Due to its excellent flexibility, graphene has an important application prospect in epidermal electronic sensors. However, there are drawbacks in current devices, such as sensitivity, range, lamination, and artistry. In this work, we have demonstrated a multilayer graphene epidermal electronic skin based on laser scribing graphene, whose patterns are programmable. A process has been developed to remove the unreduced graphene oxide. This method makes the epidermal electronic skin not only transferable to butterflies, human bodies, and any other objects inseparably and elegantly, merely with the assistance of water, but also have better sensitivity and stability. Therefore, pattern electronic skin could attach to every object like artwork. When packed in Ecoflex, electronic skin exhibits excellent performance, including ultrahigh sensitivity (gauge factor up to 673), large strain range (as high as 10%), and long-term stability. Therefore, many subtle physiological signals can be detected based on epidermal electronic skin with a single graphene line. Electronic skin with multiple graphene lines is employed to detect large-range human motion. To provide a deeper understanding of the resistance variation mechanism, a physical model is established to explain the relationship between the crack directions and electrical characteristics. These results show that graphene epidermal electronic skin has huge potential in health care and intelligent systems.

No abstract

No abstract

The flexible pressure sensor is one of the essential components of the wearable device, which is a critical solution to the applications of artificial intelligence and human−computer interactions in the future. Due to its simple manufacturing process and measurement methods, research related to piezoresistive mechanical sensors is booming, and those sensors are already widely used in industry. However, existing pressure sensors are almost all based on negative resistance variations, making it difficult to reach a balance between the sensitivity and the detection range. Here, we demonstrated a low-cost flexible pressure sensor with a positive resistance−pressure response based on laser scribing graphene. The sensor can be customized and modulated to achieve both an ultrahigh sensitivity and a broad detection range. Furthermore, the device possesses the signal amplification property like a mechanical triode under the external pressure bias. Based on its amplification ability, varieties of physiological signals and human movements have been detected using our devices; then, an integrated gait monitoring system has been realized. The reported positive graphene pressure sensor has outstanding capability, showing a wide application range such as intelligent perception, an interactive device, and real-time health/motion monitoring.

No abstract

Based on the good characteristics of graphene, many physiological signals can be detected by graphene sensors covering the human body. Graphene wearable sensors have great potential in healthcare and telemedicine.

Single-cell technologies have enabled the characterization of the tumour microenvironment at unprecedented depth and have revealed vast cellular diversity among tumour cells and their niche. Anti-tumour immunity relies on cell–cell relationships within the tumour microenvironment1,2, yet many single-cell studies lack spatial context and rely on dissociated tissues3. Here we applied imaging mass cytometry to characterize the immunological landscape of 139 high-grade glioma and 46 brain metastasis tumours from patients. Single-cell analysis of more than 1.1 million cells across 389 high-dimensional histopathology images enabled the spatial resolution of immune lineages and activation states, revealing differences in immune landscapes between primary tumours and brain metastases from diverse solid cancers. These analyses revealed cellular neighbourhoods associated with survival in patients with glioblastoma, which we leveraged to identify a unique population of myeloperoxidase (MPO)-positive macrophages associated with long-term survival. Our findings provide insight into the biology of primary and metastatic brain tumours, reinforcing the value of integrating spatial resolution to single-cell datasets to dissect the microenvironmental contexture of cancer.

Single-cell technologies have revealed the complexity of the tumour immune microenvironment with unparalleled resolution1–9. Most clinical strategies rely on histopathological stratification of tumour subtypes, yet the spatial context of single-cell phenotypes within these stratified subgroups is poorly understood. Here we apply imaging mass cytometry to characterize the tumour and immunological landscape of samples from 416 patients with lung adenocarcinoma across five histological patterns. We resolve more than 1.6 million cells, enabling spatial analysis of immune lineages and activation states with distinct clinical correlates, including survival. Using deep learning, we can predict with high accuracy those patients who will progress after surgery using a single 1-mm2 tumour core, which could be informative for clinical management following surgical resection. Our dataset represents a valuable resource for the non-small cell lung cancer research community and exemplifies the utility of spatial resolution within single-cell analyses. This study also highlights how artificial intelligence can improve our understanding of microenvironmental features that underlie cancer progression and may influence future clinical practice.