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arXiv 2025年

作者： Huang, Yi Zhang, Ke Liu, Wei Wang, Yuanyuan Patel, Vishal M. Lu, Le Han, Xu Jin, Dakai Yan, Ke DAMO Academy Alibaba Group China Hupan Lab Hangzhou China Department of Biomedical Engineering Fudan University China Department of Electrical and Computer Engineering Johns Hopkins University United States Department of Hepatobiliary and Pancreatic Surgery The First Affiliated Hospital of College of Medicine Zhejiang University China

Accurate segmentation of tubular structures in medical images, such as vessels and airway trees, is crucial for computer-aided diagnosis, radiotherapy, and surgical planning. However, significant challenges exist in algorithm design when faced with diverse sizes, complex topologies, and (often) incomplete data annotation of these structures. We address these difficulties by proposing a new tubular structure segmentation framework named HarmonySeg. First, we design a deep-to-shallow decoder network featuring flexible convolution blocks with varying receptive fields, which enables the model to effectively adapt to tubular structures of different scales. Second, to highlight potential anatomical regions and improve the recall of small tubular structures, we incorporate vesselness maps as auxiliary information. These maps are aligned with image features through a shallow-and-deep fusion module, which simultaneously eliminates unreasonable candidates to maintain high precision. Finally, we introduce a topology-preserving loss function that leverages contextual and shape priors to balance the growth and suppression of tubular structures, which also allows the model to handle low-quality and incomplete annotations. Extensive quantitative experiments are conducted on four public datasets. The results show that our model can accurately segment 2D and 3D tubular structures and outperform existing state-of-the-art methods. External validation on a private dataset also demonstrates good generalizability. © 2025, CC BY.

关键词： Image segmentation

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Bioactive Glass Preloaded with Antibiotics for Delivery of Long-Term Localized Drug Release Exhibiting Inherent Antimicrobial Activity

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Applied Sciences (Switzerland) 2025年第10期15卷

作者： Almasri, Dana Dahman, Yaser Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University TorontoONM5B 2K3 Canada Department of Chemical Engineering Toronto Metropolitan University TorontoONM5B 2K3 Canada

Bacterial infections caused by biofilms are often difficult to treat due to the resistant nature of the latter, which require high concentrations of antibiotics to be applied for prolonged periods to achieve complete eradication. This study aimed to design a new drug delivery system for long-term drug release for the treatment of bacterial infections. Specifically, bioactive glass preloaded with vancomycin (BG-V) and synthesized without any thermal treatment was designed to load higher percentages of the drug and release it slowly over time. In this study, BG-V was-synthesized using a solution containing vancomycin, with glass being formed around it by adding metal precursors to this solution. The BG-V was then left to dry at room temperature to form a white powder with vancomycin trapped in the structure. The successful synthesis of BG-V was confirmed by X-ray diffraction, Fourier-transform infrared spectroscopy, scanning emission microscopy, and nitrogen adsorption–desorption analysis. The results showed that BG-V was successfully developed using FTIR, showing vancomycin within the BG-V structure. After the drug release, BG-V showed great bioactivity, as indicated by the XRD and bacterial studies. The result shows that drug release is controlled by a combination of diffusion through the matrix and the gradual erosion of the delivery system. © 2025 by the authors.

关键词： Fourier transform infrared spectroscopy

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Rapid measurement of the local pressure amplitude in microchannel acoustophoresis using motile cells

arXiv

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arXiv 2021年

作者： Kim, Minji Barnkob, Rune Meacham, J. Mark Department of Mechanical Engineering and Materials Science Washington University in St. Louis St. LouisMO63112 United States Heinz-Nixdorf-Chair of Biomedical Electronics Department of Electrical and Computer Engineering Technical University of Munich TranslaTUM Munich81675 Germany

