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Abstract During cardiac development, mechanotransduction from the in vivo microenvironment modulates cardiomyocyte growth in terms of the number, area, and arrangement heterogeneity. However, the response of cells to different degrees of mechanical stimuli is unclear. Organ-on-a-chip, as a platform for investigating mechanical stress stimuli in cellular mimicry of the in vivo microenvironment, is limited by the lack of ability to accurately quantify externally induced stimuli. However, previous technology lacks the integration of external stimuli and feedback sensors in microfluidic platforms to obtain and apply precise amounts of external stimuli. Here, we designed a cell stretching platform with an in-situ sensor. The in-situ liquid metal sensors can accurately measure the mechanical stimulation caused by the deformation of the vacuum cavity exerted on cells. The platform was applied to human cardiomyocytes (AC16) under cyclic strain (5%, 10%, 15%, 20 and 25%), and we found that cyclic strain promoted cell growth induced the arrangement of cells on the membrane to gradually unify, and stabilized the cells at 15% amplitude, which was even more effective after 3 days of culture. The platform’s precise control and measurement of mechanical forces can be used to establish more accurate in vitro microenvironmental models for disease modeling and therapeutic research.

Abstract Sedentary, inadequate sleep and exercise can affect human health. Artificial intelligence (AI) and Internet of Things (IoT) create the Artificial Intelligence of Things (AIoT), providing the possibility to solve these problems. This paper presents a novel approach to monitor various human behaviors for AIoT‐based health management using triboelectric nanogenerator (TENG) sensors. The insole with solely one TENG sensor, creating a most simplified system that utilizes machine learning (ML) for personalized motion monitoring, encompassing identity recognition and gait classification. A cushion with 12 TENG sensors achieves real‐time identity and sitting posture recognition with accuracy rates of 98.86% and 98.40%, respectively, effectively correcting sedentary behavior. Similarly, a smart pillow, equipped with 15 sensory channels, detects head movements during sleep, identifying 8 sleep patterns with 96.25% accuracy. Ultimately, constructing an AIoT‐based health management system to analyze these data, displaying health status through human‐machine interfaces, offers the potential to help individuals maintain good health.

Abstract Polarization, one of the fundamental properties of light, is critical for certain imaging applications because it captures information from the scene that cannot directly be recorded by traditional intensity cameras. Currently, mainstream approaches for polarization imaging rely on strong dichroism of birefringent crystals or artificially fabricated structures that exhibit a high diattenuation typically exceeding 99%, which corresponds to a polarization extinction ratio (PER) >~100. This not only limits the transmission efficiency of light, but also makes them either offer narrow operational bandwidth or be non-responsive to the circular polarization. Here, we demonstrate a single-shot full-Stokes polarization camera incorporating a disordered metasurface array with weak dichroism. The diattenuation of the metasurface array is ~65%, which corresponds to a PER of ~2. Within the framework of compressed sensing, the proposed disordered metasurface array serves as an efficient sensing matrix. By incorporating a mask-aware reconstruction algorithm, the signal can be accurately recovered with a high probability. In our experiments, the proposed approach exhibits high-accuracy full-Stokes polarimetry and high-resolution real-time polarization imaging. Our demonstration highlights the potential of combining meta-optics with reconstruction algorithms as a promising approach for advanced imaging applications.

Microvasculature plays a crucial role in human physiology and is closely related to various human diseases. Building in vitro vascular networks is essential for studying vascular tissue behavior with repeatable morphology and signaling conditions. Engineered 3D microvascular network models, developed through advanced microfluidic-based techniques, provide accurate and reproducible platforms for studying the microvasculature in vitro, an essential component for designing organ-on-chips to achieve greater biological relevance. By optimizing the microstructure of microfluidic devices to closely mimic the in vivo microenvironment, organ-specific models with healthy and pathological microvascular tissues can be created. This review summarizes recent advancements in in vitro strategies for constructing microvascular tissue and microfluidic devices. It discusses the static vascularization chips’ classification, structural characteristics, and the various techniques used to build them: growing blood vessels on chips can be either static or dynamic, and in vitro blood vessels can be grown in microchannels, elastic membranes, and hydrogels. Finally, the paper discusses the application scenarios and key technical issues of existing vascularization chips. It also explores the potential for a novel organoid chip vascularization approach that combines organoids and organ chips to generate better vascularization chips.