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SSRN 2023年

作者： Lewis, Mackenzie David, Gabriel Jacobs, Danielle Woessner, Alan E. Kuczwara, Patrick Kim, Jin-Woo Quinn, Kyle P. Song, Younghye Department of Biomedical Engineering University of Arkansas FayettevilleAR United States Department of Biological & Agricultural Engineering University of Arkansas FayettevilleAR United States Materials Science & Engineering Program University of Arkansas FayettevilleAR United States

Peripheral nerve injuries persist as a major clinical issue facing the US population and can be caused by stretch, laceration, or crush injuries. Small nerve gaps are simple to treat, and the nerve stumps can be reattached with sutures. In longer nerve gaps, traditional treatment options consist of autografts, hollow nerve guidance conduits, and, more recently, manufactured fibrous scaffolds. These manufactured scaffolds often incorporate stem cells, growth factors, and/or extracellular matrix (ECM) proteins to better mimic the native environment but can have issues with homogenous cell distribution or uniformly oriented neurite outgrowth in scaffolds without fibrous alignment. Here, we utilize a custom device to fabricate collagen I hydrogels with aligned fibers and encapsulated adipose-derived mesenchymal stem cells (ASCs) for potential use as a peripheral nerve repair graft. Analysis of these scaffolds in vitro revealed heightened therapeutic secretome from ASCs, ECM deposition, and dorsal root ganglia (DRG) neurite outgrowth along the axis of fiber alignment. Our platform serves as an in vitro testbed platform to assess neuro-regenerative potential of ASCs in aligned collagen fiber scaffolds and may provide guidance on next-generation nerve repair scaffold design. © 2023, The Authors. All rights reserved.

关键词： Collagen

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Spatiotemporal Properties of Stimulus-evoked Responses in Mice Following Single Whisker Stimulation

Spatiotemporal Properties of Stimulus-evoked Responses in Mi...

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Optics and the Brain, BRAIN 2024 - Part of Optica Biophotonics Congress: biomedical Optics

作者： Schloss, Kaelyn H. Wang, Xiaodan Padawer-Curry, Jonah A. Bice, Annie R. Bauer, Adam Q. Department of Biomedical Engineering Washington University in St. Louis 1 Brookings Drive St. LouisMO United States Imaging Science Program Washington University in St. Louis 1 Brookings Drive St. LouisMO United States Department of Radiology Washington University in St. Louis 1 Brookings Drive St. LouisMO United States

We used novel peripheral stimulation methods to achieve highly precise single whisker targeting to examine spatiotemporal relationships between stimulus-evoked neuronal, metabolic, and hemodynamic activity in awake mi... 详细信息

ISBN: (纸本)9781957171340

关键词： Mammals

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Recent advances in Fenton and Fenton-like reaction mediated nanoparticle in cancer therapy

Biomedical Technology

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biomedical Technology 2023年 3卷 40-51页

作者： Huijie Han Jiachen Li Hélder A. Santos Department of Biomedical Engineering University Medical Center Groningen University of Groningen Groningen 9713 AV the Netherlands W.J. Korf Institute for Biomedical Engineering and Materials Science University Medical Center Groningen University of Groningen Groningen 9713 AV the Netherlands Drug Research Program Division of Pharmaceutical Chemistry and Technology Faculty of Pharmacy FI-00014 Helsinki Finland

Fenton and Fenton like reaction have been well clarified as efficient reactive oxygen species (ROS) sources in tumor, and have been widely developed into a cancer treatment method. Meanwhile, transition metal-based nanomaterials with Fenton or Fenton like reaction characteristics also have been well explored as therapeutic agents for the cancer therapy, mainly in chemo-dynamic and ferroptosis induced cancer therapy. Herein,to summarize recent advances in Fenton and Fenton like reaction mediated nanoparticles for cancer therapy, in this minireview, we first introduced the mechanisms of Fenton and Fenton like reaction and two therapeutic methods based on Fenton and Fenton like reaction, and then we introduced the well-designed nanoparticles with Fenton reaction or Fenton-like reaction characteristics for the cancer therapies. Finally its challenges and perspectives are discussed.

