Currently, research on the construction of vascular-like tubular structures is a hot area of tissue engineering, since it has potential applications in the building of artificial blood vessels. In this paper, we report a fluidic self-assembly method using cell embedded microstructures to construct vascular-like microtubes. A novel 4-layer microfluidic device was fabricated using polydimethylsiloxane (PDMS), which contains fabrication, self-assembly and extraction areas inside one channel. Cell embedded microstructures were directly fabricated using poly(ethylene glycol) diacrylate (PEGDA) in the fabrication area, namely on-chip fabrication. Self-assembly of the fabricated microstructures was performed in the assembly area which has a micro well. Assembled tubular structures (microtubes) were extracted outside the channel into culture dishes using a normally closed (NC) micro valve in the extraction area. The self-assembly mechanism was experimentally demonstrated. The performance of the NC micro valve and embedded cell concentration were both evaluated. Fibroblast (NIH/3T3) embedded vascular-like microtubes were constructed inside this reusable microfluidic device.

The vascular network of the circulatory system plays a vital role in maintaining homeostasis in the human body. In this paper, a novel modular microfluidic system with a vertical two-layered configuration is developed to generate large-scale perfused microvascular networks in vitro. The two-layer polydimethylsiloxane (PDMS) configuration allows the tissue chambers and medium channels not only to be designed and fabricated independently but also to be aligned and bonded accordingly. This method can produce a modular microfluidic system that has high flexibility and scalability to design an integrated platform with multiple perfused vascularized tissues with high densities. The medium channel was designed with a rhombic shape and fabricated to be semiclosed to form a capillary burst valve in the vertical direction, serving as the interface between the medium channels and tissue chambers. Angiogenesis and anastomosis at the vertical interface were successfully achieved by using different combinations of tissue chambers and medium channels. Various large-scale microvascular networks were generated and quantified in terms of vessel length and density. Minimal leakage of the perfused 70-kDa FITC-dextran confirmed the lumenization of the microvascular networks and the formation of tight vertical interconnections between the microvascular networks and medium channels in different structural layers. This platform enables the culturing of interconnected, large-scale perfused vascularized tissue networks with high density and scalability for a wide range of multiorgan-on-a-chip applications, including basic biological studies and drug screening.

This article reports an automated optically-induced dielectrophoresis (ODEP) system for characterizing the specific membrane capacitance (SMC) of individual cells. The simulation of cell motion is conducted to analyze the electrokinetic forces acting on the cell. A self-developed visual tracking algorithm for multicells is used to realize an automated process for determining the frequency-sweeping range, crossover frequencies, and cell radii. The SMC values of malignant bladder cancer cells (T24 and RT4) and normal urothelial cells (SV-HUC-1) were quantified using the automated system, demonstrating that the system has a measurement speed of ~ 1 cell/s, an accuracy of 1 kHz for the crossover frequency determination, and an accuracy of 0.2 μm for the cell radius measurement.

The takeover issue, especially the setting of the takeover time budget, is a critical factor restricting the implementation and development of conditionally automated vehicles. The general fixed takeover time budget has certain limitations, as it does not take into account the driver’s non-driving behaviors. Here, we propose an intelligent takeover assistance system consisting of all-round sensing gloves, a non-driving behavior identification module, and a takeover time budget determination module. All-round sensing gloves based on triboelectric sensors seamlessly detect delicate motions of hands and interactions between hands and other objects, and then transfer the electrical signals to the non-driving behavior identification module, which achieves an accuracy of 94.72% for six non-driving behaviors. Finally, combining the identification result and its corresponding minimum takeover time budget obtained through the takeover time budget determination module, our system dynamically adjusts the takeover time budget based on the driver’s current non-driving behavior, significantly improving takeover performance in terms of safety and stability. Our work presents a potential value in the application and implementation of conditionally automated vehicles.