This study explores the integration of nature and art in recent hospital construction projects in Finland, focusing on public hospitals. Interviews with 15 stakeholders, including hospital executives, workers, designers, artists, and architects, reveal the value placed on nature and nature-themed art in hospital settings. The research question presented was: How nature and art are incorporated in Finnish hospitals in order to achieve a restorative hospital environment? Findings highlight themes that appeared in different hospitals: (1) the desired atmosphere, (2) nature and multisensory experiences, (3) social support, and (4) sense of connection and belonging. Bringing nature inside the hospital, whether through natural elements or artworks, emerges as a promising approach. Despite positive outcomes, challenges such as cost and maintenance persist, indicating the need for further research to optimize these initiatives. Overall, incorporating nature and art in hospitals has the potential to enhance healing and well-being for patients, families, and healthcare workers.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1177/19375867241239320 | DOI Listing |
J Imaging Inform Med
January 2025
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.
While radiation hazards induced by cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) can be reduced by sparse-view sampling, the image quality is inevitably degraded. We propose a deep learning-based multi-view projection synthesis (DLMPS) approach to improve the quality of sparse-view low-dose CBCT images. In the proposed DLMPS approach, linear interpolation was first applied to sparse-view projections and the projections were rearranged into sinograms; these sinograms were processed with a sinogram restoration model and then rearranged back into projections.
View Article and Find Full Text PDFAdv Drug Deliv Rev
January 2025
Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, 117997, Moscow, Russia; School of Mathematical and Physical Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia; Research Center for Translational Medicine, Sirius University of Science and Technology, 354340, Sochi, Russia; National Research Ogarev Mordovia State University, Saransk, Mordovia Republic 430005, Russia.
Once an exotic add-on to fluorescence microscopy for life science research, fluorescence lifetime imaging (FLIm) has become a powerful and increasingly utilised technique owing to its self-calibration nature, which affords superior quantification over conventional steady-state fluorescence imaging. This review focuses on the state-of-the-art implementation of FLIm related to the formulation, release, dosage, and mechanism of action of drugs aimed for innovative diagnostics and therapy. Quantitative measurements using FLIm have appeared instrumental for encapsulated drug delivery design, pharmacokinetics and pharmacodynamics, pathological investigations, early disease diagnosis, and evaluation of therapeutic efficacy.
View Article and Find Full Text PDFMed Image Anal
January 2025
Sixth Medical Center of Chinese PLA General Hospital, Beijing, 100037, China. Electronic address:
Medical Visual Question Answering aims to assist doctors in decision-making when answering clinical questions regarding radiology images. Nevertheless, current models learn cross-modal representations through residing vision and text encoders in dual separate spaces, which inevitably leads to indirect semantic alignment. In this paper, we propose UnICLAM, a Unified and Interpretable Medical-VQA model through Contrastive Representation Learning with Adversarial Masking.
View Article and Find Full Text PDFMed Image Anal
January 2025
Department of Computer and Data Science and Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA.
Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achieving reliable and consistent segmentation across different cases. Therefore, this study proposes a novel dynamic spectrum-driven hierarchical learning model (DSHNet), the first to specifically leverage image frequency domain information to explore region-level salience differences among and within polyps for precise segmentation.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Psychology, City College, City University of New York, New York, NY 10031.
Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive the world in the absence of active physiology, deliberative thought, or any form of feedback that resembles human affective experience offer tools to demystify the relationship between seeing and feeling, and to assess how much of visually evoked affective experiences may be a straightforward function of representation learning over natural image statistics. In this work, we deploy a diverse sample of 180 state-of-the-art deep neural network models trained only on canonical computer vision tasks to predict human ratings of arousal, valence, and beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!