Department of Physics, Chemistry and Biology (IFM), Linköping University, 58183, Linköping, Sweden.
Published: August 2019
AI Article Synopsis
Article Abstract
Untethered actuation is important for robotic devices to achieve autonomous motion, which is typically enabled by using batteries. Using enzymes to provide the required electrical charge is particularly interesting as it will enable direct harvesting of fuel components from a surrounding fluid. Here, a soft artificial muscle is presented, which uses the biofuel glucose in the presence of oxygen. Glucose oxidase and laccase enzymes integrated in the actuator catalytically convert glucose and oxygen into electrical power that in turn is converted into movement by the electroactive polymer polypyrrole causing the actuator to bend. The integrated bioelectrode pair shows a maximum open-circuit voltage of 0.70 ± 0.04 V at room temperature and a maximum power density of 0.27 µW cm at 0.50 V, sufficient to drive an external polypyrrole-based trilayer artificial muscle. Next, the enzymes are fully integrated into the artificial muscle, resulting in an autonomously powered actuator that can bend reversibly in both directions driven by glucose and O only. This autonomously powered artificial muscle can be of great interest for soft (micro-)robotics and implantable or ingestible medical devices manoeuvring throughout the body, for devices in regenerative medicine, wearables, and environmental monitoring devices operating autonomously in aqueous environments.
A vast multitude of tasks in histopathology could potentially benefit from the support of artificial intelligence (AI). Many examples have been shown in the literature and first commercial products with FDA or CE-IVDR clearance are available. However, two key challenges remain: (1) a scarcity of thoroughly annotated images, respectively the laboriousness of this task, and (2) the creation of robust models that can cope with the data heterogeneity in the field (domain generalization).
Background & Aims: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables.
Methods: An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software.
Changes in maternal nutrition during the periconceptional period can influence postnatal growth in cattle. This study aimed to identify the impact of supplementing beef cows with rumen-protected methionine (RP-Met) during the periconceptional period on their female progeny. In exp 1, plasma methionine (Met) levels were analyzed in samples from 10 Angus crossbred, non-lactating beef cows.
Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, China.
Objectives: To assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies.
Methods: The phantom images were reconstructed with the hybrid iterative algorithm (HIR: Karl 3D-3, 5, 7, 9) and AIIR (grades 1-5) algorithm. Noise power spectra (NPS), task transfer functions (TTF) were measured, and additionally sharpness was assessed using a "blur metric" procedure.
