Institute of Organic Chemistry, Faculty of Chemistry, Lodz University of Technology, Zeromskiego 116, 90-924 Lodz, Poland.
Published: September 2020
AI Article Synopsis
Article Abstract
The purpose of this study was to investigate whether Human Serum Albumin (HSA) can bind native human insulin and its A13-A19 and B12-B17 fragments, which are responsible for the aggregation of the whole hormone. To label the hormone and both hot spots, so that their binding positions within the HSA could be identified, 4-(1-pyrenyl)butyric acid was used as a fluorophore. Triazine coupling reagent was used to attach the 4-(1-pyrenyl)butyric acid to the N-terminus of the peptides. When attached to the peptides, the fluorophore showed extended fluorescence lifetimes in the excited state in the presence of HSA, compared to the samples in buffer solution. We also analyzed the interactions of unlabeled native insulin and its hot spots with HSA, using circular dichroism (CD), the microscale thermophoresis technique (MST), and three independent methods recommended for aggregating peptides. The CD spectra indicated increased amounts of the α-helical secondary structure in all analyzed samples after incubation. Moreover, for each of the two unlabeled hot spots, it was possible to determine the dissociation constant in the presence of HSA, as 14.4 µM (A13-A19) and 246 nM (B12-B17). Congo Red, Thioflavin T, and microscopy assays revealed significant differences between typical amyloids formed by the native hormone or its hot-spots and the secondary structures formed by the complexes of HSA with insulin and A13-A19 and B12-B17 fragments. All results show that the tested peptide-probe conjugates and their unlabeled analogues interact with HSA, which inhibits their aggregation.
This study expands traditional reaction-diffusion models by incorporating hyperbolic dynamics to explore the effects of inertial delays on pattern formation. The kinetic system considers a harvested predator-prey model where predator and prey populations gather in herds. Diffusion and inertial effects are subsequently introduced.
A highly sensitive aptamer sensor (aptasensor) is proposed based on metal-organic frameworks-silver nanoparticles (AgNPs@MOF) to detect sulfadimethoxine (SDM) by surface-enhanced Raman spectroscopy (SERS). AgNPs@MOF with SERS activity was successfully fabricated by synthesizing AgNPs in situ on the surface of MIL-101(Fe), and SDM aptamer and Raman reporter 4-aminophenthiophenol (4-ATP) were selected as specific recognition elements and signal probes, respectively. When SDM was absent, the SDM aptamers were effectively adsorbed on the surface of AgNPs@MOF, thus keeping AgNPs@MOF in a dispersed state, resulting in a weakened SERS signal of 4-ATP.
Background: Diabetes has emerged as the foremost public health challenge of the 21st century, with a notable shift towards managing it through an inflammatory lens. This study seeks to investigate the role of NLRP3 infalmmasome in diabetes over the past ten years, leveraging bibliometric analysis to pinpoint prevailing trends, underscore critical focal points, and establish a roadmap for subsequent research endeavors.
Method: A literature search was conducted based on the SCI-E database, and all recorded results were downloaded in plain text format for subsequent analysis.
Complex-Langevin field-theoretic simulations (CL-FTSs) provide an approximation-free method of calculating fluctuation corrections to the self-consistent field theory (SCFT) of block copolymer melts. However, the complex fields are prone to the formation of hot spots, which causes the method to fail. This problem has been attributed to an invariance under complex translations, which allows the system to drift away from the real-valued saddle-point of SCFT.
Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail.