Acoustic microfluidics (or acoustofluidics) provides a non-contact and label-free means to manipulate and interrogate bioparticles. Owing to their biocompatibility and precision, acoustofluidic approaches have enabled innovations in various areas of biomedical research. Future breakthroughs will rely on translation of these techniques from academic labs to clinical and industrial settings. Here, accurate characterization and standardization of device performance is crucial. Versatile, rapid, and widely accessible performance quantification is needed. We propose a field quantification method using motile Chlamydomonas reinhardtii algae cells. We previously reported qualitative mapping of acoustic fields using living microswimmers as active probes. In the present study, we extend our approach to achieve the challenging quantitative in situ measurement of the acoustic energy density. C. reinhardtii cells continuously swim in an imposed force field and dynamically redistribute as the field changes. This behavior allows accurate and complete, real-time performance monitoring, which can be easily applied and adopted within the acoustofluidics and broader microfluidics research communities. Additionally, the approach relies only on standard bright-field microscopy to assess the field under numerous conditions within minutes. We benchmark the method against conventional passive-particle tracking, achieving agreement within 1 % for field strengths from 0 to 100 J m-3 (0 to ~1 MPa). © 2021, CC BY.

关键词： Microfluidics

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Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes

arXiv

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arXiv 2023年

作者： Mahbub, Maria Goethert, Ian Danciu, Ioana Knight, Kathryn Srinivasan, Sudarshan Tamang, Suzanne Rozenberg-Ben-Dror, Karine Solares, Hugo Martins, Susana Trafton, Jodie Begoli, Edmon Peterson, Gregory D. Cyber Resilience and Intelligence Division Oak Ridge National Laboratory Oak RidgeTN United States Information Technology Services Division Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Biomedical Informatics Vanderbilt University NashvilleTN United States Program Evaluation and Resource Center Office of Mental Health and Suicide Prevention Department of Veterans Affairs United States Department of Medicine Stanford University School of Medicine StanfordCA United States Veterans Affairs Great Lakes Health Care System WestchesterIL United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville KnoxvilleTN United States

Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a question-answering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care. © 2023, CC BY-NC-ND.

关键词： Data warehouses

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Raman Spectroscopy Reveals Photobiomodulation-Induced α-Helix to β-Sheet Transition in Tubulins: Potential Implications for Alzheimer's and Other Neurodegenerative Diseases

arXiv

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arXiv 2023年

作者： Di Gregorio, Elisabetta Staelens, Michael Hosseinkhah, Nazanin Karimpoor, Mahroo Liburd, Janine Lim, Lew Shankar, Karthik Tuszyński, Jack A. Department of Physics Faculty of Science University of Alberta EdmontonABT6G 2E1 Canada Faculty of Biomedical Engineering Polytechnic University of Turin Turin10129 Italy Department of Physics Freie Universität Berlin Berlin14195 Germany Instituto de Física Corpuscular CSIC Universitat de València Carrer Catedràtic José Beltrán 2 València Paterna46980 Spain Vielight Inc. TorontoONM4Y 2G8 Canada Department of Electrical and Computer Engineering Faculty of Engineering University of Alberta EdmontonABT6G 1H9 Canada Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland

In small clinical studies, the application of photobiomodulation (PBM), which typically delivers near-infrared (NIR) to treat the brain, has presented some remarkable results in the treatment of dementia and several neurodegenerative diseases. However, while the literature is rich with the mechanisms of action underlying PBM outcomes, the underlying mechanisms affecting a neurodegenerative disease are not entirely clear. While large clinical trials are warranted to validate these findings, evidence of the mechanisms can explain and hence provide credible support for PBM as a potential treatment for these diseases. Tubulin and its polymerized state of microtubules have been known to play important roles in the pathology of Alzheimer's and other neurodegenerative diseases. We investigated the effects of PBM on these structures in the quest for answers. In this study, we employed a Raman spectroscopic analysis of the amide I band of polymerized samples of tubulin exposed to pulsed low-intensity NIR radiation (810 nm, 10 Hz, 22.5 J/cm2 dose). Peaks in the Raman fingerprint region (300-1900 cm−1), in particular, in the amide I band (1600-1700 cm−1), can be used to quantify the percentage of protein secondary structures. Under this band, hidden signals of C=O stretching, belonging to different structures, are superimposed-producing a complex signal as a result. An accurate decomposition of the amide I band is therefore required for the reliable analysis of the conformation of proteins, which we achieved through a straightforward method employing a Voigt profile. This approach was validated through secondary structure analyses of unexposed control samples, for which comparisons with other values available in the literature could be conducted. Subsequently, using this validated method, we present novel findings of statistically significant alterations in the secondary structures of NIR-exposed tubulin, characterized by a notable decrease in α-helix content and a concurrent

