The history of medical physics in Asia-Oceania goes back to the late nineteenth century when X-ray imaging was introduced, although medical physicists were not appointed until much later. Medical physics developed very quickly in some countries, but in others the socio-economic situation as such prevented it being established for many years. In others, the political situation and war has impeded its development. In many countries their medical physics history has not been well recorded and there is a danger that it will be lost to future generations. In this paper, brief histories of the development of medical physics in most countries in Asia-Oceania are presented by a large number of authors to serve as a record. The histories are necessarily brief; otherwise the paper would quickly turn into a book of hundreds of pages. The emphasis in each history as recorded here varies as the focus and culture of the countries as well as the length of their histories varies considerably.
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Front Biosci (Landmark Ed)
December 2024
Research Centre for Medical Genetics, 115522 Moscow, Russia.
Background: There is a growing interest in exploring the biological characteristics of nanoparticles and exploring their potential applications. However, there is still a lack of research into the potential genotoxicity of fullerene derivatives and their impact on gene expression in human cells. In this study, we investigated the effects of a water-soluble fullerene derivative, C60[C6H4SCH2COOK]5H (F1), on human embryonic lung fibroblasts (HELF).
View Article and Find Full Text PDFChem Biomed Imaging
December 2024
Experimental Solid State Physics Group, Department of Physics, Imperial College, Exhibition Road, SW72AZ London, U.K.
Mesoporous silica nanoparticles (MSNPs) are promising nanomedicine vehicles due to their biocompatibility and ability to carry large cargoes. It is critical in nanomedicine development to be able to map their uptake in cells, including distinguishing surface associated MSNPs from those that are embedded or internalized into cells. Conventional nanoscale imaging techniques, such as electron and fluorescence microscopies, however, generally require the use of stains and labels to image both the biological material and the nanomedicines, which can interfere with the biological processes at play.
View Article and Find Full Text PDFOptica
December 2024
Department of Medical Physics and Biomedical Engineering, University College London, London, WC1E 6BT, UK.
X-ray dark-field imaging highlights sample structures through contrast generated by sub-resolution features within the inspected volume. Quantifying dark-field signals generally involves multiple exposures for phase retrieval, separating contributions from scattering, refraction, and attenuation. Here, we introduce an approach for non-interferometric X-ray dark-field imaging that presents a single-parameter representation of the sample.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.
The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global versus local signaling patterns. However, there is no consensus for how to best define the two states. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, and , from functional MRI data.
View Article and Find Full Text PDFProc IEEE Int Symp Biomed Imaging
May 2024
Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA.
In medical image segmentation, although multi-modality training is possible, clinical translation is challenged by the limited availability of all image types for a given patient. Different from typical segmentation models, modality-agnostic (MAG) learning trains a single model based on all available modalities but remains input-agnostic, allowing a single model to produce accurate segmentation given any modality combinations. In this paper, we propose a novel frame-work, MAG learning through Multi-modality Self-distillation (MAG-MS), for medical image segmentation.
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