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Structural Mapping of Protein Aggregates in Live Cells Modeling Huntington's Disease

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Angewandte Chemie 2024年第35期136卷

作者： Zhongyue Guo Dr. Giulio Chiesa Jiaze Yin Adam Sanford Stefan Meier Prof. Ahmad S. Khalil Prof. Ji-Xin Cheng Department of Biomedical Engineering Boston University Boston MA 02215 USA Photonics Center Boston University Boston MA 02215 USA These authors contributed equally. Biological Design Center Boston University Boston MA 02215 USA Department of Electrical and Computer Engineering Boston University Boston MA 02215 USA Wyss Institute for Biologically Inspired Engineering Harvard University Boston MA 02215 USA

While protein aggregation is a hallmark of many neurodegenerative diseases, acquiring structural information on protein aggregates inside live cells remains challenging. Traditional microscopy does not provide structural information on protein systems. Routinely used fluorescent protein tags, such as Green Fluorescent Protein (GFP), might perturb native structures. Here, we report a counter-propagating mid-infrared photothermal imaging approach enabling mapping of secondary structure of protein aggregates in live cells modeling Huntington's disease. By comparing mid-infrared photothermal spectra of label-free and GFP-tagged huntingtin inclusions, we demonstrate that GFP fusions indeed perturb the secondary structure of aggregates. By implementing spectra with small spatial step for dissecting spectral features within sub-micrometer distances, we reveal that huntingtin inclusions partition into a β-sheet-rich core and a ɑ-helix-rich shell. We further demonstrate that this structural partition exists only in cells with the [ RNQ + ] prion state, while [ rnq − ] cells only carry smaller β-rich non-toxic aggregates. Collectively, our methodology has the potential to unveil detailed structural information on protein assemblies in live cells, enabling high-throughput structural screenings of macromolecular assemblies.

关键词： Vibrational Spectroscopy Protein Aggregation Secondary Structure Synthetic Biology Huntington's Disease

Rapid on-site amplification and visual detection of misfolded proteins via microfluidic quaking-induced conversion (Micro-QuIC)

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npj Biosensing

npj Biosensing 2024年第1期1卷 1-9页

作者： Dong Jun Lee Peter R. Christenson Sang-Hyun Oh Gage Rowden Peter A. Larsen Nathan C. Lindquist Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA Minnesota Center for Prion Research and Outreach University of Minnesota St. Paul MN USA Department of Veterinary and Biomedical Sciences University of Minnesota St. Paul MN USA Department of Physics and Engineering Bethel University St. Paul MN USA

Protein misfolding diseases, such as prion diseases, Alzheimer’s, and Parkinson’s, share a common molecular mechanism involving the misfolding and aggregation of specific proteins. There is an urgent need for point-of-care (POC) diagnostic technologies that can accurately detect these misfolded proteins, facilitating early diagnosis and intervention. Here, we introduce the microfluidic quaking-induced conversion (Micro-QuIC), a novel acoustofluidic platform for the rapid and sensitive detection of protein misfolding diseases. We demonstrate the utility of our technology using chronic wasting disease (CWD) as a model system, since samples from wild white-tailed deer are readily accessible, and CWD shares similarities with human protein misfolding diseases. Acoustofluidic mixing enables homogeneous mixing of reagents in a high-Reynolds-number regime, significantly accelerating the turnaround time for CWD diagnosis. Our Micro-QuIC assay amplifies prions significantly faster than the current gold standard, real-time quaking-induced conversion (RT-QuIC). Furthermore, we integrated Micro-QuIC with a gold nanoparticle-based, naked-eye detection method, which enables visual discrimination between CWD-positive and CWD-negative samples without the need for a bulky fluorescence detection module. This integration creates a rapid, POC testing platform capable of detecting misfolded proteins associated with a variety of protein misfolding diseases.

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Roles of scattered and ballistic photons in imaging through scattering media: a deep learning-based study

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arXiv 2022年

作者： Zhang, Xuyu Gao, Jingjing Gan, Yu Song, Chunyuan Zhang, Dawei Zhuang, Songlin Han, Shensheng Lai, Puxiang Liu, Honglin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Science Beijing100049 China Hangzhou Institute for Advanced study University of Chinese Academy of Sciences Hangzhou310024 China Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518000 China Photonics Research Institute The Hong Kong Polytechnic University Hong Kong

Scattering of light in complex media scrambles optical wavefronts and breaks the principles of conventional imaging methods. For decades, researchers have endeavored to conquer the problem by inventing approaches such as adaptive optics, iterative wavefront shaping, and transmission matrix measurement. That said, imaging through/into thick scattering media remains challenging to date. With the rapid development of computing power, deep learning has been introduced and shown potentials to reconstruct target information through complex media or from rough surfaces. But it also fails once coming to optically thick media where ballistic photons become negligible. Here, instead of treating deep learning only as an image extraction method, whose best-selling advantage is to avoid complicate physical models, we exploit it as a tool to explore the underlying physical principles. By adjusting the weights of ballistic and scattered photons through a random phasemask, it is found that although deep learning can extract images from both scattered and ballistic light, the mechanisms are different: scattering may function as an encryption key and decryption from scattered light is key sensitive, while extraction from ballistic light is stable. Based on this finding, it is hypothesized and experimentally confirmed that the foundation of the generalization capability of trained neural networks for different diffusers can trace back to the contribution of ballistic photons, even though their weights of photon counting in detection are not that significant. Moreover, the study may pave an avenue for using deep learning as a probe in exploring the unknown physical principles in various fields. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