关键词： Fenton reaction Fenton like reaction Cancer therapy Chemodynamic therapy Ferroptosis

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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Synthetic Data-Empowered Supervised Machine Learning Modeling for a Binary Fracture Risk Assessment from 2D-DXA Analysis

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SSRN 2023年

作者： Choi, Heesun Quagliato, Luca Chung, Yoon-Sok Lee, Taeyong Division of Mechanical and Biomedical Engineering Ewha Womans University Seoul03760 Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul03760 Korea Republic of Department of Endocrinology and Metabolism Ajou University School of Medicine Suwon16499 Korea Republic of

Background and Objectives: Bone fracture risk assessment for osteopenia and osteoporotic patients is essential for the adoption of early countermeasures and avoiding discomfort and hospitalization. Currently employed methodologies, such as FRAX®, provides a risk assessment over a five to ten years period without showing the main variables influencing the prediction nor how they can be targeted in the short term. Thus, a lesser black-box approach where the fracture risk can be assessed in real-time from a commonly employed analysis, id est the dual-energy X-ray absorptiometry (2D-DXA), would be of help to clinicians and patients alike. Accordingly, this study presents three real-time machine learning (ML) assessment models, with distinct complexity, architectures, and performances, capable of predicting a binary fracture risk based on a femur head-hip joint 2D-DXA scan. Methods: A ~10,000 adult Korean gathered between 2017 and 2021 and composed of ~90% female and ~10% male ranging from 50 to 99 years of age was considered. 10% of the data is relevant to subjects who experienced skeletal fractures, among which 245 cases are femur fractures. The 2D-DXA analyses of the femur head-hip joint region carried out on patients allowed collecting 23 parameters, including the patient’s age, BMI, and gender, associated with one binary variable, defined in terms of non-fracture (NFX) or fracture (FX), to be employed as the training dataset for the three ML models employed and optimized in this research. The 2D-DXA results’ database (DB) was employed to train three ML classifiers with a binary (NFX/FX) output layer, namely the Extreme Gradient Boosting (XGB), the K-Nearest Neighbor (KNN), and Deep Neural Network (DNN). To avoid overfitting due to the higher number of NFX data with respect to FX one, two features augmentation techniques based on the Synthetic Minority Over-sampling Technique (SMOTE) and Oversample using Adaptive Synthetic (ADASYN) oversamplers were employed. For all

关键词： Fracture

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Electrospun Aligned PCL/Gelatin Scaffolds Mimicking the Skin ECM for Effective Antimicrobial Wound Dressings

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Advanced Fiber Materials 2023年第1期5卷 235-251页

作者： Erfan Rezvani Ghomi Rajamani Lakshminarayanan Vijila Chellappan Navin Kumar Verma Amutha Chinnappan Rasoul Esmaeely Neisiany Kottaiswamy Amuthavalli Zhi Sheng Poh Brandon Han Siang Wong Nileshkumar Dubey Roger Narayan Seeram Ramakrishna Department of Mechanical Engineering Center for Nanotechnology and SustainabilityNational University of SingaporeSingapore 117581Singapore Ocular Infections and Antimicrobials Research Group Singapore Eye Research InstituteThe Academia20 College RoadDiscovery TowerSingapore 169856Singapore Institute of Materials Research and Engineering(IMRE) Agency for Science Technology and Research(A*STAR)#08‑032 Fusionopolis WayInnovisSingapore 138634Singapore Lee Kong Chian School of Medicine Nanyang Technological University SingaporeClinical Sciences Building11 Mandalay RoadSingapore 308232Singapore Department of Pharmacy National University of Singapore18 Science Drive 4Singapore 117543Singapore Academic Clinical Program in Ophthalmology and Visual Sciences Academic Clinical Program Duke-NUS Medical SchoolSingapore 169857Singapore National Skin Centre 1 Mandalay RoadSingapore 308205Singapore Department of Materials and Polymer Engineering Faculty of EngineeringHakim Sabzevari UniversitySabzevar ***Iran Faculty of Dentistry National University of SingaporeSingapore 119085Singapore ORCHIDS:Oral Care Health Innovations and Designs Singapore National University of SingaporeSingaporeSingapore 119085Singapore Department of Biomedical Engineering University of North Carolina at Chapel HillChapel HillNC 27514USA

Bacterial infections and multidrug-resistant bacteria are major health burdens in wound *** antimicrobial agents,e.g.,ε-polylysine(ε-PL),provide a broad spectrum of antibacterial properties and support dermal cell ***,ε-PL was incorporated into polycaprolactone(PCL)/gelatin electrospun scaffolds collected at varying rotation ***,the samples were crosslinked using dopamine hydrochloride to provide highly proliferative dressings with broad antimicrobial *** morphological study showed that the electrospun wound dressings were smooth,continuous,and bead-free,with a mean diameter ranging from 267±7 to 331±8 nm for all random and aligned *** fiber alignment of the electrospun PCL/gelatin scaffolds improved their tensile strength and ***,nanofiber mats are highly hydro-philic,which is crucial for an efficient wound *** samples also demonstrated high antimicrobial properties against common wound bacterial strains,including methicillin-resistant Staphylococcus aureus(MRSA),Staphylococcus aureus(SA),Escherichia coli(EC),Acinetobacter baumannii(AB),and Pseudomonas aeruginosa(PA).Mammalian cell prolifera-tion and morphology assays involving primary human dermal fibroblasts(hDFs)and immortalized keratinocytes(HaCaT)showed excellent biocompatibility of the electrospun mats and remarkably aligned ***,aligned mats showed more cell migration than randomly oriented mats,which is desirable for more efficient wound ***,it can be concluded that aligned PCL/gelatin mats containingε-PL are promising for potential use in wound dressings.

