Abstract. Accumulating evidence from epidemiological studies indicates that chronic inflammation and oxidative stress play critical roles in neoplastic development. The aim of this study was to investigate the anti-inflammatory, anti-oxidative stress activities, and differential regulation of Nrf2-mediated genes by tea Chrysanthemum zawadskii (CZ) and licorice Glycyrrhiza uralensis (LE) extracts. The antiinflammatory and anti-oxidative stress activities of hexane/ethanol extracts of CZ and LE were investigated using in vitro and in vivo approaches, including quantitative real-time PCR (qPCR) and microarray. Additionally, the role of the transcriptional factor Nrf2 (nuclear erythroid-related factor 2) signaling pathways was examined. Our results show that CZ and LE extracts exhibited potent antiinflammatory activities by suppressing the mRNA and protein expression levels of pro-inflammatory biomarkers IL-1β, IL-6, COX-2 and iNOS in LPS-stimulated murine RAW 264.7 macrophage cells. CZ and LE also significantly suppressed the NO production of LPS-stimulated RAW 264.7 cells. Additionally, CZ and LE suppressed the NF-κB luciferase activity in human HT-29 colon cancer cells. Both extracts also showed strong Nrf2-mediated antioxidant/Phase II detoxifying enzymes induction. CZ and LE induced NQO1, Nrf2, and UGT and antioxidant response element (ARE)-luciferase activity in human hepatoma HepG2 C8 cells. Using Nrf2 knockout [Nrf2 (−/−)] and Nrf2 wild-type (+/+) mice, LE and CZ showed Nrf2-dependent transactivation of Nrf2-mediated antioxidant and phase II detoxifying genes. In summary, CZ and LE possess strong inhibitory effects against NF-κB-mediated inflammatory as well as strong activation of the Nrf2-ARE-anti-oxidative stress signaling pathways, which would contribute to their overall health promoting pharmacological effects against diseases including cancer.

The phantoms described in this work simulate the mechanical, optical, and acoustic properties of human skin tissues, vessel tissue, and blood. In this way, the phantoms are uniquely suited to serve as test models for multimodal imaging techniques and image-guided interventions.

edical robots promise enhanced precision, safety and efficacy by working beyond the limits of human perception and dexterity 1,2. Recent advancements in image guid

Accessing the venous bloodstream to deliver fluids or obtain a blood sample is the most common clinical routine practiced in the U.S. Practitioners continue to rely on manual venipuncture techniques, but success rates are heavily dependent on clinician skill and patient physiology. In the U.S., failure rates can be as high as 50% in difficult patients, making venipuncture the leading cause of medical injury. To improve the rate of first-stick success, we have developed a portable autonomous venipuncture device that robotically servos a needle into a suitable vein under image guidance. The device operates in real time, combining near-infrared and ultra-sound imaging, image analysis, and a 7-degree-of-freedom (DOF) robotic system to perform the venipuncture. The robot consists of a 3-DOF gantry to image the patient's peripheral forearm veins and a miniaturized 4-DOF serial arm to guide the cannula into the selected vein under closed-loop control. In this paper, we present the system architecture of the robot and evaluate the accuracy and precision through tracking, free-space positioning, and in vitro phantom cannulation experiments. The results demonstrate sub-millimeter accuracy throughout the operating workspace of the manipulator and a high rate of success when cannulating phantom veins in a skin-mimicking tissue model.

Large-droplet macrovesicular steatosis (ld-MaS) in over 30% of the liver graft hepatocytes is a major risk factor in liver transplantation. An accurate assessment of ld-MaS percentage is crucial to determine liver graft transplantability, which is currently based on pathologists’ evaluations of hematoxylin and eosin (H&E) stained liver histology specimens, with the predominant criteria being the lipid droplets’ (LDs) relative size and their propensity to displace the hepatocyte’s nucleus to the cell periphery. Automated image analysis systems aimed at objectively and reproducibly quantifying ld-MaS do not accurately differentiate large LDs from small-droplet macrovesicular steatosis (sd-MaS) and do not take into account LD-mediated nuclear displacement, leading to poor correlation with pathologists’ assessment. Here we present an improved image analysis method that incorporates nuclear displacement as a key image feature to segment and classify ld-MaS from H&E stained liver histology slides. More than 52,000 LDs in 54 digital images from 9 patients were analyzed, and the performance of the proposed method was compared against that of current image analysis methods and the ld-MaS percentage evaluations of two trained pathologists from different centers. We show that combining nuclear displacement and LD size information significantly improves the separation between large and small macrovesicular LDs (specificity=93.7%, sensitivity=99.3%) and the correlation with the pathologists’ ld-MaS percentage assessment (R2=0.97). This performance vastly exceeds that of other automated image analyzers, which typically underestimate or overestimate the pathologists’ ld-MaS score. This work demonstrates the potential of automated ld-MaS analysis in monitoring the steatotic state of livers. The image analysis principles demonstrated here may help standardize ld-MaS scores among centers and ultimately help in the process of determining liver graft transplantability.