Ovarian Cysts Classification Using Novel Deep Q-Learning With Harris Hawks Optimization Method

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Research Square

Research Square 2021年

作者： Narmatha, C. Manimegalai, P. Krishnadass, J. Valsalan, Prajoona Manimurugan, S. Karunya Institute of Technology and Sciences India Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Biomedical Engineering Karunya Deemed University Tamil Nadu Coimbatore India Sahrdaya College of Engineering and Technology Kodakara India Department of Electrical and Computer Engineering Dhofar University Salalah Oman Scientific Research and Consultant Unit Industrial Innovation & Robotics Center Department of Information Technology Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia

This research presents an essential solution for classifying ultrasound diagnostic images describing seven types of ovarian cysts: Follicular cyst, Hemorrhagic cyst, Corpus luteum cyst, Polycystic-appearing ovary, endometriosis cysts, Dermoid cyst, and Teratoma. This work proposed a novel technique using images of ovarian ultrasound cysts from an ongoing database with this motivation. Initially, the work is followed by removing noise in preprocessing, feature extraction, and finally classifying using new Deep Q-Network with Harris Hawks Optimization (HHO) classifier. Automatic feature extraction is implemented using the recent popular convolutional neural network (CNN) technique that extracts image features as conditions in the reinforcement learning algorithm. With this, through the procedure of a new deep Q-learning algorithm, Deep Q-Network (DQN) is generated to train a Q-network. The swarm-based method of HHO utilized the optimization method to produce optimal hyperparameters in the DQN model known as HHO-DQN, a novel technique for classifying ovarian cysts. Extensive experimental evaluations on datasets show that the proposed HHO- DQN approach outperforms existing active learning approaches for ovarian cyst classification. Compared with the ANN, CNN, and AlexNet models, the performance of the proposed model is better in terms of precision, f-measure, recall, accuracy, and IoU. The proposed model has achieved 96% precision, 96.5% f-measure, 96% recall, 97% accuracy, and 0.65 IoU. © 2021, CC BY.

关键词： Image classification

SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction

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arXiv 2022年

作者： Hu, Yuyang Gan, Weijie Ying, Chunwei Wang, Tongyao Eldeniz, Cihat Liu, Jiaming Chen, Yasheng An, Hongyu Kamilov, Ulugbek S. Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO63130 United States Department of Computer Science and Engineering Washington University in St. Louis St. LouisMO63130 United States Mallinckrodt Institute of Radiology Washington University in St. Louis St. LouisMO63110 United States Department of Biomedical Engineering Washington University in St. Louis St. LouisMO63130 United States Department of Neurology Washington University in St. Louis St. LouisMO63130 United States

Purpose: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation. Methods: SPICER consists of two modules to simultaneously reconstructs accurate MR images and estimates high-quality coil sensitivity maps (CSMs). The first module, CSM estimation module, uses a convolutional neural network (CNN) to estimate CSMs from the raw measurements. The second module, DMBA-based MRI reconstruction module, forms reconstructed images from the input measurements and the estimated CSMs using both the physical measurement model and learned CNN prior. With the benefit of our self-supervised learning strategy, SPICER can be efficiently trained without any fully-sampled reference data. Results: We validate SPICER on both open-access datasets and experimentally collected data, showing that it can achieve state-of-the-art performance in highly accelerated data acquisition settings (up to 10×). Our results also highlight the importance of different modules of SPICER—including the DMBA, the CSM estimation, and the SPICER training loss—on the final performance of the method. Moreover, SPICER can estimate better CSMs than pre-estimation methods especially when the ACS data is limited. Conclusion: Despite being trained on noisy undersampled data, SPICER can reconstruct high-quality images and CSMs in highly undersampled settings, which outperforms other self-supervised learning methods and matches the performance of the well-known E2E-VarNet trained on fully-sampled groundtruth data. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

Autonomous Reinforcement Control of Underwater Vehicles based on Monocular Depth Vision ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9201-9206页

作者： Pengli Zhu Shuhan Yao Yancheng Liu Siyuan Liu Xiaoling Liang Marine Engineering College Dalian Maritime University Dalian China Department of Radiology and Biomedical Research Imaging Center(BRIC) University of North Carolina at Chapel Hill Chapel HillUSA Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

In this paper, a monocular depth prediction based end-to-end reinforcement control framework is proposed for autonomous control of underwater vehicles in the unknown environment. In the control framework, with the input of camera sensor RGB videos, a monocular depth prediction network is proposed to generate underwater depth images and a sequential reinforcement learning controller is also developed for autonomous obstacle-avoiding navigation and movement control. Simulated and experimental results demonstrate that the proposed control scheme can achieve remarkable performance on collision-avoidance navigation and autonomous control in the unknown environment.

