Type 1 diabetes mellitus (T1DM) represents one of the most frequent chronic illnesses affecting children. The early diagnosis of this disease is crucial, as it plays a key role in preventing the development of a life-threatening acute complication: diabetic ketoacidosis. The etiopathogenetic role of viral infections has long been suggested and emerging data are pointing towards a complex bidirectional relationship between diabetes and COVID-19. The aim of this study is to assess the impact of the COVID-19 pandemic on the incidence and severity of new T1DM cases in children in Romania. We analyzed the differences between a group of 312 patients diagnosed with T1DM in the period 2003–2019 and a group of 147 children diagnosed during the pandemic. The data were investigated using statistical analysis of a series of relevant variables. The total number of newly diagnosed T1DM increased by 30.08% in the period March 2020–February 2021 compared to the previous years. The patients in the pandemic group had a higher mean age at the onset of T1DM, were less frequently living in an urban area, and presented a higher mean value of HbA1c. Diabetic ketoacidosis at the onset of T1DM was 67.40% more frequent, and a higher percentage of these patients presented with a severe form. The duration of T1DM symptoms did not differ significantly between the two groups. A number of 8 patients associated SARS-CoV-2 infection at the time of T1DM diagnosis.
The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.
Diabetes represents a major health problem, involving a severe imbalance of blood sugar levels, which can disturb the nerves, eyes, kidneys, and other organs. Diabes management involves several synthetic drugs focused on improving insulin sensitivity, increasing insulin production, and decreasing blood glucose levels, but with unclear molecular mechanisms and severe side effects. Natural chemicals extracted from several plants such as Gymnema sylvestre, Momordica charantia or Ophiopogon planiscapus Niger have aroused great interest for their anti-diabetes activity, but also their hypolipidemic and anti-obesity activity. Here, we focused on the anti-diabetic activity of a few natural and synthetic compounds, in correlation with their pharmacokinetic/pharmacodynamic profiles, especially with their blood-brain barrier (BBB) permeability. We reviewed studies that used bioinformatics methods such as predicted BBB, molecular docking, molecular dynamics and quantitative structure-activity relationship (QSAR) to elucidate the proper action mechanisms of antidiabetic compounds. Currently, it is evident that BBB damage plays a significant role in diabetes disorders, but the molecular mechanisms are not clear. Here, we presented the efficacy of natural (gymnemic acids, quercetin, resveratrol) and synthetic (TAK-242, propofol, or APX3330) compounds in reducing diabetes symptoms and improving BBB dysfunctions. Bioinformatics tools can be helpful in the quest for chemical compounds with effective anti-diabetic activity that can enhance the druggability of molecular targets and provide a deeper understanding of diabetes mechanisms.
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