CS12192, a Novel JAK3/JAK1/TBK1 Inhibitor,Synergistically Enhances the Anti-Inflammation Effect of Methotrexate in a Rat Model of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a common disease worldwide and is treated commonly with methotrexate (MTX). CS12192 is a novel JAK3 inhibitor discovered by Chipscreen Biosciences for the treatment of autoimmune diseases. In the present study, we examined the therapeutic effect of CS12192 against RA and explored if the combinational therapy of CS12192 and MTX produced a synergistic effect against RA in rat collagen-induced arthritis (CIA). Arthritis was induced in male Sprague-Dawley rats by two intradermal injections of bovine type II collagen (CII) and treated with MTX, CS12192, or the combination of CS12192 and MTX daily for two weeks. Effects of different treatments on arthritis score, X-ray score, pathology, and expression of inflammatory cytokines and biomarkers were examined. We found that treatment with either CS12192 or MTX produced a comparable therapeutic effect on CIA including: (1) significantly lowering the arthritis score, X-ray score, serum levels of rheumatic factor (RF), C-reactive protein (CRP), and anti-nuclear antibodies (ANA); (2) largely alleviating histopathological damage, reducing infiltration of Th17 cells while promoting Treg cells; (3) inhibiting the expression of inflammatory cytokines and chemokines such as IL-1β, TNF-α, IL-6, CCL2, and CXCL1. All these inhibitory effects were further improved by the combinational therapy with MTX and CS12192. Of importance, the combinational treatment also resulted in a marked switching of the Th17 to Treg and the M1 to M2 immune responses in synovial tissues of CIA. Thus, when compared to the monotherapy, the combination treatment with CS12192 and MTX produces a better therapeutic effect against CIA with a greater suppressive effect on T cells and macrophage-mediated joint inflammation.     

AAV13 Enables Precise Targeting of Local Neural Populations

As powerful tools for local gene delivery, adeno-associated viruses (AAVs) are widely used for neural circuit studies and therapeutical purposes. However, most of them have the characteristics of large diffusion range and retrograde labeling, which may result in off-target transduction during in vivo application. Here, in order to achieve precise gene delivery, we screened AAV serotypes that have not been commonly used as gene vectors and found that AAV13 can precisely transduce local neurons in the brain, with a smaller diffusion range than AAV2 and rigorous anterograde labeling. Then, AAV13-based single-viral and dual-viral strategies for sparse labeling of local neurons in the brains of C57BL/6 or Cre transgenic mice were developed. Additionally, through the neurobehavioral test in the ventral tegmental area, we demonstrated that AAV13 was validated for functional monitoring by means of carrying Cre recombinase to drive the expression of Cre-dependent calcium-sensitive indicator. In summary, our study provides AAV13-based toolkits for precise local gene delivery, which can be used for in situ small nuclei targeting, sparse labeling and functional monitoring.     

Antioxidant and Antibacterial Effects of Potential Probiotics Isolated from Korean Fermented Foods

A total of sixteen bacterial strains were isolated and identified from the fourteen types of Korean fermented foods that were evaluated for their in vitro probiotic potentials. The results showed the highest survivability for Bacillus sp. compared to Lactobacillus sp. in simulated gastric pH, and it was found to be maximum for B. inaquosorum KNUAS016 (8.25 ± 0.08 log10 CFU/mL) and minimum for L. sakei KNUAS019 (0.8 ± 0.02 log10 CFU/mL) at 3 h of incubation. Furthermore, B. inaquosorum KNUAS016 and L. brevis KNUAS017 also had the highest survival rates of 6.86 ± 0.02 and 5.37 ± 0.01 log10 CFU/mL, respectively, in a simulated intestinal fluid condition at 4 h of incubation. The percentage of autoaggregation at 6 h for L. sakei KNUAS019 (66.55 ± 0.33%), B. tequilensis KNUAS015 (64.56 ± 0.14%), and B. inaquosorum KNUAS016 (61.63 ± 0.19%) was >60%, whereas it was lower for L. brevis KNUAS017 (29.98 ± 0.09%). Additionally, B. subtilis KNUAS003 showed higher coaggregation at 63.84 ± 0.19% while B. proteolyticus KNUAS001 found at 30.02 ± 0.33%. Among them, Lactobacillus sp. showed the best non-hemolytic activity. The highest DPPH and ABTS radical scavenging activity was observed in L. sakei KNUAS019 (58.25% and 71.88%). The cell-free supernatant of Lactobacillus sp. considerably inhibited pathogenic growth, while the cell-free supernatant of Bacillus sp. was moderately inhibited when incubated for 24 h. However, the overall results found that B. subtilis KNUAS003, B. proteolyticus KNUAS012, L. brevis KNUAS017, L. graminis KNUAS018, and L. sakei KNUAS019 were recognized as potential probiotics through different functional and toxicity assessments.     

The Role of Matrix Metalloproteinase in Inflammation with a Focus on Infectious Diseases

Matrix metalloproteinases (MMPs) are involved in extracellular matrix remodeling through the degradation of extracellular matrix components and are also involved in the inflammatory response by regulating the pro-inflammatory cytokines TNF-α and IL-1β. Dysregulation in the inflammatory response and changes in the extracellular matrix by MMPs are related to the development of various diseases including lung and cardiovascular diseases. Therefore, numerous studies have been conducted to understand the role of MMPs in disease pathogenesis. MMPs are involved in the pathogenesis of infectious diseases through a dysregulation of the activity and expression of MMPs. In this review, we discuss the role of MMPs in infectious diseases and inflammatory responses. Furthermore, we present the potential of MMPs as therapeutic targets in infectious diseases.     

数据驱动的航天器故障诊断研究现状及挑战

总结了基于数据驱动的故障诊断技术理论、方法及其在航天器上的应用现状,指出其面临的挑战并提出一种解决方案。将基于数据驱动的故障诊断方法按照技术发展顺序分为基于传统机器学习的方法、基于深度学习的方法以及基于迁移学习的方法。基于传统机器学习的方法需要大量的人工参与和丰富的专家经验,在小样本数据上有着优异的性能,但不适合处理大数据。在大数据背景下,重点介绍了卷积神经网络、循环神经网络、深度自编码器以及深度信念网络的基本概念以及原理,对其在航天器故障诊断领域的研究现状进行了阐述和总结。针对深度学习严重依赖于带标签数据这一问题,介绍了基于迁移学习的故障诊断技术,并提出适应航天器应用的场景,为数据驱动的故障诊断技术工程应用提供了一种方法和思路。     

法国贝尔纳尔丹铀矿床热液蚀变与成矿特征研究

在总结法国贝尔纳尔丹花岗岩型铀矿床蚀变类型、矿物组合、矿物生成顺序等矿化特征基础上,对矿床的蚀变特征和成矿模式进行了探讨。该矿床含矿围岩分别为英云闪长岩-花岗闪长岩(G型)和中-粗粒过铝质二云母浅色花岗岩(L型),其中L型花岗岩为含矿围岩。矿床中与铀矿化密切相关的蚀变类型较多,最典型的当属由富18 O流体进入花岗岩裂隙形成的变正长岩化蚀变,使花岗岩更为碎裂,增加了成矿空间并在之后的流体作用下发生再次蚀变,形成伊利石-蒙脱石并吸附了铀石,伊利石化-蒙脱石化越强烈,铀矿化程度越高。铀矿化是两次蚀变的产物,矿体的形态不仅受变正长岩中形成的裂隙构造所控制,同时矿化还受流经变正长岩的流体和表生作用的控制。