ABSTRACTABSTRACTIntroduction Fibrosis is a disease that damages organs and even causes death. Because of the complicated pathogenesis, the development of drugs for fibrosis is challenging. In the lysophosphatidic acid receptor type 1 (LPA1) signaling pathway, LPA1 and its downstream Rho-associated coiled-coil forming protein kinase (ROCK) are related to the process of fibrosis. Targeting LPA1 signaling pathway is a potential strategy for the treatment of fibrosis.Area covered This review describes the process of fibrosis mediated by the LPA1 signaling pathway and then summarizes LPA1 antagonist patents reported since 2010 and ROCK inhibitor patents since 2017 according to their scaffolds based on the Cortellis Drug Discovery Intelligence database. Information on LPA1 antagonists entering clinical trials is integrated.Expert opinion Over the past decade, a large number of antagonists targeting the LPA1 signaling pathway have been patented for fibrosis therapy. A limited number of compounds have entered clinical trials. Different companies and research groups have used different scaffolds when designing compounds for fibrosis therapy. Therefore, LPA1 and ROCK are competitive targets for the development of new therapies for fibrosis to provide a potential treatment method for fibrosis in the future.KEYWORDS: fibrosisLPA1 signaling pathwayLPA1 antagonistsROCK inhibitorspatent review Article highlights LPA1 is expressed at high levels in the process of fibrosis, and the degree of fibrosis may be alleviated by knocking out or antagonizing LPA1.The LPA1 signaling pathway is closely related to the process of fibrosis. Antagonists of LPA1 with various scaffolds exert antifibrotic effects.ROCK downstream of LPA1 causes cytoskeletal reorganization, regulates downstream fibrosis-related targets, such as CTGF, MMP and NF-κB, and then affects the process of fibrosis. Therefore, inhibitors targeting ROCK also treat fibrosis.Patents disclose LPA1 antagonists and ROCK inhibitors. Some leads have also entered clinical trials.Due to the wide distribution of LPA1 and ROCK, off-target effects cause adverse reactions. Drug structures were modified to reduce adverse reactions and improve physical and chemical properties.This box summarizes key points contained in the article.Declaration of interestsThe authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.Reviewer disclosuresPeer reviewers on this manuscript have no relevant financial or other relationships to disclose.Additional informationFundingThis study was supported by Projects 81903439 of the National Natural Science Foundation of China, the Natural Science Foundation of Jiangsu Province of China (no. BK20190562).
The purpose of this study is to explore the intelligent application design based on artificial intelligence and adaptive interface. First, we outline the basic principles of artificial intelligence technology and its important role in application design, as well as the basic concepts and principles of adaptive interface design. Then, by analyzing practical cases, we discuss the close combination of artificial intelligence and UI page design, including design practices in the fields of intelligent recommendation system, intelligent voice assistant and intelligent search engine. Through these case studies, we delve into how AI and adaptive interfaces can work together to drive smart and personalized application design. Finally, we summarize the research results and look forward to the development trend and research direction of intelligent application design in the future.
The large availability of depth sensors provides valuable complementary information for salient object detection (SOD) in RGBD images. However, due to the inherent difference between RGB and depth information, extracting features from the depth channel using ImageNet pre-trained backbone models and fusing them with RGB features directly are sub-optimal. In this paper, we utilize contrast prior, which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-based architecture to enhance the depth information. The enhanced depth cues are further integrated with RGB features for SOD, using a novel fluid pyramid integration, which can make better use of multi-scale cross-modal features. Comprehensive experiments on 5 challenging benchmark datasets demonstrate the superiority of the architecture CPFP over 9 state-of-the-art alternative methods.
Deep learning has achieved impressive success in natural language processing. However, most previous models are learned on the specific single tasks, suffering from insufficient training set. Multi-task deep learning can solve this dilemma by sharing the part of parameters, improving generalization. The common multi-task deep learning model consists of the shared layer and the task specific layer. In this paper, we attempt to enhance the performance of shared layer and proposed two variants based on convolutional neural networks. The first model is Agent Model-Direct Concatenate, where each task is assigned with a separate convolutional neural network for extracting the common and task specific features simultaneously. The second model is Agent Model-Gating Concatenation, where the task specific layer could automatically decide the information flow of each element of the output of shared layer. The two networks are trained jointly over three pair-wise groups of movie review data sets. Experiments show the effectiveness of our two networks, inspiring a potential direction for the related research of multi-task deep learning.
Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity according to the scale of datasets, especially for small scale datasets, and is robust with few hyper-parameters and without over-fitting. We found that the proposed model is superior to other CPI prediction models and performs well on the constructed challenging dataset. We finally predicted 2 new inhibitors for clusters of differentiation 47(CD47) which has few known inhibitors. The IC50s of enzyme activities of these 2 new small molecular inhibitors targeting CD47-SIRPα interaction are 3.57 and 4.79 μM respectively. These results fully demonstrate the competence of this concise but efficient tool for CPI prediction.
At a time when artificial intelligence is widely used in all walks of life, the way users interact with the digital world also needs to incorporate intelligent elements to reduce the cost of connectivity. This cost can be quantified through "experience metrics", which reveal the problems users encounter when using the interface (UI), and make targeted optimization. With AI, deep learning and prediction of user behavior can be achieved to anticipate and address potential barriers to use in UI design. This will not only improve the user experience, but also promote the development of UI design in a more user-friendly and intelligent direction. Through accurate analysis of experience indicators and combined with AI technology to optimize design, the gap between users and the digital world can be greatly reduced, making digital products more suitable for user needs and achieving seamless interactive experience.
Neural style transfer (NST), where an input image is rendered in the style of another image, has been a topic of considerable progress in recent years. Research over that time has been dominated by transferring aspects of color and texture, yet these factors are only one component of style. Other factors of style include composition, the projection system used, and the way in which artists warp and bend objects. Our contribution is to introduce a neural architecture that supports transfer of geometric style. Unlike recent work in this area, we are unique in being general in that we are not restricted by semantic content. This new architecture runs prior to a network that transfers texture style, enabling us to transfer texture to a warped image. This form of network supports a second novelty: we extend the NST input paradigm. Users can input content/style pair as is common, or they can chose to input a content/texture-style/geometry-style triple. This three image input paradigm divides style into two parts and so provides significantly greater versatility to the output we can produce. We provide user studies that show the quality of our output, and quantify the importance of geometric style transfer to style recognition by humans.
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