Astragaloside IV attenuates renal tubule injury in DKD rats via suppression of CD36-mediated NLRP3 inflammasome activation

Background:In recent years, diabetic kidney disease (DKD) has emerged as a prominent factor contributing to end-stage renal disease. Tubulointerstitial inflammation and lipid accumulation have been identified as key factors in the development of DKD. Earlier research indicated that Astragaloside IV (AS-IV) reduces inflammation and oxidative stress, controls lipid accumulation, and provides protection to the kidneys. Nevertheless, the mechanisms responsible for its protective effects against DKD have not yet been completely elucidated.Purpose:The primary objective of this research was to examine the protective properties of AS-IV against DKD and investigate the underlying mechanism, which involves CD36, reactive oxygen species (ROS), NLR family pyrin domain containing 3 (NLRP3), and interleukin-1β (IL-1β).Methods:The DKD rat model was created by administering streptozotocin along with a high-fat diet. Subsequently, the DKD rats and palmitic acid (PA)-induced HK-2 cells were treated with AS-IV. Atorvastatin was used as the positive control. To assess the therapeutic effects of AS-IV on DKD, various tests including blood sugar levels, the lipid profile, renal function, and histopathological examinations were conducted. The levels of CD36, ROS, NLRP3, Caspase-1, and IL-1β were detected using western blot analysis, PCR, and flow cytometry. Furthermore, adenovirus-mediated CD36 overexpression was applied to explore the underlying mechanisms through in vitro experiments.Results:In vivo experiments demonstrated that AS-IV significantly reduced hyperglycemia, dyslipidemia, urinary albumin excretion, and serum creatinine levels in DKD rats. Additionally, it improved renal structural abnormalities and suppressed the expression of CD36, NLRP3, IL-1β, TNF-α, and MCP-1. In vitro experiments showed that AS-IV decreased CD36 expression, lipid accumulation, and lipid ROS production while inhibiting NLRP3 activation and IL-1β secretion in PA-induced HK-2 cells.Conclusion:AS-IV alleviated renal tubule interstitial inflammation and tubule epithelial cell apoptosis in DKD rats by inhibiting CD36-mediated lipid accumulation and NLRP3 inflammasome activation

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Algorithm Selection for Protein-Ligand Docking: A Case Study on ACE with AutoDock

The present study investigates the use of algorithm selection for automatically choosing an algorithm for any given protein-ligand docking task. In drug discovery and design process, conceptualizing protein-ligand binding is a major problem. Targeting this problem through computational methods is beneficial in order to substantially reduce the resource and time requirements for the overall drug development process. One way of addressing protein-ligand docking is to model it as a search and optimization problem. There have been a variety of algorithmic solutions in this respect. However, there is no ultimate algorithm that can efficiently tackle this problem, both in terms of protein-ligand docking quality and speed. This argument motivates devising new algorithms, tailored to the particular protein-ligand docking scenarios. To this end, this paper reports a machine learning-based approach for improved and robust docking performance. The proposed set-up is fully automated, operating without any expert opinion or involvement both on the problem and algorithm aspects. As a case study, an empirical analysis was performed on a well-known protein, Human Angiotensin-Converting Enzyme (ACE), with 1428 ligands. For general applicability, AutoDock 4.2 was used as the docking platform. The candidate algorithms are also taken from AutoDock 4.2. Twenty-eight distinctly configured Lamarckian-Genetic Algorithm (LGA) are chosen to build an algorithm set. ALORS which is a recommender system-based algorithm selection system was preferred for automating the selection from those LGA variants on a per-instance basis. For realizing this selection automation, molecular descriptors and substructure fingerprints were employed as the features characterizing each target protein-ligand docking instance. The computational results revealed that algorithm selection outperforms all those candidate algorithms. Further assessment is reported on the algorithms space, discussing the contributions of LGA’s parameters. As it pertains to protein-ligand docking, the contributions of the aforementioned features are examined, which shed light on the critical features affecting the docking performance

Resonant X-ray excitation of the nuclear clock isomer 45Sc

Resonant oscillators with stable frequencies and large quality factors help us to keep track of time with high precision. Examples range from quartz crystal oscillators in wristwatches to atomic oscillators in atomic clocks, which are, at present, our most precise time measurement devices1. The search for more stable and convenient reference oscillators is continuing2,3,4,5,6. Nuclear oscillators are better than atomic oscillators because of their naturally higher quality factors and higher resilience against external perturbations7,8,9. One of the most promising cases is an ultra-narrow nuclear resonance transition in 45Sc between the ground state and the 12.4-keV isomeric state with a long lifetime of 0.47 s (ref. 10). The scientific potential of 45Sc was realized long ago, but applications require 45Sc resonant excitation, which in turn requires accelerator-driven, high-brightness X-ray sources11 that have become available only recently. Here we report on resonant X-ray excitation of the 45Sc isomeric state by irradiation of Sc-metal foil with 12.4-keV photon pulses from a state-of-the-art X-ray free-electron laser and subsequent detection of nuclear decay products. Simultaneously, the transition energy was determined as with an uncertainty that is two orders of magnitude smaller than the previously known values. These advancements enable the application of this isomer in extreme metrology, nuclear clock technology, ultra-high-precision spectroscopy and similar applications