A Microfluidic Experimental Method for Studying Cell-to-Cell Exosome Delivery-Taking Stem Cell-Tumor Cell Interaction as a Case.

Cell-to-cell communication must occur through molecular transport in the intercellular fluid space. Nanoparticles, such as exosomes, diffuse or move more slowly in fluids than small molecules. To find a microfluidic technology for real-time exosome experiments on intercellular communication between living cells, we use the microfluidic culture dish's quaternary ultra-slow microcirculation flow field to accumulate nanoparticles in a specific area. Taking stem cell-tumor cell interaction as an example, the ultra-slow microcirculatory flow field controls stem cell exosomes to interfere with tumor cells remotely. Under static coculture conditions (without microfluidics), the tumor cells near stem cells (<200 µm) show quick breaking through from its Matrigel drop to meet stem cells, but this 'breaking through' quickly disappears with increasing distance. In programmed ultra-slow microcirculation, stem cells induce tumor cells 5000 µm far at the site of exosome deposition (according to nanoparticle simulations). After 14 days of programmed coculture, the glomeration and migration of tumor cells were observed in the exosome deposition area. This example shows that the ultra-slow microcirculation of the microfluidic culture dish has good prospects in quantitative experiments to study exosome communication between living cells and drug development of cancer metastasis.

GL-Segnet: Global-Local representation learning net for medical image segmentation.

Medical image segmentation has long been a compelling and fundamental problem in the realm of neuroscience. This is an extremely challenging task due to the intensely interfering irrelevant background information to segment the target. State-of-the-art methods fail to consider simultaneously addressing both long-range and short-range dependencies, and commonly emphasize the semantic information characterization capability while ignoring the geometric detail information implied in the shallow feature maps resulting in the dropping of crucial features. To tackle the above problem, we propose a Global-Local representation learning net for medical image segmentation, namely GL-Segnet. In the Feature encoder, we utilize the Multi-Scale Convolution (MSC) and Multi-Scale Pooling (MSP) modules to encode the global semantic representation information at the shallow level of the network, and multi-scale feature fusion operations are applied to enrich local geometric detail information in a cross-level manner. Beyond that, we adopt a global semantic feature extraction module to perform filtering of irrelevant background information. In Attention-enhancing Decoder, we use the Attention-based feature decoding module to refine the multi-scale fused feature information, which provides effective cues for attention decoding. We exploit the structural similarity between images and the edge gradient information to propose a hybrid loss to improve the segmentation accuracy of the model. Extensive experiments on medical image segmentation from Glas, ISIC, Brain Tumors and SIIM-ACR demonstrated that our GL-Segnet is superior to existing state-of-art methods in subjective visual performance and objective evaluation.