关键词： Monocular depth prediction Autonomous reinforcement control Underwater vehicles

electrical Stimulation in Combating Pressure Ulcer for Immobilize Subjects: A Conceptual Framework

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Electrical Stimulation in Combating Pressure Ulcer for Immob...

2019 International Conference on biomedical engineering, ICoBE 2019

作者： Azman, Amelia Wong Fajingbesi, Fawwaz Eniola Ahmad, Zuraida Mustafah, Yasir Mohd Olanrewaju, Rashidah Funke Hamzaid, Nur Azah Department of Electrical and Computer Engineering Malaysia Department of Manufacturing and Materials Engineering Malaysia Department of Mechatronics Engineering International Islamic University Malaysia Malaysia Department of Biomedical Engineering Faculty of Engineering University Malaya Malaysia Department of Physics Electronics and Earth Science Fountain University Osogbo Nigeria

A paradigm shift in health system did intensify the need for research on wearable electronics. There are numerous reasons that could lead to skin ulcers. The most common are lesion due to constant pressure, incontinence control, excessive humidity and postural changes. Over the years, there have been numerous interventions ranging from specific skin treatment, introducing prevention programs as well as to mechanical intervention. On the rise in such clinical rehabilitation program is the use of electrical stimulation (ES) at a therapeutic level, wound care and muscle function restoration. This paper presents a concept development and implementation process of a new pressure ulcer intervention system which can be used for both chairbound and bedridden patients. The concept here is to integrate the use of electrical stimulation, interface pressure mapping and redistribution with antimicrobial protection not only to act as therapeutic but at the same time as a means of prevention of pressure ulcers by improving muscle tone and decreasing atrophy among immobilized subjects. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Diseases

LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation

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arXiv 2022年

作者： Chaudhary, Muhammad F.A. Gerard, Sarah E. Christensen, Gary E. Cooper, Christopher B. Schroeder, Joyce D. Hoffman, Eric A. Reinhardt, Joseph M. The Roy J. Carver Department of Biomedical Engineering The University of Iowa Iowa CityIA52242 United States The Department of Electrical and Computer Engineering The University of Iowa Iowa CityIA52242 United States The Department of Physiology David Geffen School of Medicine University of California Los AngelesCA90095 United States The Department of Radiology and Imaging Sciences The University of Utah Salt Lake CityUT84132 United States The Department of Radiology The University of Iowa Iowa CityIA52242 United States

Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale;leading to a missed opportunity to evaluate underlying small airways disease. Here, we propose LungViT – a generative adversarial learning approach using hierarchical vision transformers for translating inspiratory CT intensities to corresponding expiratory CT intensities. LungViT addresses several limitations of the traditional generative models including slicewise discontinuities, limited size of generated volumes, and their inability to model texture transfer at volumetric level. We propose a shifted-window hierarchical vision transformer architecture with squeeze-and-excitation decoder blocks for modeling dependencies between features. We also propose a multiview texture similarity distance metric for texture and style transfer in 3D. To incorporate global information into the training process and refine the output of our model, we use ensemble cascading. LungViT is able to generate large 3D volumes of size 320 × 320 × 320. We train and validate our model using a diverse cohort of 1500 subjects with varying disease severity. To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects. Clinical validation on internal and external testing sets shows that synthetic volumes could be reliably adopted for deriving clinical endpoints of chronic obstructive pulmonary disease. © 2022, CC BY-NC-ND.

关键词： Textures

MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images

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arXiv 2023年

作者： Jiang, Hongxu Imran, Muhammad Muralidharan, Preethika Patel, Anjali Pensa, Jake Liang, Muxuan Benidir, Tarik Grajo, Joseph R. Joseph, Jason P. Terry, Russell DiBianco, John Michael Su, Li-Ming Zhou, Yuyin Brisbane, Wayne G. Shao, Wei Department of Electrical and Computer Engineering University of Florida GainesvilleFL32608 United States Department of Medicine University of Florida GainesvilleFL32608 United States Department of Health Outcomes and Biomedical Informatics University of Florida GainesvilleFL32608 United States College of Medicine University of Florida GainesvilleFL32608 United States Department of Bioengineering University of California Los AngelesCA90095 United States Department of Biostatistics University of Florida GainesvilleFL32608 United States Department of Urology University of Florida GainesvilleFL32608 United States Department of Radiology University of Florida GainesvilleFL32608 United States Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Urology University of California Los AngelesCA90095 United States

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. This paper presents MicroSegNet, a multi-scale annotation-guided transformer UNet model designed specifically to tackle these challenges. During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations. We achieve this by proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns a larger weight to prediction errors in hard regions and a lower weight to prediction errors in easy regions. The AG-BCE loss was seamlessly integrated into the training process through the utilization of multi-scale deep supervision, enabling MicroSegNet to capture global contextual dependencies and local information at various scales. We trained our model using micro-US images from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model achieved a Dice coefficient of 0.939 and a Hausdorff distance of 2.02 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. Our code is publicly available at https://***/mirthAI/MicroSegNet and our dataset is publicly available at https://***/records/10475293. © 2023, CC BY-NC-SA.

关键词： Image segmentation