Histamine and Histamine H4 Receptor Promotes Osteoclastogenesis in Rheumatoid Arthritis.

Histamine H4 receptor (H4R) has immune-modulatory and chemotaxic effects in various immune cells. This study aimed to determine the osteoclastogenic role of H4R in rheumatoid arthritis (RA). The concentration of histamine in synovial fluid (SF) and sera in patients with RA was measured using ELISA. After RA SF and peripheral blood (PB) CD14+ monocytes were treated with histamine, IL-17, IL-21 and IL-22, and a H4R antagonist (JNJ7777120), the gene expression H4R and RANKL was determined by real-time PCR. Osteoclastogenesis was assessed by counting TRAP-positive multinucleated cells in PB CD14+ monocytes cultured with histamine, Th17 cytokines and JNJ7777120. SF and serum concentration of histamine was higher in RA, compared with osteoarthritis and healthy controls. The expression of H4R was increased in PB monocytes in RA patients. Histamine, IL-6, IL-17, IL-21 and IL-22 induced the expression of H4R in monocytes. Histamine, IL-17, and IL-22 stimulated RANKL expression in RA monocytes and JNJ7777120 reduced the RANKL expression. Histamine and Th17 cytokines induced the osteoclast differentiation from monocytes and JNJ7777120 decreased the osteoclastogenesis. H4R mediates RANKL expression and osteoclast differentiation induced by histamine and Th17 cytokines. The blockage of H4R could be a new therapeutic modality for prevention of bone destruction in RA

Cut-Based Graph Learning Networks to Discover Compositional Structure of Sequential Video Data

Conventional sequential learning methods such as Recurrent Neural Networks (RNNs) focus on interactions between consecutive inputs, i.e. first-order Markovian dependency. However, most of sequential data, as seen with videos, have complex dependency structures that imply variable-length semantic flows and their compositions, and those are hard to be captured by conventional methods. Here, we propose Cut-Based Graph Learning Networks (CB-GLNs) for learning video data by discovering these complex structures of the video. The CB-GLNs represent video data as a graph, with nodes and edges corresponding to frames of the video and their dependencies respectively. The CB-GLNs find compositional dependencies of the data in multilevel graph forms via a parameterized kernel with graph-cut and a message passing framework. We evaluate the proposed method on the two different tasks for video understanding: Video theme classification (Youtube-8M dataset) and Video Question and Answering (TVQA dataset). The experimental results show that our model efficiently learns the semantic compositional structure of video data. Furthermore, our model achieves the highest performance in comparison to other baseline methods.Comment: 8 pages, 3 figures, Association for the Advancement of Artificial Intelligence (AAAI2020). arXiv admin note: substantial text overlap with arXiv:1907.0170

How Well Do Large Language Models Truly Ground?

Reliance on the inherent knowledge of Large Language Models (LLMs) can cause issues such as hallucinations, lack of control, and difficulties in integrating variable knowledge. To mitigate this, LLMs can be probed to generate responses by grounding on external context, often given as input (knowledge-augmented models). Yet, previous research is often confined to a narrow view of the term "grounding", often only focusing on whether the response contains the correct answer or not, which does not ensure the reliability of the entire response. To address this limitation, we introduce a strict definition of grounding: a model is considered truly grounded when its responses (1) fully utilize necessary knowledge from the provided context, and (2) don't exceed the knowledge within the contexts. We introduce a new dataset and a grounding metric to assess this new definition and perform experiments across 13 LLMs of different sizes and training methods to provide insights into the factors that influence grounding performance. Our findings contribute to a better understanding of how to improve grounding capabilities and suggest an area of improvement toward more reliable and controllable LLM applications

Wnt3a upregulates brain-derived insulin by increasing NeuroD1 via Wnt/beta-catenin signaling in the hypothalamus

Background: Insulin plays diverse roles in the brain. Although insulin produced by pancreatic β-cells that crosses the blood-brain barrier is a major source of brain insulin, recent studies suggest that insulin is also produced locally within the brain. However, the mechanisms underlying the production of brain-derived insulin (BDI) are not yet known. Results: Here, we examined the effect of Wnt3a on BDI production in a hypothalamic cell line and hypothalamic tissue. In N39 hypothalamic cells, Wnt3a treatment significantly increased the expression of the Ins2 gene, which encodes the insulin isoform predominant in the mouse brain, by activating Wnt/β-catenin signaling. The concentration of insulin was higher in culture medium of Wnt3a-treated cells than in that of untreated cells. Interestingly, neurogenic differentiation 1 (NeuroD1), a target of Wnt/β-catenin signaling and one of transcription factors for insulin, was also induced by Wnt3a treatment in a time- and dose-dependent manner. In addition, the treatment of BIO, a GSK3 inhibitor, also increased the expression of Ins2 and NeuroD1. Knockdown of NeuroD1 by lentiviral shRNAs reduced the basal expression of Ins2 and suppressed Wnt3a-induced Ins2 expression. To confirm the Wnt3a-induced increase in Ins2 expression in vivo, Wnt3a was injected into the hypothalamus of mice. Wnt3a increased the expression of NeuroD1 and Ins2 in the hypothalamus in a manner similar to that observed in vitro. Conclusion: Taken together, these results suggest that BDI production is regulated by the Wnt/β-catenin/NeuroD1 pathway in the hypothalamus. Our findings will help to unravel the regulation of BDI production in the hypothalamus.1

