samples ranged from 5.00–45.45% between human insulin and SARS-CoV2 and from 10.45–22.22% between GAD65 and SARS-CoV2. Conclusion Immunoinformatics data suggest a potential pathogenic link between SARS-CoV-2/COVID and DM1. Thus, by molecular mimicry, we found that sequence similarity between epitopes and AA sequence between SARS-CoV-2 / COVID and human insulin and GAD65 could lead to the production of an immune cross-response to self-antigens, with self-tolerance breakdown, which could thus trigger DM1.

Bile acids (BAs) are steroid molecules that have a hydrophilic and a hydrophobic end, and are synthesized exclusively in the liver, being end product of cholesterol catabolism. Type 2 diabetes mellitus (DM2) is a chronic degenerative disease, with a pathophysiology characterized by insulin resistance (IR), insulin deficiency due to insufficient production of pancreatic ß-cells, and elevated serum glucose levels leading to multiple complications. BAs are related to several metabolic alterations, including metabolic syndrome and DM2. It is currently known that BAs act as a ligand for the nuclear farnesoid X receptor, a receptor with an important role in glucose metabolism, lipids and cellular energy production, as well as in the regulation of production, elimination and mobilization of BAs. BAs have also been reported to act as a signaling pathway through of Takeda G protein-coupled receptor 5. In this manuscript, we describe the interface between BAs and metabolic disorders, in particular DM2, including discussing possibilities in the development of therapeutic procedures targeting BAs as an optional pathway in the treatment of DM2.

Introduction: Thyroid Imaging Reporting and Data System (TI-RADS) is a system for classifying thyroid nodules detected at ultrasonography, aiming at a descriptive standardization as well as to classify their risk of malignancy based on sonographic findings. Aium: To present a pictorial essay by composing anatomical, histological, ultrasound and elastography images of the TI-RADS score. Method: Using software for image composition, based on the ultrasound image of the various types of thyroid nodules, we adapt to the anatomical image, the elastosonography, and histological corresponding TI-RADS and present a pictorial essay. Results: The correlation between the sonographic, elastography, anatomical, cytology and histological images corresponding to the TI-RADS score are demonstrated. Conclusion: Ultrasonographic features, elastosonography, anatomical, and histological in evaluation of thyroid nodules can correlate with features TI-RADS score.

Introduction The halogens are the non-metallic chemical elements belonging to group 17 of the Periodic Table, namely: fluorine, chlorine, bromine, iodine, astate, and teness. Halogens are biologically atypical components, however are frequent as replacement in the binders of the thyroid hormones and inhibitors, binding precisely to nucleic acids and proteins. Objective Simulate in sílico and through a mathematical model the interactions between the ionic changes in the thyroxine (T4) molecule in the process of autoimmunity induction. Methods We used an online application to simulate the docking of fluorine, chlorine, and bromine in the T4 molecule in place of iodine. A hypothetical-deductive mathematical model was assembled to evaluate halogen substitution in the T4 molecule and immune system and its correlation with the development of autoimmune thyroiditis. Results Simulation of the coupling of fluorine, chlorine and bromine, instead of iodine, to T4 were successful using the induced fit docking program. Positioning of each halogen ion in replacing the iodine at position 5 of T4 was achieved. The mathematical model used demonstrated that the change of the halogen ion in the T4 molecule has been shown to be the trigger for the autoimmune trigger of thyroiditis. Conclusion The findings from this study suggest that halogens of lower atomic weight than iodine may act as a trigger for the onset of autoimmune thyroiditis.