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| http://dx.doi.org/10.21542/gcsp.2020.12 | DOI Listing |
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Predicting inflammatory response of biomimetic nanofibre scaffolds for tissue regeneration using machine learning and graph theory.
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Tissue regeneration after a wound occurs through three main overlapping and interrelated stages namely inflammatory, proliferative, and remodelling phases, respectively. The inflammatory phase is key for successful tissue reconstruction and triggers the proliferative phase. The macrophages in the non-healing wounds remain in the inflammatory loop, but their phenotypes can be changed interactions with nanofibre-based scaffolds mimicking the organisation of the native structural support of healthy tissues.
View Article and Find Full Text PDFSingle-omics approaches often provide a limited view of complex biological systems, whereas multiomics integration offers a more comprehensive understanding by combining diverse data views. However, integrating heterogeneous data types and interpreting the intricate relationships between biological features-both within and across different data views-remains a bottleneck. To address these challenges, we introduce COSIME (Cooperative Multi-view Integration and Scalable Interpretable Model Explainer).
View Article and Find Full Text PDFThe G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.
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View Article and Find Full Text PDFAtomic force microscopy combined with microfluidics for label-free sorting and automated nanomechanics of circulating tumor cells in liquid biopsy.
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Liquid biopsies are expected to advance cancer management, and particularly physical cues are gaining attention for indicating tumorigenesis and metastasis. Atomic force microscopy (AFM) has become a standard and important tool for detecting the mechanical properties of single living cells, but studies of developing AFM-based methods to efficiently measure the mechanical properties of circulating tumor cells (CTCs) in liquid biopsy for clinical utility are still scarce. Herein, we present a proof-of-concept study based on the complementary combination of AFM and microfluidics, which allows label-free sorting of individual CTCs and subsequent automated AFM measurements of the mechanical properties of CTCs.
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