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Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.
BMC Cancer
January 2025
Department of Radiology, Xiangtan Central Hospital, Xiangtan, 411000, P. R. China.
Background: This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).
Methods: In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method.
Impacts of variable magnetic field on ternary Casson nanofluid flow through ciliated arterial walls incorporating interfacial nanolayer.
Electromagn Biol Med
January 2025
Department of Applied Mathematics, University of Calcutta, Kolkata, India.
The current investigation explores tri-hybrid mediated blood flow through a ciliary annular model, designed to emulate an endoscopic environment. The human circulatory system, driven by the metachronal ciliary waves, is examined in this study to understand how ternary nanoparticles influence wave-like flow dynamics in the presence of interfacial nanolayers. We also analyze the effect of an induced magnetic field on Ag-Cu-/blood flow within the annulus, focusing on thermal radiation, heat sources, buoyancy forces and ciliary motion.
View Article and Find Full Text PDFMulti-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.
PLoS Comput Biol
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Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China.
Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction.
View Article and Find Full Text PDFSemblans: Automated assembly and processing of RNA-Seq data.
Bioinformatics
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Department of Biological Sciences, University of Illinois at Chicago, Illinois 60607, United States.
Motivation: Recent advancements in parallel sequencing methods have precipitated a surge in publicly available short-read sequence data. This has encouraged the development of novel computational tools for the de novo assembly of transcriptomes from RNA-seq data. Despite the availability of these tools, performing an end-to-end transcriptome assembly remains a programmatically involved task necessitating familiarity with best practices.
View Article and Find Full Text PDFQuotient Complex (QC)-Based Machine Learning for 2D Hybrid Perovskite Design.
J Chem Inf Model
January 2025
Division of Physics & Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.
With remarkable stability and exceptional optoelectronic properties, two-dimensional (2D) halide layered perovskites hold immense promise for revolutionizing photovoltaic technology. Effective data representations are key to the success of all learning models. Currently, the lack of comprehensive and accurate material representations has hindered AI-based design and discovery of 2D perovskites, limiting their potential for advanced photovoltaic applications.
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