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Aims: To compare the fibroinflammatory diseases Kimura's disease (KD) and immunoglobulin (Ig)G4‐related disease (IgG4RD) and to explore their possible relationship. Methods and results: Forty‐six cases of KD and 29 IgG4RD from our institution diagnosed from 2011 to 2020 were studied. They were compared with each other on clinical, pathological and immunohistological features. There were similar clinical features, except that IgG4RD affected an older patient population, with more frequent salivary gland involvement and KD affected head and neck lymph nodes, and showed blood eosinophilia more frequently than IgG4RD. IgG4RD exhibited frequent storiform fibrosis and obliterative phlebitis, while KD showed more frequent tissue eosinophilia, eosinophilic abscess, germinal centre eosinophilic deposit and vascularisation. Twenty to 30% of KD had more than 50 IgG4+ plasma cells (PC) per high‐power field (HPF) and IgG4/IgG+PC ratio exceeding 40%. These parameters, however, occurred in 100% of IgG4RD. Significantly more KD had >10 IgE+PC/HPF and lymphoid germinal centre IgE reticular staining compared to IgG4RD. All these histological and immunohistological features are overlapping in the two diseases, although they differed with statistical significance. Conclusion: Our study confirmed that there is significant overlap in clinical, pathological and immunohistological features between KD and IgG4RD. It is important to recognise these overlapping features, and correlation with a clinicopathological picture is required in differential diagnoses. The overlapping features also suggest a possible close relationship between KD and IgG4RD, which could represent different facets of a continuous fibroinflammatory disease spectrum. [ABSTRACT FROM AUTHOR]

Proline, glutamate, and leucine-rich protein 1 (PELP1) are involved in several cancers, but little is known about PELP1 in lung cancer. In this study, PELP1 expression was evaluated in 305 lung cancer (NSCLC) specimens to explore the role of PELP1 in lung cancer. After silencing PELP1, the proliferation, migration, invasion of tumor cells, PELP1 in relation to cell cycle and signaling pathways were evaluated, and whole-genome exons were analyzed. PELP1 is overexpressed in lung cancer, PELP1 expression correlated with squamous carcinoma, smoking, and wild-type EGFR status (all Ps<0.001) but associated with lung cancer-specific survival (P > 0.05). Silencing significantly inhibited lung cancer cell proliferation, migration, and invasion (P < 0.05) and promoted high sensitivity of lung cancer cells to tyrosine kinase inhibitor (TKI) gefitinib. PELP1-silenced cells showed downregulated phosphorylated MAPK, cyclinD1, CDK2, and upregulated RB (P < 0.05) but no change in AKT. In PELP1-silenced lung cancer cells, 140 genes were upregulated, and 143 genes were downregulated. Furthermore, the number of T regulatory cell was higher in lung adenocarcinoma with pelp1 high-expression and pelp1 expression was negatively correlated with CD274 (PDL-1) and CTLA4. Therefore, PELP1 plays an important role in the malignant behavior of NSCLC and could be a potential therapeutic target. [ABSTRACT FROM AUTHOR]

Heterogeneous graph embedding aims at learning low-dimensional representations from a graph featuring nodes and edges of diverse natures, and meanwhile preserving the underlying topology. Existing approaches along this line have largely relied on meta-paths, which are by nature hand-crafted and pre-defined transition rules, so as to explore the semantics of a graph. Despite the promising results, defining meta-paths requires domain knowledge, and thus when the test distribution deviates from the priors, such methods are prone to errors. In this paper, we propose a self-learning scheme for heterogeneous graph embedding, termed as self-guided walk (SILK), that bypasses meta-paths and learns adaptive attentions for node walking. SILK assumes no prior knowledge or annotation is provided, and conducts a customized random walk to encode the contexts of the heterogeneous graph of interest. Specifically, this is achieved via maintaining a dynamically-updated guidance matrix that records the node-conditioned transition potentials. Experimental results on four real-world datasets demonstrate that SILK significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Crawler detection is always an important research topic in network security. With the development of web technology, crawlers are constantly updating and changing, and their types are becoming diverse. The diversity and dynamics of crawlers pose significant challenges for feature applicability and model robustness. Existing crawler detection methods can only detect a limited number of crawlers by predefined rules and can not cover all types of crawlers; worse, they can be completely invalidated by the emergence of new types of crawlers. In this paper, we propose a reinforcement learning based web crawler detection method for diversity and dynamics (WC3D), which is composed of a feature selector and a session classifier. The feature selector selects the appropriate feature set for different types of crawlers with deep deterministic policy gradient. The session classifier makes crawler detection and provides rewards to the feature selector. The two modules are trained jointly to optimize the feature selection and session classification processes. Extensive experiments demonstrate the existence of crawler diversity and that the proposed method is still highly robust against the new type of crawlers and achieves state-of-the-art performance even without considering the dynamics of the crawlers. [ABSTRACT FROM AUTHOR]

