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Understanding ADC variation by fat content effect using a dual-function MRI phantom

European Radiology Experimental 2024年第1期8卷 19页

作者： Liu, Yi-Jui Tsai, Tung-Sheng Li, Ya-Hui Peng, Jo-Hua Chang, Hing-Chiu Peng, Hsu-Hsia Lee, Ying-Chieh Lee, Tung-Yang Liou, Chang-Hsien Lin, Tz-Feng Chew, Fatt-Yang Chou, Ruey-Hwang Juan, Chun-Jung Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital 199 Sec. 1 Xinglong Rd. Hsinchu County Zhubei City 302 Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Biomedical Engineering Chinese University of Hong Kong Hong Kong Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Institute of Nuclear Engineering and Science National Tsing Hua University Hsinchu Taiwan Department of Fiber and Composite Materials Feng Chia University Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan Department of Radiology School of Medicine China Medical University Taichung Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung 406 Taiwan Center for Molecular Medicine China Medical University Hospital Taichung Taiwan Department of Medical Laboratory and Biotechnology Asia University Taichung Taiwan

Background: A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs. Methods: A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the “iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ,” IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC. Results: The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R² = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs. Conclusions: A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC. Relevance statement: The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements. Key points: • A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values

关键词： Adipose tissue Diffusion magnetic resonance imaging Phantoms (imaging) Quality assurance (healthcare) Quality control

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Biofabrication and biomanufacturing in Ireland and the UK

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Bio-Design and Manufacturing 2024年第6期7卷 825-856页

作者： Jack F.Murphy Martha Lavelle Lisa Asciak Ross Burdis Hannah J.Levis Cosimo Ligorio Jamie Mc Guire Marlene Polleres Poppy O.Smith Lucinda Tullie Juan Uribe-Gomez Biqiong Chen Jonathan I.Dawson Julien E.Gautrot Nigel M.Hooper Daniel J.Kelly Vivian S.W.Li Alvaro Mata Abhay Pandit James B.Phillips Wenmiao Shu Molly M.Stevens Rachel L.Williams James P.K.Armstrong Yan Yan Shery Huang Department of Engineering University of CambridgeCambridge CB21PZUK Department of Translational Health Sciences Bristol Medical SchoolUniversity of BristolBristol BS13NYUK Department of Biomedical Engineering University of StrathclydeGlasgow G40NWUK Department of Materials Institute of Biomedical EngineeringImperial College LondonLondon SW72AZUK Department of Bioengineering Institute of Biomedical EngineeringImperial College LondonLondon SW72AZUK Department of Eye and Vision Science Institute of Life Course and Medical SciencesUniversity of LiverpoolLiverpool L78TXUK Biodiscovery Institute University of NottinghamNottingham NG72RDUK Department of Chemical and Environmental Engineering University of NottinghamNottingham NG72RDUK Centre for Human Development Stem Cells and RegenerationHuman Development and HealthFaculty of MedicineUniversity of SouthamptonSouthampton S0166YDUK Division of Neuroscience School of Biological SciencesFaculty of BiologyMedicine and HealthUniversity of ManchesterManchester M139PTUK UCL Centre for Nerve Engineering Department of PharmacologyUCL School of PharmacyUniversity College LondonLondon WC1N 1AXUK Stem Cell and Cancer Biology Laboratory The Francis Crick InstituteLondon NW11ATUK CÚRAM SFI Research Centre for Medical DevicesUniversity of GalwayGalway H91 W2TYIreland School of Mechanical and Aerospace Engineering Queen’s University BelfastBelfast BT95AHUK School of Engineering and Materials Science Queen Mary University of LondonLondon E14NSUK Geoffrey Jefferson Brain Research Centre Manchester Academic Health Science CentreNorthern Care Alliance and University of ManchesterManchester M139PLUK Trinity Centre for Biomedical Engineering Trinity Biomedical Sciences InstituteTrinity College DublinDublin 2Ireland Department of Mechanical Manufacturing and Biomedical EngineeringSchool of EngineeringTrinity College DublinDublin 2Ireland Advanced Materials and Bioengineering Research Centre(AMBER) Royal College of Surgeons in Ireland and T

As we navigate the transition from the Fourth to the Fifth Industrial Revolution,the emerging fields of biomanufacturing and biofabrication are transforming life sciences and *** sectors are benefiting from a synergy of synthetic and engineering biology,sustainable manufacturing,and integrated design *** techniques such as 3D bioprinting,tissue engineering,directed assembly,and self-assembly are instrumental in creating biomimetic scaffolds,tissues,organoids,medical devices,and biohybrid *** field of biofabrication in the United Kingdom and Ireland is emerging as a pivotal force in bioscience and healthcare,propelled by cutting-edge research and *** on the production of biologically functional products for use in drug delivery,in vitro models,and tissue engineering,research institutions across these regions are dedicated to innovating healthcare solutions that adhere to ethical standards while prioritising sustainability,affordability,and healthcare system benefits.

