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Circulating tumor cells (CTCs) are important markers of metastatic cancer. The isolation and detection of CTCs from peripheral blood provides valuable information for cancer diagnosis and precision medicine. However, cost-efficient targeted separation of CTCs of different origins with clinically significant specificity and efficiency remains a major challenge. In this study, a facile approach was developed to fabricate a thin sheet of hyaluronic acid (HA)-functionalized PLGA nanofibrous membrane and integrate it into a microfluidic chamber. The HA was covalently conjugated onto polyethyleneimine (PEI)-modified electrospun poly(lactic-co-glycolic acid) (PLGA) nanofibers. Different techniques were employed to characterize the resulted nanofibers. The results show that the CD44+ carcinoma of various origins (HeLa, KB, A549, and MCF-7 cells) could be selectively captured by the PLGA-PEI-HA nanofibers in the microfluidic platform. Importantly, the PLGA-PEI-HA nanofibrous membrane was more efficient to capture HeLa cancer cells under flowing conditions than in static dishes, and at a really low density (20 cells per mL). Furthermore, with constant media perfusion, the captured HeLa cells could grow on the nanofibrous membrane in the microchip for days without compromised cell viability. This is the first trial of using HA-functionalized electrospun nanofibers in a lab-chip device for cancer cell capture and culture. Compared to conventional CTC capture methods, the integration of inexpensive functional electrospun nanofibers and microfluidic technologies may expand the frontiers of using advanced nanomaterials in portable diagnostic applications.

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Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. To generate reliable attention weights, we propose multi-level adaptive patch matching to improve the distinctiveness of the matching cost at different disparities even for textureless regions. The proposed cost volume is named attention concatenation volume (ACV) which can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the aggregation network can achieve higher accuracy for GwcNet. Furthermore, we design a highly accurate network (ACVNet) based on our ACV, which achieves state-ofthe-art performance on several benchmarks. The code is available at https://github.com/gangweiX/ACVNet.