Isolation and analysis of tumor‐derived extracellular vesicles (T‐EVs) are important for clinical cancer management. Here, we develop a fluid multivalent magnetic interface (FluidmagFace) in a microfluidic chip for high‐performance isolation, release, and protein profiling of T‐EVs. The FluidmagFace increases affinity by 105‐fold with fluidity‐enhanced multivalent binding to improve isolation efficiency by 13.9 % compared with a non‐fluid interface. Its anti‐adsorption property and microfluidic hydrodynamic shear minimize contamination, increasing detection sensitivity by two orders of magnitude. Moreover, its reversibility and expandability allow high‐throughput recovery of T‐EVs for mass spectrometric protein analysis. With the chip, T‐EVs were detected in all tested cancer samples with identification of differentially expressed proteins compared with healthy controls. The FluidmagFace opens a new avenue to isolation and release of targets for cancer diagnosis and biomarker discovery.
Knowledge graph completion (KGC) can solve the problem of data sparsity in the knowledge graph. A large number of models for the KGC task have been proposed in recent years. However, the underutilisation of the structure information around nodes is one of the main problems of the previous KGC model, which leads to relatively single encoding information. To this end, a new KGC model that encodes and decodes the feature information is proposed. First, we adopt the subgraph sampling method to extract node structure. Moreover, the graph convolutional network (GCN) introduced the channel attention convolution encode node structure features and represent them in matrix form to fully mine the node feature information. Eventually, the high-dimensional structure analysis weight decodes the encoded matrix embeddings and then constructs the scoring function. The experimental results show that the model performs well on the datasets used.
MicroRNAs (miRNAs) in tumor-derived extracellular vesicles (tEVs) are important cancer biomarkers for cancer screening and early diagnosis. Multiplex detection of miRNAs in tEVs facilitates accurate diagnosis but remains a challenge. Herein, we propose an encoded fusion strategy to profile the miRNA signature in tEVs for pancreatic cancer diagnosis. A panel of encoded-targeted-fusion beads was fabricated for the selective recognition and fusion of tEVs, with the turn-on fluorescence signals of molecule beacons for miRNA quantification and barcode signals for miRNA identification using readily accessible flow cytometers. Using this strategy, six types of pancreatic-cancer-associated miRNAs can be profiled in tEVs from 2 μL plasma samples (n = 36) in an isolation-free and lysis-free manner with only 2 h of processing, offering a high accuracy (98%) to discriminate pancreatic cancer, pancreatitis, and healthy donors. This encoded fusion strategy exhibits great potential for multiplex profiling of miRNA in tEVs, offering new avenues for cancer diagnosis and screening.
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