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42nd Society for Biomaterials Annual Meeting and Exposition 2019: The Pinnacle of Biomaterials Innovation and Excellence

作者： Xin, Shangjing Dai, Jing Han, Arum Alge, Daniel Department of Biomedical Engineering Texas A&M University United States Department of Electrical and Computer Engineering Texas A&M University United States

ISBN: (纸本)9781510883901

Statement of Purpose: Stem cell fate is influenced by the biochemical and biophysical cues presented by the extracellular matrix (ECM), and there is high interest in developing tissue engineering scaffolds with controlled heterogeneity to direct stem cell behavior. Assembling microgels into 3D tissue engineering scaffolds is a unique approach to address this challenge, as one key advantage is that this bottom-up strategy allows the incorporation of multiple formulations of microgels to generate spatial patterning of physicochemical properties. Random distributions or zones of distinct microgels in a single scaffold have been explored as injectable hydrogel systems. Here, we developed a microfluidic method to create poly(ethylene glycol) (PEG) gradient microgel scaffolds by tuning the flow rates of two gel components, with the objective of modulating human mesenchymal stem cell (hMSC) behaviors by presenting physicochemical gradients in these scaffolds. © 2019 Omnipress - All rights reserved.

关键词： Stem cells

Technological enhancements in personalized dietary management for chronic conditions

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Biomedical Engineering Advances

biomedical engineering Advances 2025年 10卷

作者： Tania Tahtouh Hadil Salman Nermin Eissa Najla Al Nassar Sofyan Maghaydah Marah Alhalabi Maha Yaghi Abdalla Gad Dana Abdallah Salma Elberry Aysha Alhosani Shaikha Alshehhi Mohammad Alkhedher Mohamad Ramadan Mohammed Ghazal Health Sciences Department Abu Dhabi University Abu Dhabi 59911 United Arab Emirates Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi 59911 United Arab Emirates Department of Mechanical Engineering Abu Dhabi University Abu Dhabi 59911 United Arab Emirates School of Engineering International University of Beirut Beirut 146404 Lebanon

Dietary compliance plays a vital role in the control of chronic diseases and influences response to therapy. The rapid development experienced nowadays through smart technology has enabled the personalization of dietary advice to meet individual needs. This paper provides an overview of the current technological solutions to dietary adherence among patients with chronic conditions. With the increasing prevalence of chronic diseases requiring diet-specific interventions, this review encompasses mobile apps and wearables to IoT-based devices and generative intelligence tools, including chatbots, aimed at offering specific guidance as well as providing individualized support for dietary requirements. This review provides an overview of the strengths and limitations of these approaches in real world applications, as well as emerging methodologies that attempt to improve this by focusing on means to personalize dietary management more effectively. The study suggests that technology-enabled dietary interventions can be useful to support chronic disease management and generative artificial intelligence may have a profound effect on the further personalization of dietary guidance. However, issues surrounding regulatory alignment, content validity and long-term user engagement remain to be resolved in order to maximize the tools’ functionality in clinical and home settings.

关键词： Dietary management Chronic conditions mHealth Personalized health Artificial intelligence Smart technology

Overcoming the fundamental limit of two-photon microscopy with non-degenerate excitation

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Overcoming the fundamental limit of two-photon microscopy wi...

Optics and the Brain, BRAIN 2020

作者： Sadegh, Sanaz Yang, Mu-Han Cheng, Xiaojun Thunemann, Martin Saisan, Payam A. Boas, David A. Fainman, Yeshaiahu Devor, Anna Department of Neurosciences University of California San DiegoCA92093 United States Department of Electrical and Computer Engineering University of California San DiegoCA92093 United States Department of Biomedical Engineering Boston University MA02215 United States Department of Radiology University of California San DiegoCA92093 United States Martinos Center for Biomedical Imaging MGH Harvard Medical School CharlestownMA02129 United States

We use non-degenerate two-photon excitation where the two excitation beams are displaced in space outside the focal volume to increase the signal-to-background ratio (SBR), overcoming the fundamental penetration depth... 详细信息

