T-LAK cell-oriented protein kinase (TOPK) potently promotes malignant proliferation of tumour cells and is considered as a maker of tumour progression. Psoriasis is a common inflammatory skin disease characterized by abnormal proliferation of keratinocytes. However, the role of TOPK in psoriasis has not been well elucidated. This study aims to investigate the expression and role of TOPK in psoriasis, and the role of TOPK inhibitor in psoriasis attenuation. Gene Expression Omnibus datasets derived from psoriasis patients and psoriatic model mice were screened for analysis. Skin specimens from psoriasis patients were collected for TOPK immunohistochemical staining to investigate the expression and localization of TOPK. Next, psoriatic mice model was established to further confirm TOPK expression pattern. Then, TOPK inhibitor was applied to investigate the role of TOPK in psoriasis progression. Finally, cell proliferation assay, apoptosis assay and cell cycle analysis were performed to investigate the potential mechanism involved. Our study showed that TOPK was upregulated in the lesions of both psoriasis patients and psoriatic model mice, and TOPK levels were positively associated with psoriasis progression. TOPK was upregulated in psoriatic lesions and expressed predominantly by epidermal keratinocytes. In addition, TOPK levels in epidermal keratinocytes were positively correlated with epidermal hyperplasia. Furthermore, topical application of TOPK inhibitor OTS514 obviously alleviated disease severity and epidermal hyperplasia. Mechanismly, inhibiting TOPK induces G2/M phase arrest and apoptosis of keratinocytes, thereby attenuating epidermal hyperplasia and disease progression. Collectively, this study identifies that upregulation of TOPK in keratinocytes promotes psoriatic progression, and inhibiting TOPK attenuates epidermal hyperplasia and psoriatic progression.
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http://dx.doi.org/10.1111/exd.14909 | DOI Listing |
Brain Res Bull
January 2025
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; Guangdong Province Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou 510006, China; Department of Aging Research and Geriatric Medicine, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan. Electronic address:
The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. In this study, we constructed three types of brain graphs (BGs), three types of gut graphs (GGs), and nine types of brain-gut combined graphs (BGCGs) for each individual. We proposed a novel methodology of multi-omics graph convolutional network (MO-GCN) with an attention mechanism to construct a classification model by integrating all BGCGs.
View Article and Find Full Text PDFJ Pers Med
November 2024
Department of Radiation Oncology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
Large language models (LLMs) show promise in healthcare but face challenges with hallucinations, particularly in rapidly evolving fields like diabetes management. Traditional LLM updating methods are resource-intensive, necessitating new approaches for delivering reliable, current medical information. This study aimed to develop and evaluate a novel retrieval system to enhance LLM reliability in diabetes management across different languages and guidelines.
View Article and Find Full Text PDFVLDB J
December 2024
University of Salzburg, Salzburg, Austria.
We provide efficient support for applications that aim to continuously find pairs of similar sets in rapid streams, such as Twitter streams that emit tweets as sets of words. Using a sliding window model, the top- result changes as new sets enter the window or existing ones leave the window. Specifically, when a set arrives, it may form a new top- result pair with any set already in the window.
View Article and Find Full Text PDFProc Int World Wide Web Conf
May 2024
Emory University, Atlanta, GA, USA.
Graph Neural Networks (GNNs) have achieved great success in learning with graph-structured data. Privacy concerns have also been raised for the trained models which could expose the sensitive information of graphs including both node features and the structure information. In this paper, we aim to achieve node-level differential privacy (DP) for training GNNs so that a node and its edges are protected.
View Article and Find Full Text PDFMed Image Anal
December 2024
Tri-Institutional Center for Translational Research in Neuro Imaging and Data Science (TreNDS), USA; Department of Computer Science, Georgia State University, Atlanta, USA.
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions.
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