IL-17 induces production of IL-6 and IL-8 in rheumatoid arthritis synovial fibroblasts via NF-κB- and PI3-kinase/Akt-dependent pathways

Recent studies of the pathogenesis of rheumatoid arthritis (RA) have revealed that both synovial fibroblasts and T cells participate in the perpetuation of joint inflammation as dynamic partners in a mutual activation feedback, via secretion of cytokines and chemokines that stimulate each other. In this study, we investigated the role of IL-17, a major Th1 cytokine produced by activated T cells, in the activation of RA synovial fibroblasts. Transcripts of IL-17R (IL-17 receptor) and IL-17RB (IL-17 receptor B) were present in fibroblast-like synoviocytes (FLS) of RA patients. IL-17R responded with increased expression upon in vitro stimulation with IL-17, while the level of IL-17RB did not change. IL-17 enhanced the production of IL-6 and IL-8 in FLS, as previously shown, but did not affect the synthesis of IL-15. IL-17 appears to be a stronger inducer of IL-6 and IL-8 than IL-15, and even exerted activation comparable to that of IL-1β in RA FLS. IL-17-mediated induction of IL-6 and IL-8 was transduced via activation of phosphatidylinositol 3-kinase/Akt and NF-κB, while CD40 ligation and p38 MAPK (mitogen-activated protein kinase) are not likely to partake in the process. Together these results suggest that IL-17 is capable of more than accessory roles in the activation of RA FLS and provide grounds for targeting IL-17-associated pathways in therapeutic modulation of arthritis inflammation

Hexa: Self-Improving for Knowledge-Grounded Dialogue System

A common practice in knowledge-grounded dialogue generation is to explicitly utilize intermediate steps (e.g., web-search, memory retrieval) with modular approaches. However, data for such steps are often inaccessible compared to those of dialogue responses as they are unobservable in an ordinary dialogue. To fill in the absence of these data, we develop a self-improving method to improve the generative performances of intermediate steps without the ground truth data. In particular, we propose a novel bootstrapping scheme with a guided prompt and a modified loss function to enhance the diversity of appropriate self-generated responses. Through experiments on various benchmark datasets, we empirically demonstrate that our method successfully leverages a self-improving mechanism in generating intermediate and final responses and improves the performances on the task of knowledge-grounded dialogue generation

Increased interleukin-17 production via a phosphoinositide 3-kinase/Akt and nuclear factor κB-dependent pathway in patients with rheumatoid arthritis

Inflammatory mediators have been recognized as being important in the pathogenesis of rheumatoid arthritis (RA). Interleukin (IL)-17 is an important regulator of immune and inflammatory responses, including the induction of proinflammatory cytokines and osteoclastic bone resorption. Evidence for the expression and proinflammatory activity of IL-17 has been demonstrated in RA synovium and in animal models of RA. Although some cytokines (IL-15 and IL-23) have been reported to regulate IL-17 production, the intracellular signaling pathways that regulate IL-17 production remain unknown. In the present study, we investigated the role of the phosphoinositide 3-kinase (PI3K)/Akt pathway in the regulation of IL-17 production in RA. Peripheral blood mononuclear cells (PBMC) from patients with RA (n = 24) were separated, then stimulated with various agents including anti-CD3, anti-CD28, phytohemagglutinin (PHA) and several inflammatory cytokines and chemokines. IL-17 levels were determined by sandwich enzyme-linked immunosorbent assay and reverse transcription–polymerase chain reaction. The production of IL-17 was significantly increased in cells treated with anti-CD3 antibody with or without anti-CD28 and PHA (P < 0.05). Among tested cytokines and chemokines, IL-15, monocyte chemoattractant protein-1 and IL-6 upregulated IL-17 production (P < 0.05), whereas tumor necrosis factor-α, IL-1β, IL-18 or transforming growth factor-β did not. IL-17 was also detected in the PBMC of patients with osteoarthritis, but their expression levels were much lower than those of RA PBMC. Anti-CD3 antibody activated the PI3K/Akt pathway; activation of this pathway resulted in a pronounced augmentation of nuclear factor κB (NF-κB) DNA-binding activity. IL-17 production by activated RA PBMC is completely or partly blocked in the presence of the NF-κB inhibitor pyrrolidine dithiocarbamate and the PI3K/Akt inhibitor wortmannin and LY294002, respectively. However, inhibition of activator protein-1 and extracellular signal-regulated kinase 1/2 did not affect IL-17 production. These results suggest that signal transduction pathways dependent on PI3K/Akt and NF-κB are involved in the overproduction of the key inflammatory cytokine IL-17 in RA