关键词： Bioprinting Drug delivery Biomaterials Tissue engineering Sustainability Biohybrid

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Cytometry in the Short-Wave Infrared

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ACS Nano 2024年第28期18卷 18534-18547页

作者： Liu, Te-I Wang, Jhih-Shan Nguyen, Ai-Phuong Raabe, Marco Quiroz Reyes, Carlos Jose Lin, Chih-Hsin Lin, Ching-Wei Institute of Atomic and Molecular Sciences Academia Sinica Taipei City106319 Taiwan Department of Materials Science and Engineering National Taiwan University Taipei City106319 Taiwan Department of Physics University of Stuttgart Stuttgart70174 Germany Department of Chemistry National Tsing Hua University Hsinchu300044 Taiwan International Ph.D. Program in Biomedical Engineering Taipei Medical University New Taipei City235603 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University New Taipei City235603 Taiwan

Cytometry plays a crucial role in characterizing cell properties, but its restricted optical window (400-850 nm) limits the number of stained fluorophores that can be detected simultaneously and hampers the study and utilization of short-wave infrared (SWIR;900-1700 nm) fluorophores in cells. Here we introduce two SWIR-based methods to address these limitations: SWIR flow cytometry and SWIR image cytometry. We develop a quantification protocol for deducing cellular fluorophore mass. Both systems achieve a limit of detection of ∼0.1 fg cell-1 within a 30 min experimental time frame, using individualized, high-purity (6,5) single-wall carbon nanotubes as a model fluorophore and macrophage-like RAW264.7 as a model cell line. This high-sensitivity feature reveals that low-dose (6,5) serves as an antioxidant, and cell morphology and oxidative stress dose-dependently correlate with (6,5) uptake. Our SWIR cytometry holds immediate applicability for existing SWIR fluorophores and offers a solution to the issue of spectral overlapping in conventional cytometry. © 2024 The Authors. Published by American Chemical Society.

关键词： Oxygen

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Fully rubbery synaptic transistors made out of all-organic materials for elastic neurological electronic skin

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Nano Research 2022年第2期15卷 758-764页

作者： Hyunseok Shim Seonmin Jang Jae Gyu Jang Zhoulyu Rao Jong-ln Hong Kyoseung Sim Cunjiang Yu Materials Science and Engineering Program University of HoustonHoustonTX 77204 USA Department of Chemistry Seoul National UniversitySeoul 08826Republic of Korea Department of Chemistry Ulsan National Institute of Science and Technology(UNIST)Ulsan 44919Republic of Korea Department of Mechanical Engineering University of HoustonHoustonTX 77204 USA Department of Biomedical Engineering University of HoustonHoustonTX 77204USA Department of Electrical and Computer Engineering University of HoustonHoustonTX 77204 USA Texas Center for Superconductivity University of HoustonHoustonTX 77204USA

Neurologic function implemented soft organic electronic skin holds promise for wide range of applications,such as skin prosthetics,neurorobot,bioelectronics,human-robotic interaction(HRI),***,we report the development of a fully rubbery synaptic transistor which consists of all-organic materials,which shows unique synaptic characteristics existing in biological *** synaptic characteristics retained even under mechanical stretch by 30%.We further developed a neurological electronic skin in a fully rubbery format based on two mechanoreceptors(for synaptic potentiation or depression)of pressure-sensitive rubber and an all-organic synaptic *** converting tactile signals into Morse Code,potentiation and depression of excitatory postsynaptic current(EPSC)signals allow the neurological electronic skin on a human forearm to communicate with a robotic *** collective studies on the materials,devices,and their characteristics revealed the fundamental aspects and applicability of the all-organic synaptic transistor and the neurological electronic skin.

关键词： synaptic transistor stretchable electronic skin all-organic

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Overview on signal transduction cascades regulation roles of garlic and its bioactive constituents

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Food science and Human Wellness 2024年第5期13卷 2353-2362页

作者： Ammad Ahmad Farooqi Iqra Mobeen Rukset Attar Khalida I.Noel Baojun Xu William C.Cho Institute of Biomedical and Genetic Engineering(IBGE) Islamabad 44000Pakistan Khursheed Rasheed Hospital Lahore Lahore 53720Pakistan Department of Obstetrics and Gynecology Yeditepe UniversityAtaşehir 34755Turkey Human Anatomy Department College of MedicineAl-Mustansiriyah UniversityBaghdad 10036Iraq Food Science and Technology Program Department of Life SciencesBNU-HKBU United International CollegeZhuhai 519087China Department of Clinical Oncology Queen Elizabeth HospitalHong Kong G514TFChina

High-throughput technologies in combination with modern exciting advancements in mass spectrometry-based proteomics and data analysis pipelines have empowered comprehensive characterization of disease phenotypes and their mechanistic regulation by dietary agents and bioactive molecules at unprecedented dimensionality and ***-ordinary breakthroughs in the field of nutrigenomics have leveraged our understanding altogether to a new level of *** researchers have extensively analyzed health promoting and pharmacologically significant properties of garlic(Allium sativum).Importantly,garlic and its biologically active chemicals targeted oncogenic signaling *** this mini-review we have attempted to summarize how garlic and its bioactive constituents regulated signal transduction cascades in cell culture studies and tumor-bearing mice.

