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Background and Aim Studies have found that gadolinium‐ethoxybenzyl‐diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced T1 mapping magnetic resonance imaging (MRI) could assess liver fibrosis, cirrhosis, and function with high effectiveness. The aim of this study is to explore the efficacy of MRI in predicting the safety of hepatectomy. Methods Forty‐nine patients who underwent liver resection were recruited. Gd‐EOB‐DTPA‐enhanced MRI examination was performed 1 week before surgery, and the rate of T1 relaxation time reduction (ΔT120min%) of liver parenchyma was calculated. Posthepatectomy liver failure (PHLF) was defined by the “50–50 criteria” and International Study Group of Liver Surgery (ISGLS) classification, respectively, and posthepatectomy complications (PHC) were defined by the Clavien‐Dindo grading system. The effectiveness of ΔT120min% in predicting the occurrence of PHLF and PHC was analyzed. Results The area under the curve (AUC) for ΔT120min% predicting PHLF meeting “50–50 criteria” was 0.957, with a cutoff value of 0.497, sensitivity of 100%, and specificity of 89.1%. The AUC for predicting ISGLS grade B/C (severe) PHLF was 0.84, with a cutoff value of 0.5232, sensitivity of 63.6%, and specificity of 92.6%. The AUC for predicting PHC of Clavien‐Dindo grades 3–5 (severe) was 0.882, with a cutoff value of 0.5646, sensitivity of 87.5%, and specificity of 75.8%. Univariate and multivariate analyses showed that ΔT120min%, We designed a preliminary study to find a new method to predict the safety of hepatectomy. Our study results proved that gadolinium‐ethoxybenzyl‐diethylenetriamine pentaacetic acid‐enhanced T1 mapping MRI could effectively predict the incidence of posthepatectomy liver failure and posthepatectomy complications.

Memristor crossbar with programmable conductance could overcome the energy consumption and speed limitations of neural networks when executing core computing tasks in image processing. However, the implementation of crossbar array (CBA) based on ultrathin 2D materials is hindered by challenges associated with large-scale material synthesis and device integration. Here, a memristor CBA is demonstrated using wafer-scale (2-inch) polycrystalline hafnium diselenide (HfSe2 ) grown by molecular beam epitaxy, and a metal-assisted van der Waals transfer technique. The memristor exhibits small switching voltage (0.6 V), low switching energy (0.82 pJ), and simultaneously achieves emulation of synaptic weight plasticity. Furthermore, the CBA enables artificial neural network with a high recognition accuracy of 93.34%. Hardware multiply-and-accumulate (MAC) operation with a narrow error distribution of 0.29% is also demonstrated, and a high power efficiency of greater than 8-trillion operations per second per Watt is achieved. Based on the MAC results, hardware convolution image processing can be performed using programmable kernels (i.e., soft, horizontal, and vertical edge enhancement), which constitutes a vital function for neural network hardware.

Wearable electronics have received considerable attention in recent years. These devices have penetrated every aspect of our daily lives and stimulated interest in futuristic electronics. Thus, flexible batteries that can be bent or folded are desperately needed, and their electrochemical functions should be maintained stably under the deformation states, given the increasing demands for wearable electronics. Carbon nanomaterials, such as carbon nanotubes, graphene, and/or their composites, as flexible materials exhibit excellent properties that make them suitable for use in flexible batteries. Herein, the most recent progress on flexible batteries using carbon nanomaterials is discussed from the viewpoint of materials fabrication, structure design, and property optimization. Based on the current progress, the existing advantages, challenges, and prospects are outlined and highlighted.