关键词： Neurodegenerative diseases

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Deep Direct Visual Servoing of Tendon-Driven Continuum Robots

arXiv

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arXiv 2021年

作者： Abdulhafiz, Ibrahim Nazari, Ali A. Abbasi-Hashemi, Taha Jalali, Amir Zareinia, Kourosh Saeedi, Sajad Janabi-Sharifi, Farrokh Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada Department of Mechanical and Industrial Engineering Ryerson University TorontoONM5B 2K3 Canada

Vision-based control provides a significant potential for the end-point positioning of continuum robots under physical sensing limitations. Traditional visual servoing requires feature extraction and tracking followed by full or partial pose estimation, limiting the controller’s efficiency. We hypothesize that employing deep learning models and implementing direct visual servoing can effectively resolve the issue by eliminating such intermediate steps, enabling control of a continuum robot without requiring an exact system model. This paper presents the control of a single-section tendon-driven continuum robot using a modified VGG-16 deep learning network and an eye-in-hand direct visual servoing approach. The proposed algorithm is first developed in Blender software using only one input image of the target and then implemented on a real robot. The convergence and accuracy of the results in normal, shadowed, and occluded scenes demonstrate the effectiveness and robustness of the proposed controller. Copyright © 2021, The Authors. All rights reserved.

关键词： Visual servoing

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Magnetic Micromanipulation for In Vivo Measurement of Stiffness Heterogeneity and Anisotropy in the Mouse Mandibular Arch

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Research 2020年第1期2020卷 1409-1419页

作者： Min Zhu Kaiwen Zhang Hirotaka Tao Sevan Hopyan Yu Sun Department of Mechanical and Industrial Engineering University of TorontoCanada M5S 3G8 Program in Developmental and Stem Cell Biology Research InstituteThe Hospital for Sick ChildrenTorontoONCanada M5G 0A4 Department of Molecular Genetics University of TorontoCanada M5S 1A8 Division of Orthopaedic Surgery The Hospital for Sick Children and University of TorontoCanada M5G 1X8 Institute of Biomaterials and Biomedical Engineering University of TorontoCanada M5S 3G9 Department of Electrical and Computer Engineering University of TorontoCanada M5S 3G4

The mechanical properties of tissues are pivotal for morphogenesis and disease *** approaches have enabled measurements of the spatial distributions of viscoelastic properties among embryonic and pathological model systems and facilitated the generation of important hypotheses such as durotaxis and tissue-scale phase *** likely are many unexpected aspects of embryo biomechanics we have yet to discover which will change our views of mechanisms that govern development and *** area in the blind spot of even the most recent approaches to measuring tissue stiffness is the potentially anisotropic nature of that ***,we report a magnetic micromanipulation device that generates a uniform magnetic field gradient within a large workspace and permits measurement of the variation of tissue stiffness along three orthogonal *** applying the device to the organ-stage mouse embryo,we identify spatially heterogenous and directionally anisotropic stiffness within the mandibular *** properties correspond to the domain of expression and the angular distribution of fibronectin and have potential implications for mechanisms that orient collective cell movements and shape tissues during *** of anisotropic properties extends the repertoire of current methods and will enable the generation and testing of hypotheses.

