Clinical Significance of Glycolytic Metabolic Activity in Hepatocellular Carcinoma

High metabolic activity is a hallmark of cancers, including hepatocellular carcinoma (HCC). However, the molecular features of HCC with high metabolic activity contributing to clinical outcomes and the therapeutic implications of these characteristics are poorly understood. We aimed to define the features of HCC with high metabolic activity and uncover its association with response to current therapies. By integrating gene expression data from mouse liver tissues and tumor tissues from HCC patients (n = 1038), we uncovered three metabolically distinct HCC subtypes that differ in clinical outcomes and underlying molecular biology. The high metabolic subtype is characterized by poor survival, the strongest stem cell signature, high genomic instability, activation of EPCAM and SALL4, and low potential for benefitting from immunotherapy. Interestingly, immune cell analysis showed that regulatory T cells (Tregs) are highly enriched in high metabolic HCC tumors, suggesting that high metabolic activity of cancer cells may trigger activation or infiltration of Tregs, leading to cancer cells\u27 evasion of anti-cancer immune cells. In summary, we identified clinically and metabolically distinct subtypes of HCC, potential biomarkers associated with these subtypes, and a potential mechanism of metabolism-mediated immune evasion by HCC cells

Optic Disc and Fovea Localisation in Ultra-widefield Scanning Laser Ophthalmoscope Images Captured in Multiple Modalities

We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina. Images captured in both reflectance and autofluorescence (AF) modes, and central pole and eyesteered gazes, were used. The method achieved an OD localisation accuracy of 99.4% within one OD radius, and fovea localisation accuracy of 99.1% within one OD radius on a test set comprising of 1790 images. The performance of fovea localisation in AF images was comparable to the variation between human annotators at this task. The laterality of the image (whether the image is of the left or right eye) was inferred from the OD and fovea coordinates with an accuracy of 99.9%

3D-Printed Triboelectric Nanogenerator Sensors for Soft Haptic Interfaces

Haptic interfaces allow us to interact with the virtual environment by sensing our realistic tactile information and delivering them to the computer system. However, rigid and cumbersome form factors of haptic interfaces limit the comfortable interfaces and various applications. Here we report triboelectric-based 3D-printed soft haptic interfaces with free-form factor. An elastic material 3D printing enables us to fabricate soft haptic interfaces with 3D sensors as we desire. After printing some components of the 3D flexible sensors, we use the active materials to achieve surface modification of printed components and assemble those functionalized components into a single structure. When we apply the external force to the sensor structure with multiple components, those components are contacted or separated from each other. Due to their different surface electron affinities the triboelectric effect occurs, which derives the electron transfer and generates the electrical signal. This electrical signal is transmitted as realistic tactile information to the computer system for interacting with the virtual environmental system. We expect these soft haptic interfaces will contribute to expand the haptic technologies for various applications

Encoding of multi-modal emotional information via personalized skin-integrated wireless facial interface

Human affects such as emotions, moods, feelings are increasingly being considered as key parameter to enhance the interaction of human with diverse machines and systems. However, their intrinsically abstract and ambiguous nature make it challenging to accurately extract and exploit the emotional information. Here, we develop a multi-modal human emotion recognition system which can efficiently utilize comprehensive emotional information by combining verbal and non-verbal expression data. This system is composed of personalized skin-integrated facial interface (PSiFI) system that is self-powered, facile, stretchable, transparent, featuring a first bidirectional triboelectric strain and vibration sensor enabling us to sense and combine the verbal and non-verbal expression data for the first time. It is fully integrated with a data processing circuit for wireless data transfer allowing real-time emotion recognition to be performed. With the help of machine learning, various human emotion recognition tasks are done accurately in real time even while wearing mask and demonstrated digital concierge application in VR environment.Published versionThis work was supported by National Research Foundation of Korea (NRF) grants funded by the Korean government, NRF-2020R1A2C2102842, NRF-2021R1A4A3033149, NRF-RS-2023-00302525, the Fundamental Research Program of the Korea Institute of Material Science, PNK7630 and Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (P0023703, HRD Program for Industrial Innovation)

Neighborhood Safety and Hypertension Risk: A Systematic Review

Background Responding to the increasing focus on residential environments, our systematic review aimed to consolidate existing empirical evidence regarding the impact of neighborhood safety on blood pressure. We also summarized the mediating and moderating mechanisms through which neighborhood safety influences blood pressure, alongside their direct effects, to offer insights for future research. Methods We searched 5 electronic databases (PubMed, Ovid MEDLINE, CINAHL Complete, ProQuest Dissertations and Theses Global, and Web of Science) for the period up to and including December 27, 2022. The initial search yielded 4944 studies reviewed, of which 19 met our criteria and were reviewed. Results Our findings consistently show that living in a safe neighborhood is associated with lower blood pressure outcomes. While most cross‐sectional studies found that the association was not statistically significant (7/10 studies showed insignificant results), longitudinal studies that tracked changes in neighborhood safety over time (4/5 studies) showed significant negative associations between neighborhood safety and blood pressure. Additionally, some studies identified sex (n=3), age (n=2), and neighborhood characteristics (n=4) as significant moderators, with the strength of the association between neighborhood safety and blood pressure varying across different demographic groups and neighborhood contexts. Conclusions Our findings suggest that unsafe neighborhoods may increase blood pressure and hypertension risk, warranting further research and interventions. This review also highlights the importance of adopting longitudinal designs, especially those using time‐varying measures of neighborhood environments