关键词： Cancer Nutrigenomics Signaling Apoptosis Allium sativum

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Smart Nanopipette Technology for Studying 2D and 3D Cancer Models

Smart Nanopipette Technology for Studying 2D and 3D Cancer M...

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2024 IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale, 3M-NANO 2024

作者： Vaneev, Alexander Timoshenko, Roman Kolmogorov, Vasilii Akasov, Roman Karshieva, Saida Koudan, Elizaveta Majouga, Alexander Gorelkin, Peter Erofeev, Alexander National University of Science and Technology ''MISIS Laboratory of Biophysics Moscow Russia I.M. Sechenov First Moscow State Medical University Laboratory of Molecular Pharmacology Moscow Russia National University of Science and Technology ''MISIS Center for Biomedical Engineering Moscow Russia National University of Science and Technology ''MISIS Department of Physical Materials Science Moscow Russia

ISBN: (纸本)9798350362107

This study presents a novel approach for studying 3D spheroids using glass nanopipettes and nanoelectrodes. The innovative application of these tools allows the investigation of spheroids from multiple perspectives. The first demonstration of this approach involved the use of scanning ion conductance microscopy (SICM), which enabled the spheroidal surface topography of U87MG (human glioblastoma) to be obtained for the first time. This was complemented by the mapping of Young's module. Furthermore, the electrochemical approach enabled the internal oxygen, reactive oxygen species(ROS) profiles to be obtained within the spheroids formed from primary cortical neurons and U87MG spheroids, representing a novel advancement in this field. The principal advantage of nanocapillary-based electrodes is their ability to perform minimally invasive measurements in the interior regions of spheroids, thereby facilitating precise electrochemical detection of oxygen and ROS levels at a given spheroid depth. This technological advance represents a significant step forward, opening up new opportunities for minimally invasive, high-resolution spheroid studies that can improve the development of therapies for tumor and neurodegenerative diseases. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Design and Feasibility Test of In-House Indirect Perfusion Bioreactor for 3-D Cell Culture 15

Design and Feasibility Test of In-House Indirect Perfusion B...

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15th biomedical engineering International Conference, BMEiCON 2023

作者： Suwannaphan, T. Tharasanit, T. Wongpakham, T. Pimpin, A. Chessadangkul, K. King Mongkut's University of Technology North Bangkok Department of Mechanical Engineering Technology Bangkok Thailand Faculty of Veterinary Science Chulalongkorn University Department of Obstetrics Gynaecology and Reproduction Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Mechanical Engineering Bangkok Thailand Chulalongkorn University Biomedical Engineering Program Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9798350345247

Selection of bioreactor system depends on many factors such as type of products, process of culture, and product density. No standard for development methodology is available nowadays. This work demonstrated the development of multi mini-chamber indirect perfusion bioreactor to support 3-D cell culture. The systems consisted of scaffolds, cell/scaffold retainers, culture tanks with a single peristaltic pump. Flow simulation was conducted to investigate the effects of flow rate and inlets/outlets' position on the distribution of shear stresses on the cell/scaffold cassettes installed inside the bioreactor. The prototype with 4 culture tanks connected in series was tested for long period operation. Lastly, preliminary experiments to culture and differentiate pig muscle stem cells were conducted. The results showed that muscle cells were successfully differentiated both on glass slides and aloe vera scaffolds inside the developed bioreactor. © 2023 IEEE.

关键词： Flow simulation

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Towards a Healthy AI Tradition: Lessons from Biology and biomedical science

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Bioinformatics Graduate Program Department of Computer Science Boston University United States Department of Computer Science Tel Aviv University

AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and more. The number of applications and impact of AI is second to none and the potential of AI to broadly impact future science developments is particularly thrilling. While attempts to understand knowledge, reasoning, cognition and learning go back centuries, AI remains a relatively new field. In part due to the fact it has so many wide-ranging overlaps with other disparate fields it appears to have trouble developing a robust identity and culture. Here we suggest that contrasting the fast-moving AI culture to biological and biomedical sciences is both insightful and useful way to inaugurate a healthy tradition needed to envision and manage our ascent to AGI and beyond (independent of the AI Platforms used). After all, the human brain is a biological organ produced by evolution and human intelligence is a remarkable bi-product of nature and nurture and their complex interaction. In this perspective, we focus on traditions and culture, namely the commonly observed practices of evaluating, recognizing applauding, critiquing, debating and managing all progress including useful advances and discovery of challenging limitations. We are not discussing specific scientific exchanges between AI and Biology that include interdisciplinary cross fertilization of scientific methods, technology, ideas and applications that have been amply demonstrated and will continue to be transformative in the future. In a previous perspective, we suggested that biomedical laboratories or centers can usefully embrace logistic traditions in AI labs that will allow them to be highly collaborative, improve the reproducibility of research, reduce risk aversion and produce faster mentorship pathways for PhDs and fellows. This perspective focuses on the benefits of AI a

关键词： Biological organs

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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