Ca2+-binding human S100A1 protein is a type of S100 protein. S100A1 is a significant mediator during inflammation when Ca2+ binds to its EF-hand motifs. Receptors for advanced glycation end products (RAGE) correspond to 5 domains: the cytoplasmic, transmembrane, C2, C1, and V domains. The V domain of RAGE is one of the most important target proteins for S100A1. It binds to the hydrophobic surface and triggers signaling transduction cascades that induce cell growth, cell proliferation, and tumorigenesis. We used nuclear magnetic resonance (NMR) spectroscopy to characterize the interaction between S100A1 and the RAGE V domain. We found that S100B could interact with S100A1 via NMR 1H-15N HSQC titrations. We used the HADDOCK program to generate the following two binary complexes based on the NMR titration results: S100A1-RAGE V domain and S100A1-S100B. After overlapping these two complex structures, we found that S100B plays a crucial role in blocking the interaction site between RAGE V domain and S100A1. A cell proliferation assay WST-1 also supported our results. This report could potentially be useful for new protein development for cancer treatment.

COVID-19, caused by the SARS-Cov2, varies greatly in its severity but represent serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals, and remains uncertainty over key aspects of its infectivity, no effective remedy yet exists and this disease causes severe economic effects globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially impact on public opinions in some cases and exacerbate widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topics extracting model (named TClustVID) that analyze COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed to Twitter datasets which enabled exploration of the performance of traditional and TClustVID classification methods. TClustVID showed higher performance compared to the traditional classifiers determined by clustering criteria. Finally, we extracted significant topic clusters from TClustVID, split them into positive, neutral and negative clusters and implemented latent dirichlet allocation for extraction of popular COVID-19 topics. This approach identified common prevailing public opinions and concerns related to COVID-19, as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation.

Summary Utilizing hot electrons generated from localized surface plasmon resonance is of widespread interest in the photocatalysis of metallic nanoparticles. However, hot holes, especially generated from interband transitions, have not been fully explored for photocatalysis yet. In this study, a photocatalyzed Suzuki-Miyaura reaction using mesoporous Pd nanoparticle photocatalyst served as a model to study the role of hot holes. Quantum yields of the photocatalysts increase under shorter wavelength excitations and correlate to “deeper” energy of the holes from the Fermi level. This work suggests that deeper holes in the d -band catalyze the oxidative addition of aryl halide R-X onto Pd 0 at the nanoparticles' surface to form R-Pd II -X complex, thus accelerating the rate-determining step of the catalytic cycle. The hot electrons do not play a decisive role. In the future, catalytic mechanisms induced by deep holes should deserve as much attention as the well-known hot electron transfer mechanism.

In this report, using NMR and molecular modeling, we have studied the structure of lysozyme-S100A6 complex and the influence of tranilast [N-(3, 4-dimethoxycinnamoyl) anthranilic acid], an antiallergic drug which binds to lysozyme, on lysozyme-S100A6 and S100A6-RAGE complex formation and, finally, on cell proliferation. We have found that tranilast may block the S100A6-lysozyme interaction and enhance binding of S100A6 to RAGE. Using WST1 assay, we have found that lysozyme, most probably by blocking the interaction between S100A6 and RAGE, inhibits cell proliferation while tranilast may reverse this effect by binding to lysozyme. In conclusion, studies presented in this work, describing the protein-protein/-drug interactions, are of great importance for designing new therapies to treat diseases associated with cell proliferation such as cancers.

The effect of cooling rate on microstructure and microsegregation of three commercially important magnesium alloys was investigated using Wedge (V-shaped) castings of AZ91D, AM60B, and AE44 alloys. Thermocouples were distributed to measure the cooling rate at six different locations of the wedge casts. Solute redistribution profiles were drawn based on the chemical composition analysis obtained by EDS/WDS analysis. Microstructural and morphological features such as dendrite arm spacing and secondary phase particle size were analyzed using both optical and scanning electron microscopes. Dendritic arm spacing and secondary phase particle size showed an increasing trend with decreasing cooling rate for the three alloys. Area percentage of secondary phase particles decreased with decreasing cooling rate for AE44 alloy. The trend was different for AZ91D and AM60B alloys, for both alloys, area percentage of β-Mg17Al12 increased with decreasing cooling rate up to location 4 and then decreased slightly. The tendency for microsegregation was more severe at slower cooling rates, possibly due to prolonged back diffusion. At slower cooling rate, the minimum concentration of aluminum at the dendritic core was lower compared to faster cooled locations. The segregation deviation parameter and the partition coefficient were calculated from the experimentally obtained data.