关键词： Electrospinning Wound healing Fiber alignment Biomaterials Nanofiber dressing Bacterial infections

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Spectral learning of shared dynamics between generalized-linear processes 24

Spectral learning of shared dynamics between generalized-lin...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lucine L. Oganesian Omid G. Sani Maryam M. Shanechi Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA

ISBN: (纸本)9798331314385

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.

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Assessment of Simple Adhesive Substrates as a Method for Quantifying Microplastics and the Factors Influencing Capture

SSRN

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SSRN 2022年

作者： Rieland, Julie M. Hu, Zeyuan Deese, Julian S. Love, Brian J. Department of Materials Science and Engineering United States Department of Biomedical Engineering United States Macromolecular Science and Engineering Graduate Program 2300 Hayward St Ann ArborMI48109 United States

Microplastics are a growing environmental concern, with a large body of evidence documenting substantial distribution of plastic material in virtually all environmental compartments. Countermeasures that help to bind, aggregate, or coalesce these distributed particles might result in lower human and animal exposures. Pressure sensitive adhesives (PSAs) have been identified as a viable microplastic (MP) capture mechanism with a range of potential use conditions. As with any countermeasure, there is a need to evaluate their collection efficiency, cost, ease of installation, etc. Expanding on previous work, submerged, spray-coated PSAs were investigated as a quick and low-cost quantitative method to assess MP binding in water. Two molecular weights of poly(2-ethylhexyl acrylate) PSA (92k, 950k, and 50:50 bimodal distribution) were applied as spray-coated substrates to compare MP-adhesive interactions. Thin films of PSA (92k 6 ± 1 μm, 950k 4 ± 2 μm, 50:50 BD 6.5 ± 1 μm) were sprayed on borosilicate glass slides using a commercial air brush. Polydisperse nylon-12 particles varying in size from 15-30 µm in diameter were dispersed in aqueous solution between 0.01 and 5 mg mL -1 and agitated under ambient conditions as quantitative comparisons of adhesive wettability and net capture. Mixed assays were also performed comparing binding of common MP species including polyethylene, polystyrene, and polyester particles and fibers to understand how varying composition, size, and form factor affect adsorption. The glass slide method demonstrated linear trends associated with the MP binding to glass slides with respect both to exposure time and MP concentration. The adhesive wettability demonstrates bounding parameters under which high wettability adhesives excel at MP capture but may compromise the integrity of the adhesive film. © 2022, The Authors. All rights reserved.

关键词： Adhesives

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Acid-labile chemical bonds-based nanoparticles for endosome escape and intracellular delivery

Biomedical Technology

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biomedical Technology 2023年 3卷 52-58页

作者： Ruoyu Cheng Shiqi Wang Hélder A. Santos Department of Biomedical Engineering University Medical Center Groningen University of Groningen 9713 AV Groningen the Netherlands W.J. Kolff Institute for Biomedical Engineering and Materials Science University Medical Center Groningen University of Groningen 9713 AV Groningen the Netherlands Drug Research Program Division of Pharmaceutical Chemistry and Technology Faculty of Pharmacy University of Helsinki FI-00014 Helsinki Finland

In the last years there has been a booming in the development of biological pharmaceuticals, and various nanoparticle delivery systems have been developed to overcome biological barriers to maximize the therapeutic potentials. Especially, when these drug-loaded nanoparticle systems arrive at the target cells, they must be successfully internalized into the cells, followed by the endosome escape, to release the payload in the cytoplasm. This is crucial so that the payloads are not degraded in the endosome (around pH 6.8–6.0) or lysosome (around pH 4.5). Considering the acid environment of the endosome, several studies have shown the design of different acid-labile nanoparticle delivery systems attempting to achieve endosome escape by, for example, endosomal rupture, membrane destabilization, and membrane fusion. In this mini-review, we summarize current designs in acid-labile chemical bonds for endosome escape of nanosystems. First, we briefly introduce the structural design of acid-labile nanoparticles and their endosomal escape mechanisms. Then, we review recent research work on the topic, highlighting how the nanoparticle designs can be used in endosome escape for different biomedical applications. Finally, we discuss the challenges and future perspectives in this field.

关键词： Intracellular delivery pH-responsive Acid-labile Nanoparticle Endosomal escape

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