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Fluorinated Graphene-Coated N95 Respirators for Enhanced Protection against Viral Accumulation and Transmission

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Advanced engineering Materials 2025年

作者： He, Fengjie Zhang, Jiongyu Zhai, Shengjie Liu, Changchun Zhao, Hui Department of Mechanical Engineering University of Nevada Las Vegas 4505 S Maryland Parkway Las Vegas89154 United States Department of Biomedical Engineering University of Connecticut Health Center 263 Farmington Avenue FarmingtonCT06030 United States Department of Electrical and Computer Engineering University of Nevada Las Vegas 4505 S Maryland Parkway Las Vegas89154 United States

Highly contagious respiratory infection diseases such as COVID-19 can be transmitted by inhaling virus laden liquid droplets and short-range aerosols, released by an infected person. Particularly, in hospitals, spraying of the respiratory droplets containing pathogens from the conjunctiva or mucus of a susceptible person plays a key role in transferring the infectious diseases. N95 filtering respirators are a critical personal protective equipment. However, due to concerns over the virus accumulation on the N95 respirators, there is an urgent need to make the N95 respirators less contaminated. To address this critical issue, a one-step spray coating approach is developed to coat the fluorinated graphene (FG) nanosheet onto the N95 respirators. The synergistic effect of the FG nanosheet with a low surface energy and the increased surface roughness by FG on the respirator's surface makes it superhydrophobic. For respiratory droplets like saliva and mucus, the FG-coated respirators also show excellent superhydrophobicity. Furthermore, the ability against virus accumulation on the FG-coated respirators is tested by using the mucus droplets laden with SARS-CoV-2. The results show that FG-coated respirators largely increase the virus repel efficiency even under multiple contacts and effectively reduce the virus accumulation. © 2025 The Author(s). Advanced engineering Materials published by Wiley-VCH GmbH.

关键词： Sprayed coatings

Time variation of high-risk groups for liver function deteriorations within fluctuating long-term liver function after hepatic radiotherapy in patients with hepatocellular carcinoma

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European Journal of Medical Research 2024年第1期29卷 104-104页

作者： Tsai, Yu-Lun Yu, Pei-Chieh Nien, Hsin-Hua Lu, Tzu-Pin Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Department of Radiation Oncology Cathay General Hospital Taipei Taiwan School of Medicine China Medical University Taichung Taiwan School of Medicine Fu Jen Catholic University New Taipei City Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Institute of Health Data Analytics and Statistics College of Public Health National Taiwan University Taipei Taiwan

PURPOSE: The purpose of this study is to find essential risk factors associated with liver function (LF) deteriorations within fluctuating long-term LF and their time-varying effects in patients with hepatocellular carcinoma (HCC) receiving hepatic radiotherapy and to identify high-risk groups for adverse LF deteriorations and their changes over time in facilitating the prevention of hepatic decompensation and the improvement of survival. MATERIALS AND METHODS: A total of 133 HCC patients treated by hepatic radiotherapy were enrolled. A study design was conducted to convert posttreatment long-term LF with fluctuating levels over time to recurrent LF events using defined upgrades in a grading scale. The hazard ratios (HR) of pretreatment biochemical, demographic, clinical, and dosimetric factors in developing posttreatment LF events were estimated using the Cox model. Methodologies of the counting process approach, robust variance estimation, goodness-of-fit testing based on the Schoenfeld residuals, and time-dependent covariates in survival analysis were employed to handle the correlation within subjects and evaluate the time-varying effects during long-term follow-up. RESULTS: Baseline LF score before radiotherapy and gender were significant factors. Initial HR in developing LF events was 1.17 (95% CI 1.11-1.23;P < 0.001) for each increase of baseline LF score and kept almost constant over time (HR, 1.00;95% CI 1.00-1.01;P = 0.065). However, no difference was observed regarding initial hazards for gender (HR, 1.00;95% CI 0.64-1.56;P = 0.994), but the hazard for women got higher monthly over time compared with men (HR, 1.04;95% CI 1.01-1.07;P = 0.006). CONCLUSIONS: High-risk groups for adverse LF deteriorations after hepatic radiotherapy may change over time. Patients with poor baseline LF are vulnerable from the beginning. Women require prevention strategies and careful monitoring for deteriorations at a later stage. © 2024. The Author(s).