Video highlight detection (VHD) has emerged as a pressing task due to the unprecedentedly increasing amount of video data, such as those from e-commerce live-broadcasting platforms. Many approaches focus on exploiting text data, in the form of video description or time-sync comments, to facilitate the VHD task. Despite the promising results, they have largely overlooked the noises inherent in the text data and have mostly relied on isolating the feature of text and video. In this paper, we introduce a novel model to handle VHD, termed Coevolution Network (CoEvo-Net), that allows us to account for joint learning of the language and video features explicitly via a coevolution paradigm, in which features from the two data modalities progressively refine each other. This is achieved by a dedicated CoEvo-Cell that takes language and video together as inputs, extracts cross-modality, and filters the undesired parts of the input, such as words in a sentence. Furthermore, we release a large-scale dataset of e-commerce for VHD, in which each video is coupled with a sentence for description, to benchmark the sentence-based VHD approaches. Extensive experiments on the released dataset demonstrate that CoEvo-Net achieves state-of-the-art performance. Our dataset and code will be made publicly available. [ABSTRACT FROM AUTHOR]

While treatment for B-cell malignancies has been revolutionized through the advent of CAR immunotherapy, similar strategies for T-cell malignancies have been limited. Additionally, T-cell leukemias and lymphomas can commonly metastasize to the CNS, where outcomes are poor and treatment options are associated with severe side effects. Consequently, the development of safer and more effective alternatives for targeting malignant T cells that have invaded the CNS remains clinically important. CD5 CAR has previously been shown to effectively target various T-cell cancers in preclinical studies. As IL-15 strengthens the anti-tumor response, we have modified CD5 CAR to secrete an IL-15/IL-15sushi complex. In a Phase I clinical trial, these CD5-IL15/IL15sushi CAR T cells were tested for safety and efficacy in a patient with refractory T-LBL with CNS infiltration. CD5-IL15/IL15sushi CAR T cells were able to rapidly ablate the CNS lymphoblasts within a few weeks, resulting in the remission of the patient's lymphoma. Despite the presence of CD5 on normal T cells, the patient only experienced a brief, transient T-cell aplasia. These results suggest that CD5-IL15/IL15sushi CAR T cells may be a safe and useful treatment of T-cell malignancies and may be particularly beneficial for patients with CNS involvement. Graphical Abstract [ABSTRACT FROM AUTHOR]

Background & Aims Largazole is a novel histone deacetylase ( HDAC) inhibitor. This study investigated the effects of largazole against liver fibrosis. Methods The in vitro effects of largazole were examined using hepatic stellate cells ( HSCs). In vivo effects of largazole were studied using a mouse liver fibrotic model induced by CCl4. Results Largazole augmented acetylation of histone H3 (H3) and histone H4 (H4) in HSCs. It directly inhibited the activation of HSCs owing to HDAC inhibitory activity as the antifibrotic effect of largazole was significantly decreased in cells with HDAC1, HDAC2 and HDAC3 knockdown. Largazole also induced apoptosis of HSCs. Largazole not only inhibited the expression of TGFβR2, but also reduced phosphorylation of Smad2 and Akt induced by TGF-β1. Largazole also inhibited the expression of vascular endothelial growth factor (VEGF) and its receptor. VEGF-induced proliferation of HSCs and activation of Akt and p38MAPK were also suppressed by largazole. In vivo, largazole reduced the expression of collagen I, α-smooth muscle actin and tissue inhibitor of metalloproteinase-1 in CCl4-induced fibrosis, and these antifibrotic effects were associated with increased acetylation of H3 and H4. Largazole also induced HSCs to undergo apoptosis in vivo, which was correlated with downregulation of bcl-2 and bcl-xL. Furthermore, largazole inhibited angiogenesis in vivo as evidenced by reduced expression of CD34, VEGF and VEGFR. In addition to its antifibrotic activity, the drug reduced inflammatory activity in CCl4-induced liver fibrosis. Conclusions Our findings revealed a novel role of largazole in the treatment of liver fibrosis. Through multiple mechanisms, largazole could be a potentially effective antifibrotic agent. [ABSTRACT FROM AUTHOR]