TOPK is overexpressed in various types of cancer and associated with poor outcomes in different types of cancer. In this study, we first found that the expression of T-lymphokine-activated killer cell-originated protein kinase (TOPK) was significantly higher in Grade III or Grade IV than that in Grade II in glioma ( = 0.007 and < 0.001, respectively). Expression of TOPK was positively correlated with Ki67 ( < 0.001). Knockdown of TOPK significantly inhibited cell growth, colony formation and increased sensitivities to temozolomide (TMZ) in U-87 MG or U-251 cells, while TOPK overexpression promoted cell growth and colony formation in Hs 683 or A-172 cells. Glioma patients expressing high levels of TOPK have poor survival compared with those expressing low levels of TOPK in high-grade or low-grade gliomas (hazard ratio = 0.2995; 95% CI, 0.1262 to 0.7108; = 0.0063 and hazard ratio = 0.1509; 95% CI, 0.05928 to 0.3842; < 0.0001, respectively). The level of TOPK was low in TMZ-sensitive patients compared with TMZ-resistant patients ( = 0.0056). In TMZ-resistant population, patients expressing high TOPK have two months' shorter survival time than those expressing low TOPK. Our findings demonstrated that TOPK might represent as a promising prognostic and predictive factor and potential therapeutic target for glioma.
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http://dx.doi.org/10.18632/oncotarget.23674 | DOI Listing |
J 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.
View Article and Find Full Text PDFNAR Genom Bioinform
December 2024
Department of Data Science, 01Life Institute, Shenzhen 518000, China.
The development of multi-omics technologies has generated an abundance of biological datasets, providing valuable resources for investigating potential relationships within complex biological systems. However, most correlation analysis tools face computational challenges when dealing with these high-dimensional datasets containing millions of features. Here, we introduce pyNetCor, a fast and scalable tool for constructing correlation networks on large-scale and high-dimensional data.
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