RESUMO
Carrageenan (CG) and ion exchange resins (IERs) are better metal chelators. Kappa (κ) CG and IERs were synthesized and subjected to copper ion (Cu2+) adsorption to obtain DMSCH/κ-Cu, DC20H/κ-Cu, and IRP69H/κ-Cu nanocomposites (NCs). The NCs were studied using statistical physics formalism (SPF) at 315-375 K and a multilayer perceptron with five input nodes. The percentage of Cu2+ uptake efficiency was used as an outcome variable. Via the grand canonical ensemble, SPF gives models for both monolayer and multilayer sorption layers. For in vitro anticoagulant activity (ACA), the activated partial thromboplastin time were calculated using 100 µL of rabbit plasma incubated at 37 °C. After 2 min, 100 L of 0.025 M CaCl2 was added, and the clotting time was recorded for each group (n = 6). The results demonstrated that the key covariables for the adsorption process were pH and concentration. The results of artificial neural network models were comparable with the experimental findings. The error rates varied between 4.3 and 1.0%. The prediction analysis results ranged from 43.6 to 89.2. The ΔG and ΔS values for IRP69H/κ-Cu obtained were -18.91 and -16.32 and 26.21 and 22.74 kJ/mol for the temperatures 315 and 345 K, respectively. Adsorbate species were perpendicular to the adsorbent surfaces, notwithstanding the apparent importance of macro- and micropore volumes. These adsorbents typically fluctuate with temperature changes and contain one or more layers of sorption. Negative and positive sorption energies correspond to endothermic and exothermic processes. The biosorption energy (E1 and E2) values in this experiment have a value of less than 23 kJ mol-1. Complex SPF models' energy distributions validate surface properties and interactions with adsorbates. At a concentration of 100 µg/mL, DC20H/κ-Cu2+ exhibited an ACA of only 8 s. These NCs demonstrated better greater ACA with the order DC20H/κ < DMSCH/κ < IRP69H/κ. More research is needed to rule out the chemical processes behind the ACA of CG/IER-Cu NCs.
RESUMO
Aims: To investigate the role of phosphorylation in SARS-CoV-2 infection, potential therapeutic targets and its harmful genetic sequences. Materials & Methods: Data mining techniques were employed to identify upregulated kinases responsible for proteomic changes induced by SARS-CoV-2. Spike and nucleocapsid proteins' sequences were analyzed using predictive tools, including SNAP2, MutPred2, PhD-SNP, SNPs&Go, MetaSNP, Predict-SNP and PolyPhen-2. Missense variants were identified using ensemble-based algorithms and homology/structure-based models like SIFT, PROVEAN, Predict-SNP and MutPred-2. Results: Eight missense variants were identified in viral sequences. Four damaging variants were found, with SNPs&Go and PolyPhen-2. Promising therapeutic candidates, including gilteritinib, pictilisib, sorafenib, RO5126766 and omipalisib, were identified. Conclusion: This research offers insights into SARS-CoV-2 pathogenicity, highlighting potential treatments and harmful variants in viral proteins.
This study explores the process called phosphorylation, which involves adding phosphate groups to certain proteins, influences the way the SARS-CoV-2 virus causes disease. The virus manipulates host enzymes to help it spread and survive. Researchers used data analysis techniques to identify the proteins that play a role in this process, aiming to find potential targets for treatments. They analyzed genetic sequences of key virus proteins and used various tools to predict harmful mutations. The study found several promising compounds that could be used to target the virus. Further research and experiments are needed to confirm their effectiveness as COVID-19 treatments.
This research explored the process called phosphorylation, which involves adding certain molecules to proteins, affects how the SARS-CoV-2 virus makes people sick. The virus uses our own cell's machinery to help it spread. Researchers used computer analysis to find out which proteins are involved in this process, hoping to find new ways to treat COVID-19. They studied the genetic code of important parts of the virus and used computer programs to predict if there were harmful changes in the code. They found some potential medicines that could be used to fight the virus and reduce its harm, but more research and testing are needed to be sure.