ISBN: (纸本)9781943580743

关键词： Photons

Enhanced Superconducting Qubit Performance Through Ammonium Fluoride Etch

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arXiv 2024年

作者： Kopas, Cameron J. Goronzy, Dominic P. Pham, Thang Torres Castanedo, Carlos G. Cheng, Matthew Cochrane, Rory Nast, Patrick Lachman, Ella Zhelev, Nikolay Z. Vallières, André Murthy, Akshay A. Oh, Jin-Su Zhou, Lin Kramer, Matthew J. Cansizoglu, Hilal Bedzyk, Michael J. Dravid, Vinayak P. Romanenko, Alexander Grassellino, Anna Mutus, Josh Y. Hersam, Mark C. Yadavalli, Kameshwar Rigetti Computing BerkeleyCA United States Department of Materials Science and Engineering Northwestern University EvanstonIL United States Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL United States Department of Physics and Astronomy Northwestern University EvanstonIL United States Department of Physics University of Oregon EugeneOR United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Fermi National Accelerator Laboratory BataviaIL United States Ames Laboratory U.S. Department of Energy AmesIA United States Northwestern University EvanstonIL United States Department of Chemistry Northwestern University EvanstonIL United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States

The performance of superconducting qubits is often limited by dissipation and two-level systems (TLS) losses. The dominant sources of these losses are believed to originate from amorphous materials and defects at interfaces and surfaces, likely as a result of fabrication processes or ambient exposure. Here, we explore a novel wet chemical surface treatment at the Josephson junction-substrate and the substrate-air interfaces by replacing a buffered oxide etch (BOE) cleaning process with one that uses hydrofluoric acid followed by aqueous ammonium fluoride. We show that the ammonium fluoride etch process results in a statistically significant improvement in median T1 by ∼ 22% (p = 0.002), and a reduction in the number of strongly-coupled TLS in the tunable frequency range. Microwave resonator measurements on samples treated with the ammonium fluoride etch prior to niobium deposition also show ∼ 33% lower TLS-induced loss tangent compared to the BOE treated samples. As the chemical treatment primarily modifies the Josephson junction-substrate interface and substrate-air interface, we perform targeted chemical and structural characterizations to examine materials’ differences at these interfaces and identify multiple microscopic changes that could contribute to decreased TLS. © 2024, CC BY-NC-ND.

关键词： Fluorine compounds

ResNet Structure Simplification with the Convolutional Kernel Redundancy Measure

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arXiv 2022年

作者： Zhu, Hongzhi Rohling, Robert Salcudean, Septimiu Department of Electrical and Computer Engineering University of British Columbia 5500-2332 Main Mall VancouverBCV6T 1Z4 Canada School of Biomedical Engineering University of British Columbia 251-2222 Health Sciences Mall VancouverBCV6T 1Z4 Canada Department of Mechanical Engineering University of British Columbia 2054-6250 Applied Science Lane VancouverBCV6T 1Z4 Canada

Deep learning, especially convolutional neural networks, has triggered accelerated advancements in computer vision, bringing changes into our daily practice. Furthermore, the standardized deep learning modules (also known as backbone networks), i.e., ResNet and EfficientNet, have enabled efficient and rapid development of new computer vision solutions. Yet, deep learning methods still suffer from several drawbacks. One of the most concerning problems is the high memory and computational cost, such that dedicated computing units, typically GPUs, have to be used for training and development. Therefore, in this paper, we propose a quantifiable evaluation method, the convolutional kernel redundancy measure, which is based on perceived image differences, for guiding the network structure simplification. When applying our method to the chest X-ray image classification problem with ResNet, our method can maintain the performance of the network and reduce the number of parameters from over 23 million to approximately 128 thousand (reducing 99.46% of the parameters). © 2022, CC BY-NC-SA.

关键词： Redundancy

Brain tumor classification using deep learning technique - A comparison between cropped, uncropped, and segmented lesion images with different sizes

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arXiv 2020年

作者： Alqudah, Ali Mohammad Alquraan, Hiam Qasmieh, Isam Abu Alqudah, Amin Al-Sharu, Wafaa Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan Department of Computer Engineering Yarmouk University Irbid Jordan Department of Electrical Engineering Hashemite University Zarqa Jordan

Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the researchers' attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accuracy and sensitivity, particularly in the medical field. In general, the brain tumor is one of the most common and aggressive malignant tumor diseases which is leading to a very short expected life if it is diagnosed at a higher grade. Based on that, brain tumor grading is a very critical step after detecting the tumor in order to achieve an effective treating plan. In this paper, we used Convolutional Neural Network (CNN) which is one of the most widely used deep learning architectures for classifying a dataset of 3064 T1 weighted contrast-enhanced brain MR images for grading (classifying) the brain tumors into three classes (Glioma, Meningioma, and Pituitary Tumor). The proposed CNN classifier is a powerful tool and its overall performance with an accuracy of 98.93% and sensitivity of 98.18% for the cropped lesions, while the results for the uncropped lesions are 99% accuracy and 98.52% sensitivity and the results for segmented lesion images are 97.62% for accuracy and 97.40% sensitivity.68Txx (Primary), 30F20 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