关键词： Hepatic radiotherapy Hepatocellular carcinoma High-risk group Long-term liver function Time variation

Set-based state estimation and fault diagnosis of linear discrete-time descriptor systems using constrained zonotopes

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arXiv 2020年

作者： Rego, Brenner S. Raimondo, Davide M. Raffo, Guilherme V. Graduate Program in Electrical Engineering Federal University of Minas Gerais Belo Horizonte Brazil Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Electronics Engineering Federal University of Minas Gerais Belo Horizonte Brazil

This paper presents new methods for set-valued state estimation and active fault diagnosis of linear descriptor systems. The algorithms are based on constrained zonotopes, a generalization of zonotopes capable of describing strongly asymmetric convex sets, while retaining the computational advantages of zonotopes. Additionally, unlike other set representations like intervals, zonotopes, ellipsoids, paralletopes, among others, linear static constraints on the state variables, typical of descriptor systems, can be directly incorporated in the mathematical description of constrained zonotopes. Therefore, the proposed methods lead to more accurate results in state estimation in comparison to existing methods based on the previous sets without requiring rank assumptions on the structure of the descriptor system and with a fair trade-off between accuracy and efficiency. These advantages are highlighted in two numerical examples. Copyright © 2020, The Authors. All rights reserved.

关键词： Failure analysis

Predicting extreme events from data using deep machine learning: When and where

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Physical Review Research 2022年第2期4卷 023028-023028页

作者： Junjie Jiang Zi-Gang Huang Celso Grebogi Ying-Cheng Lai The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Research Center for Brain-inspired Intelligence Xi'an Jiaotong University The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Xi'an Shaanxi 710049 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Institute for Complex Systems and Mathematical Biology School of Natural and Computing Sciences King's College University of Aberdeen Aberdeen AB24 3UE United Kingdom Department of Physics Arizona State University Tempe Arizona 85287 USA

We develop a framework based on the deep convolutional neural network (DCNN) for model-free prediction of the occurrence of extreme events both in time (“when”) and in space (“where”) in nonlinear physical systems of spatial dimension two. The measurements or data are a set of two-dimensional snapshots or images. For a desired time horizon of prediction, a proper labeling scheme can be designated to enable successful training of the DCNN and subsequent prediction of extreme events in time. Given that an extreme event has been predicted to occur within the time horizon, a space-based labeling scheme can be applied to predict, within certain resolution, the location at which the event will occur. We use synthetic data from the two-dimensional complex Ginzburg-Landau equation and empirical wind speed data from the North Atlantic Ocean to demonstrate and validate our machine-learning-based prediction framework. The trade-offs among the prediction horizon, spatial resolution, and accuracy are illustrated, and the detrimental effect of spatially biased occurrence of extreme events on prediction accuracy is discussed. The deep learning framework is viable for predicting extreme events in the real world.

关键词： Machine learning Spatiotemporal chaos Artificial neural networks Extreme event statistics

Ptychographic lens-less polarization microscopy

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arXiv 2022年

作者： Kim, Jeongsoo Song, Seungri Kim, Bora Park, Mirae Oh, Seung Jae Kim, Daesuk Cense, Barry Huh, Yong-Min Lee, Joo Yong Joo, Chulmin Department of Mechanical Engineering Yonsei University Seoul03722 Korea Republic of Department of Ophthalmology Asan Medical Center University of Ulsan College of Medicine Seoul05505 Korea Republic of Department of Radiology College of Medicine Yonsei University Seoul03722 Korea Republic of YUHS-KRIBB Medical Convergence Research Institute Seoul03722 Korea Republic of Department of Mechanical System Engineering Jeonbuk National University Jeonju54896 Korea Republic of Optical and Biomedical Engineering Laboratory Department of Electrical Electronic & Computer Engineering The University of Western Australia PerthWA6009 Australia Department of Biochemistry & Molecular Biology College of Medicine Yonsei University Seoul03722 Korea Republic of

