The restriction of anti-cancer drugs entry to tumor sites in the brain is a major impediment to the development of new strategies for the treatment of glioma. Based on the finding that macrophages possess an intrinsic homing property enabling them to migrate to tumor sites across the endothelial barriers in response to the excretion of cytokines/chemokines in the diseased tissues, we exploited macrophages as ‘Trojan horses’ to carry drug-loading nanoparticles (NPs), pass through barriers, and offload them into brain tumor sites. Anticancer drugs were encapsulated in nanoparticles to avoid their damage to the cells. Drug loading NPs was then incubated with RAW264.7 cells in vitro to prepare macrophage-NPs (M-NPs). The release of NPs from M-NPs was very slow in medium of DMEM and 10% FBS and significantly accelerated when LPS and IFN-γ were added to mimic tumor inflammation microenvironment. The viability of macrophages was not affected when the concentration of doxorubicin lower than 25 μg/ml. The improvement of cellular uptake and penetration into the core of glioma spheroids of M-NPs compared with NPs was verified in in vitro studies. The tumor-targeting efficiency of NPs was also significantly enhanced after loading into macrophages in nude mice bearing intracranial U87 glioma. Our results provided great potential of macrophages as an active biocarrier to deliver anticancer drugs to the tumor sites in the brain and improve therapeutic effects of glioma.

Although several strategies have been applied for oral insulin delivery to improve insulin bioavailability, little success has been achieved. To overcome multiple barriers to oral insulin absorption simultaneously, insulin-loaded N-trimethyl chitosan chloride (TMC)-coated polylactide-co-glycoside (PLGA) nanoparticles (Ins TMC-PLGA NPs) were formulated in our study. The Ins TMC-PLGA NPs were prepared using the double-emulsion solvent evaporation method and were characterized to determine their size (247.6 ± 7.2 nm), ζ-potential (45.2 ± 4.6 mV), insulin-loading capacity (7.8 ± 0.5%) and encapsulation efficiency (47.0 ± 2.9%). The stability and insulin release of the nanoparticles in enzyme-containing simulated gastrointestinal fluids suggested that the TMC-PLGA NPs could partially protect insulin from enzymatic degradation. Compared with unmodified PLGA NPs, the positively charged TMC-PLGA NPs could improve the mucus penetration of insulin in mucus-secreting HT29-MTX cells, the cellular uptake of insulin via clathrin- or adsorption-mediated endocytosis in Caco-2 cells and the permeation of insulin across a Caco-2 cell monolayer through tight junction opening. After oral administration in mice, the TMC-PLGA NPs moved more slowly through the gastrointestinal tract compared with unmodified PLGA NPs, indicating the mucoadhesive property of the nanoparticles after TMC coating. Additionally, in pharmacological studies in diabetic rats, orally administered Ins TMC-PLGA NPs produced a stronger hypoglycemic effect, with 2-fold higher relative pharmacological availability compared with unmodified NPs. In conclusion, oral insulin absorption is improved by TMC-PLGA NPs with the multiple absorption barriers overcome simultaneously. TMC-PLGA NPs may be a promising drug delivery system for oral administration of macromolecular therapeutics.

To study the differences in functional brain networks between eyes-closed (EC) and eyes-open (EO) at resting state, electroencephalographic (EEG) activity was recorded in 21 normal adults during EC and EO states. The synchronization likelihood (SL) was applied to measure correlations between all pairwise EEG channels, and then the SL matrices were converted to graphs by thresholding. Graphs were measured by topological parameters in theta (4–7 Hz), alpha (8–13 Hz), and beta (14–30 Hz) bands. By changing from EC to EO states, mean cluster coefficients decreased in both theta and alpha bands, but mean shortest path lengths became shorter only in the alpha band. In addition, local efficiencies decreased in both theta and alpha bands, while global efficiencies in the alpha band increased inversely. Opening the eyes decreased both nodes and connections in frontal area in the theta band, and also decreased those in bilateral posterior areas in the alpha band. These results suggested that a combination of the SL and graph theory methods may be a useful tool for distinguishing states of EC and EO. The differences in functional connectivity between EC and EO states may reflect the difference of information communication in human brain.

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The precise segmentation of retinal blood vessel is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and then multiply the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employees a kind of structured dropout convolutional block instead of the original convolutional block of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that our proposed SA-UNet achieves the state-of-the-art retinal vessel segmentation accuracy on both datasets. 

A new hybrid solution is presented to improve the efficiency of intelligent warehouses with multirobot systems, where the genetic algorithm (GA) based task scheduling is combined with reinforcement learning (RL) based path planning for mobile robots. Reinforcement learning is an effective approach to search for a collision-free path in unknown dynamic environments. Genetic algorithm is a simple but splendid evolutionary search method that provides very good solutions for task allocation. In order to achieve higher efficiency of the intelligent warehouse system, we design a new solution by combining these two techniques and provide an effective and alternative way compared with other state-of-the-art methods. Simulation results demonstrate the effectiveness of the proposed approach regarding the optimization of travel time and overall efficiency of the intelligent warehouse system.

Swarm robotics is a specific research field of multirobotics where a large number of mobile robots are controlled in a coordinated way. Formation control is one of the most challenging goals for the coordination control of swarm robots. In this paper, a behavior-based control design approach is proposed for two kinds of important formation control problems: efficient initial formation and formation control while avoiding obstacles. In this approach, a classification-based searching method for generating large-scale robot formation is presented to reduce the computational complexity and speed up the initial formation process for any desired formation. The behavior-based method is applied for the formation control of swarm robot systems while navigating in an unknown environment with obstacles. Several groups of experimental results demonstrate the success of the proposed approach. These methods have potential applications for various swarm robot systems in both the simulation and the practical environments.