Temporal feature fusion with sampling pattern optimization for multi-echo gradient echo acquisition and image reconstruction

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arXiv 2021年

作者： Zhang, Jinwei Zhang, Hang Li, Chao Spincemaille, Pascal Sabuncu, Mert Nguyen, Thanh D. Wang, Yi Department of Biomedical Engineering Cornell University IthacaNY United States Department of Electrical and Computer Engineering Cornell University IthacaNY United States Department of Radiology Weill Medical College of Cornell University New YorkNY United States Department of Applied Physics Cornell University IthacaNY United States

Quantitative imaging in MRI usually involves acquisition and reconstruction of a series of images at multi-echo time points, which possibly requires more scan time and specific reconstruction technique compared to conventional qualitative imaging. In this work, we focus on optimizing the acquisition and reconstruction process of multi-echo gradient echo pulse sequence for quantitative susceptibility mapping as one important quantitative imaging method in MRI. A multi-echo sampling pattern optimization block extended from LOUPE-ST is proposed to optimize the k-space sampling patterns along echoes. Besides, a recurrent temporal feature fusion block is proposed and inserted into a backbone deep ADMM network to capture the signal evolution along echo time during reconstruction. Experiments show that both blocks help improve multi-echo image reconstruction performance. © 2021, CC BY.

关键词： Image reconstruction

MedNet: Pre-trained convolutional neural network model for the medical imaging tasks

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arXiv 2021年

作者： Alzubaidi, Laith Santamaría, J. Manoufali, Mohamed Mohammed, Beadaa Fadhel, Mohammed A. Zhang, Jinglan Al-Timemy, Ali H. Al-Shamma, Omran Duan, Ye School of Computer Science Queensland University of Technology BrisbaneQLD4000 Australia Department of Computer Science University of Jaén Jaén23071 Spain School of Information Technology and Electrical Engineering The University of Queensland Brisbane4072 Australia College of Computer Science and Information Technology University of Sumer Thi Qar64005 Iraq Biomedical Engineering Department Al-Khwarizmi College of Engineering University of Baghdad Baghdad10066 Iraq The University of Information Technology & Communications Baghdad10001 Iraq Faculty of Electrical Engineering & Computer Science University of Missouri ColumbiaMO65211 United States

In the last few years, deep learning (DL) methods have dramatically improved the state-of-the-art in visual object recognition, speech recognition, and object detection, among many other applications. Specifically, DL requires a large amount of training data to provide quality outcomes. However, the field of medical imaging suffers from the lack of sufficient data for properly training DL models because medical images require manual labelling carried out by clinical experts thus the process is time-consuming, expensive, and error prone. Recently, transfer learning (TL) was introduced to reduce the need for the annotation procedure by means of transferring the knowledge performed by a previous task and then fine-tuning the result using a relatively small dataset. Nowadays, multiple classification methods from medical imaging make use of TL from general-purpose pre-trained models, e.g., ImageNet, which has been proven to be ineffective due to the mismatch between the features learned from natural images (ImageNet) and those more specific from medical images especially medical gray images such as X-rays. ImageNet does not have grayscale images such as MRI, CT, and X-ray. In this paper, we propose a novel DL model to be used for addressing classification tasks of medical imaging, called MedNet. To do so, we aim to issue two versions of MedNet. The first one is Gray-MedNet which will be trained on 3M publicly available gray-scale medical images including MRI, CT, X-ray, ultrasound, and PET. The second version is Color-MedNet which will be trained on 3M publicly available color medical images including histopathology, taken images, and many other. To validate the effectiveness MedNet, both versions will be fine-tuned to train on the target tasks of a more reduced set of medical images. MedNet performs as the pre-trained model to tackle any real-world application from medical imaging and achieve the level of generalization needed for dealing with medical imaging tasks, e.g

关键词： Magnetic resonance imaging

Machine Learning Methods to Predicting Transvalvular Gradient Waveform Post-Transcatheter Aortic Valve Replacement Using Pre-procedural Echocardiogram

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The Journal of thoracic and cardiovascular surgery 2025年

作者： Wenyuan Song Taylor Sirset-Becker Luis René Mata Quinonez Dhruv Polsani Venkateshwar Polsani Pradeep Yadav Vinod Thourani Lakshmi Prasad Dasi School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA USA. Department of Biomedical Sciences The Ohio State University College of Medicine Columbus OH USA. Department of Cardiac Surgery Piedmont Heart Institute Atlanta GA USA. Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA USA. Electronic address: lakshmi.dasi@gatech.edu.