Birefringence, an inherent characteristic of optically anisotropic materials, is widely utilized in various imaging applications ranging from material characterizations to clinical diagnosis. Polarized light microscopy enables high-resolution, high-contrast imaging of optically anisotropic specimens, but it is associated with mechanical rotations of polarizer/analyzer and relatively complex optical designs. Here, we present a novel form of polarization-sensitive microscopy capable of birefringence imaging of transparent objects without an optical lens and any moving parts. Our method exploits an optical mask-modulated polarization image sensor and single-input-state LED illumination design to obtain complex and birefringence images of the object via ptychographic phase retrieval. Using a camera with a pixel resolution of 3.45 µm, the method achieves birefringence imaging with a half-pitch resolution of 2.46 µm over a 59.74 mm2 field-of-view, which corresponds to a space-bandwidth product of 9.9 megapixels. We demonstrate the high-resolution, large-area birefringence imaging capability of our method by presenting the birefringence images of various anisotropic objects, including a birefringent resolution target, liquid crystal polymer depolarizer, monosodium urate crystal, and excised mouse eye and heart tissues. © 2022, CC BY.

关键词： Birefringence

Multi-Niche Human Bone Marrow On-a-Chip for Studying the Interactions of Adoptive Car-T Cell Therapies with Multiple Myeloma

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SSRN

SSRN 2024年

作者： Ghoshal, Delta Petersen, Ingrid Ringquist, Rachel Kramer, Liana Bhatia, Eshant Hu, Thomas Richard, Ariane Park, Reda Corbin, Jenna Agarwal, Savi Thomas, Abel Ramirez, Sebastian Tharayil, Jacob Downey, Emma Ketchum, Frank Ochal, Abigail Sonthi, Neha Lonial, Sagar Kochenderfer, James N. Tran, Reginald Zhu, Mandy Lam, Wilbur A. Coskun, Ahmet F. Roy, Krishnendu The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States The Parker H. Petit Institute for Bioengineering & Bioscience Georgia Institute of Technology AtlantaGA30332 United States School of Chemical and Biomolecular Engineering Georgia Institute of Technology AtlantaGA30332 United States The George W. Woodruff Department of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Biochemistry Georgia Institute of Technology AtlantaGA30332 United States Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN46556 United States School of Biological Sciences Georgia Institute of Technology AtlantaGA30332 United States Department of Hematology and Medical Oncology Emory University School of Medicine AtlantaGA30322 United States Surgery Branch National Cancer Institute Bethesda MD20892 United States Division of Pediatric Hematology/Oncology Department of Pediatrics Aflac Cancer Center Blood Disorders Service of Children’s Healthcare of Atlanta Emory University School of Medicine AtlantaGA30322 United States School of Engineering Vanderbilt University NashvilleTN37235 United States

Multiple myeloma (MM), a cancer of bone marrow plasma cells, is the second-most common hematological malignancy. However, despite immunotherapies like chimeric antigen receptor (CAR)-T cells, relapse is nearly universal. The bone marrow (BM) microenvironment influences how MM cells survive, proliferate, and resist treatment. Yet, it is unclear which BM niches give rise to MM pathophysiology. Here, we present a 3D microvascularized culture system, which models the endosteal and perivascular bone marrow niches, allowing us to study MM-stroma interactions in the BM niche and model responses to therapeutic CAR-T cells. We demonstrated the prolonged survival of cell line-based and patient-derived multiple myeloma cells within our in vitro system and successfully perfused in donor-matched CAR-T cells. We then measured T cell survival, differentiation, and cytotoxicity against MM cells using a variety of analysis techniques. Our MM-on-a-chip system could elucidate the role of the BM microenvironment in MM survival and therapeutic evasion and inform the rational design of next-generation therapeutics. © 2024, The Authors. All rights reserved.

关键词： T-cells

Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension

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arXiv 2023年

作者： Zhang, Yuhong Li, Qin Nahata, Sujal Jamal, Tasnia Cheng, Shih-Kuen Cauwenberghs, Gert Jung, Tzyy-Ping The School of Engineering Brown University ProvidenceRI02912 United States Institute for Neural Computation University of California San Diego La Jolla CA92093 United States Department of Radiology and Biomedical Imaging Yale University New HavenCT06520 United States The Department of Bioengineering University of California Los Angeles Los AngelesCA90095 United States The Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States The Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States The Institute of Cognitive Neuroscience National Central University Taoyuan32001 Taiwan The Department of Bioengineering Institute for Neural Computation University of California San Diego La Jolla CA92093 United States The Institute for Neural Computation University of California San Diego La Jolla CA92093 United States