关键词： transition. stiffness anisotropic

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Investigation on application of egg shell micro powder based epoxy composite for vibration isolation of MEMS sensor

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AIP Conference Proceedings 2023年第1期2976卷

作者： Nitin Satpute Atharva Joshi Kaustubh Utpat Nishant Kulkarni Siddharth Jabade Ludwin Molina Arias Ramesh Narina Marek Iwaniec Faculty of Science and Technology Vishwakarma University Pune India Department of Mechanical Engineering Vishwakarma Institute of Technology Pune India Department of Process Control AGH University of Science and Technology Krakow Poland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Biocybernetics and Biomedical Engineering AGH University of Science and Technology Krakow Poland

In the presented work, the stiffness and damping properties of eggshell-based epoxy composites are investigated. The ingredients used for the preparation of the composite include 25 - 45 µm sized eggshell powder, polydimethylsiloxane (PDMS), hardener, and silane as a coupling agent. Experimentation on prototype samples has been conducted using a vibration shaker, accelerometer, and fast Fourier transform (FFT) analyzer, to investigate the influence of the addition of micro-powder on the damping factor and stiffness of the composite. The study reveals that there is an improvement of up to 201% in damping factor with the 2.5% addition of micro powder by weight. The experimental study indicates that the stiffness properties of the composite are not influenced by the addition of eggshell micro powder. Additionally, a case study is demonstrated with finite element simulation to validate the advantages of using the proposed epoxy composite for Microelectromechanical System (MEMS) sensor packaging. In case of a harmonic force of 0.2 N acting on the sensor within the frequency range of 40-60 Hz, the maximum acceleration of the MEMS sensor increases by 48%, whereas the force transmissibility decreases by 44%.

关键词：

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Large-scale augmented granger causality (lsAGC) for connectivity analysis in complex systems: From computer simulations to functional MRI (fMRI)

arXiv

引用

arXiv 2021年

作者： Wismüller, Axel Vosoughi, M. Ali Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine Institute of Clinical Radiology Ludwig Maximilian University Munich Germany

We introduce large-scale Augmented Granger Causality (lsAGC) as a method for connectivity analysis in complex systems. The lsAGC algorithm combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. We quantitatively evaluate the performance of lsAGC on synthetic directional time-series networks with known ground truth. As a reference method, we compare our results with cross-correlation, which is typically used as a standard measure of connectivity in the functional MRI (fMRI) literature. Using extensive simulations for a wide range of time-series lengths and two different signal-to-noise ratios of 5 and 15 dB, lsAGC consistently outperforms cross-correlation at accurately detecting network connections, using Receiver Operator Characteristic Curve (ROC) analysis, across all tested time-series lengths and noise levels. In addition, as an outlook to possible clinical application, we perform a preliminary qualitative analysis of connectivity matrices for fMRI data of Autism Spectrum Disorder (ASD) patients and typical controls, using a subset of 59 subjects of the Autism Brain Imaging Data Exchange II (ABIDE II) data repository. Our results suggest that lsAGC, by extracting sparse connectivity matrices, may be useful for network analysis in complex systems, and may be applicable to clinical fMRI analysis in future research, such as targeting disease-related classification or regression tasks on clinical data. © 2021, CC BY.

关键词： Time series

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Convergence and Implicit Bias of Gradient Flow on Overparametrized Linear Networks

arXiv

引用

arXiv 2021年

作者： Min, Hancheng Tarmoun, Salma Vidal, René Mallada, Enrique Mathematical Institute for Data Science Johns Hopkins University United States Department of Electrical and Computer Engineering Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Biomedical Engineering Johns Hopkins University United States

Neural networks trained via gradient descent with random initialization and without any regularization enjoy good generalization performance in practice despite being highly overparametrized. A promising direction to explain this phenomenon is to study how initialization and overparametrization affect convergence and implicit bias of training algorithms. In this paper, we present a novel analysis of single-hidden-layer linear networks trained under gradient flow, which connects initialization, optimization, and overparametrization. Firstly, we show that the squared loss converges exponentially to its optimum at a rate that depends on the level of imbalance and the margin of the initialization. Secondly, we show that proper initialization constrains the dynamics of the network parameters to lie within an invariant set. In turn, minimizing the loss over this set leads to the min-norm solution. Finally, we show that large hidden layer width, together with (properly scaled) random initialization, ensures proximity to such an invariant set during training, allowing us to derive a novel non-asymptotic upper-bound on the distance between the trained network and the min-norm solution. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

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