OBJECTIVE:Time-varying transvalvular pressure gradient after transcatheter aortic valve replacement indicates the effectiveness of the therapy. The objective was to develop a novel machine learning method enhanced by generative artificial intelligence and smart data selection strategies to predict the post-transcatheter aortic valve replacement gradient waveform using pre-procedural Doppler echocardiogram. METHODS:A total of 110 patients undergoing TAVR (mean age 78.2 ± 9.0 years, 52.5% female) were included for pressure gradient collection. A deep machine learning model was trained and tested to predict post-procedural pressure gradient waveform from pre-procedural pressure gradient waveform based on the proposed generative active learning framework. RESULTS:The trained model demonstrated an average prediction accuracy of 84.85% across the 10 test patients measured from the relative mean absolute error between the predicted gradient waveform and ground truth. The generative method improved prediction accuracy by 3.11%, while the data selection strategy increased it by 16.03% compared to the baseline experimental group using plain machine learning. Additionally, Bland-Altman analysis demonstrated a strong agreement between the proposed method and clinical measurements for both mean and peak pressure gradient predictions. CONCLUSIONS:A deep, generative, active machine learning model was developed to output the prediction of post-TAVR time-varying pressure gradient from the pre-procedural time-varying gradient obtained from Doppler echocardiogram. Such a predictive method may help guide decision-making for the prevention of various post-TAVR complications. Further studies are necessary to investigate the gradient change of other valve types.

关键词： Deep machine learning Generative active learning Generative artificial intelligence Predictive surgical planning Pressure gradient Transcatheter aortic valve implantation/replacement

Development of a Selective Wet-Chemical Etchant for 3D Structuring of Silicon via Nonlinear Laser Lithography

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arXiv 2023年

作者： Borra, Mona Zolfaghari Radfar, Behrad Nasser, Hisham Çolakoğlu, Tahir Tokel, Onur Turnalı, Ahmet Demirtaş, Merve Ustunel, Hande Toffoli, Daniele Ömer İlday, F. Turan, Raşit Pavlov, Ihor Bek, Alpan ODTÜ-GÜNAM Middle East Technical University Ankara06800 Turkey Ankara06800 Turkey Department of Electronics and Nanoengineering Aalto University Tietotie 3 EspooFI-02150 Finland Department of Physics Engineering Ankara University Ankara06100 Turkey Department of Physics Bilkent University Ankara06800 Turkey UNAM National Nanotechnology Research Center Institute of Materials Science and Nanotechnology Bilkent University Ankara06800 Turkey Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Department of Electrical and Computer Engineering Boston University 8 Saint Mary’s Street BostonMA02215 United States Ankara06800 Turkey Network Technologies Department TUBITAK ULAKBIM Ankara06800 Turkey Department of Chemical and Pharmaceutical Sciences Trieste University Trieste Italy IOM-CNR Istituto Officina dei Materiali Trieste34149 Italy Micro and Nanotechnology Graduate Program of Natural and Applied Sciences Middle East Technical Turkey

Recently-demonstrated high-quality three-dimensional (3D) subsurface laser processing inside crystalline silicon (c-Si) wafers opens a door to a wide range of novel applications in multidisciplinary research areas. Using this technique, a novel maskless micro-pillars with precise control on the surface reflection and coverage are successfully fabricated by etching the laser processed region of c-Si wafer. To achieve this, a particular selective wet chemical etching is developed to follow subsurface laser processing of c-Si to reveal the desired 3D structures with smooth surfaces. Here, we report the development of a novel chromium-free chemical etching recipe based on copper nitrate, which yields substantially smooth surfaces at high etch rate and selectivity on the both laser-processed Si surface and subsurface, i.e., without significant etching of the unmodified Si. Our results show that the etch rate and surface morphology are interrelated and strongly influenced by the composition of the adopted etching solution. After an extensive compositional study performed at room temperature, we identify an etchant with a selectivity of over 1600 times for laser-modified Si with respect to unmodified Si. We also support our findings using density functional theory calculations of HF and Cu adsorption energies, indicating significant diversity on the c-Si and laser-modified surfaces. Copyright © 2023, The Authors. All rights reserved.

关键词： Morphology