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals’ neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use a feature engineering approach to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60% across 12 subjects. Words of high relevance to the inference keyword had significantly more eye fixations per word: 1.0584 compared to 0.6576 when excluding no-fixation words, and 1.5126 compared to 1.4026 when including them. This study represents the first attempt to classify brain states at a word level using LLM knowledge. It provides valuable insights into human cognitive abilities and the realm of Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

A novel near-infrared light responsive 4D printed nanoarchitecture with dynamically and remotely controllable transformation

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Nano Research 2019年第6期12卷 1381-1388页

作者： Haitao Cui Shida Miao Timothy Esworthy Se-jun Lee Xuan Zhou Sung Yun Hann Thomas J.Webster Brent T.Harris Lijie Grace Zhang Department of Mechanical and Aerospace Engineering The George Washington UniversityWashingtonDC 20052USA Department of Chemical Engineering Northeastern UniversityBostonMA 02115USA Department of Neurology and Pathology Georgetown UniversityWashingtonDC 20007USA Departments of Electrical and Computer Engineering The George Washington UniversityWashingtonDC 20052USA Department of Biomedical Engineering The George Washington UniversityWashingtonDC 20052USA Department of Medicine The George Washington UniversityWashingtonDC 20052USA

Four-dimensional (4D) printing is an emerging and highly innovative additive manufacturing process by which to fabricate pre-designed,self-assembly structures with the ability to transform over ***,one of the critical challenges of 4D printing is the lack of advanced 4D printing systems that not only meet all the essential requirements of shape change but also possess smart,dynamic capabilities to spatiotemporally and instantly control the shape-transformation ***,we present a facile 4D printing platform which incorporates nanomaterials into the conventional stimuli-responsive polymer,allowing the 4D printed object to achieve a dynamic and remote controlled,on-time and position shape transformation.A proof-of-concept 4D printed brain model was created using near-infrared light (NIR) responsive nanocomposite to evaluate the capacity for controllable 4D transformation,and the feasibility of photothermal stimulation for modulating neural stem cell *** novel 4D printing strategy can not only be used to create dynamic 3D patterned biological structures that can spatiotemporally control their shapes or behaviors of transformation under a human benign stimulus (NIR),but can also provide a potential method for building complex self-morphing objects for widespread applications.

关键词： near-infrared light responsive four-dimensional (4D) printing dynamically and remotely controllable graphene brain neural stem cell

Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?

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arXiv 2025年

作者： Hou, Qingshan Zhou, Yukun Goh, Jocelyn Hui Lin Zou, Ke Yew, Samantha Min Er Srinivasan, Sahana Wang, Meng Lo, Thaddaeus Lei, Xiaofeng Wagner, Siegfried K. Chia, Mark A. Yang, Dawei Jiang, Hongyang Ran, AnRan Santos, Rui Somfai, Gabor Mark Zhou, Juan Helen Chen, Haoyu Chen, Qingyu Cheung, Carol Yim-Lui Keane, Pearse A. Tham, Yih Chung Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore School of Computer Science and Engineering Northeastern University Shenyang China Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore Centre for Medical Image Computing University College London London United Kingdom NIHR Biomedical Research Centre Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Singapore Institute of Ophthalmology University College London London United Kingdom Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong Hong Kong Department of Ophthalmology Stadtspital Zürich Zurich Switzerland Spross Research Institute Zurich Switzerland Department of Ophthalmology Semmelweis University Budapest Hungary Centre for Sleep and Cognition Centre for Translational MR Research Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Joint Shantou International Eye Center Shantou University the Chinese University of Hong Kong Guangdong Shantou China Shantou University Medical College Guangdong Shantou China Department of Biomedical Informatics and Data Science Yale School of Medicine Yale University New Haven United States Duke-NUS Medical School Singapore

The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM pre-trained on 142 million natural images, has shown promise in non-medical domains. However, its applicability to clinical tasks remains underexplored. To address this, we conducted head-to-head evaluations by finetuning RETFound and three DINOv2 models (large, base, small) for ocular disease detection and systemic disease prediction tasks, across eight standardized open-source ocular datasets, as well as the Moorfields AlzEye and the UK Biobank datasets. DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0.850–0.952 vs. 0.823-0.944, across three datasets, all P≤0.007) and multi-class eye diseases (AUROC=0.892 vs. 0.846, P Copyright © 2025, The Authors. All rights reserved.

关键词： Ophthalmology