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arXiv 2024年

作者： Zeiltinger, Julia Roy, Sushmita Ay, Ferhat Mathelier, Anthony Medina-Rivera, Alejandra Mahony, Shaun Sinha, Saurabh Ernst, Jason Stowers Institute for Medical Research Kansas CityMO64112 United States Department of Pathology & Laboratory Medicine The University of Kansas Medical Center Kansas CityKS66160 United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison Madison53715 United States Wisconsin Institute for Discovery University of Wisconsin-Madison Madison53715 United States Centers for Autoimmunity Inflammation and Cancer Immunotherapy La Jolla Institute for Immunology La Jolla CA United States Bioinformatics and Systems Biology Program University of California La Jolla San DiegoCA United States Department of Pediatrics University of California La Jolla San DiegoCA United States Nordic EMBL Partnership University of Oslo Oslo0318 Norway Department of Medical Genetics Institute of Clinical Medicine University of Oslo Oslo University Hospital Oslo Norway Department of Pharmacy University of Oslo Oslo Norway Laboratorio Internacional de Investigación sobre el Genoma Humano Universidad Nacional Autónoma de México Campus Juriquilla Blvd. Juriquilla 3001 Santiago de Querétaro76230 Mexico Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology The Pennsylvania State University University ParkPA16802 United States Walter H. Coulter Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA United States H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States Department of Biological Chemistry University of California Los Angeles Los AngelesCA90095 United States Computer Science Department University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Eli and Edythe Broad Center of Regenerative Medicine Stem Cell Research University of California Los Angeles Los AngelesCA90095 United States

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-regulatory elements are mapped and how their sequence rules can be learned and interpreted with sequence-to-function neural networks, with the goal of identifying genetic variants in human disease. We also discuss how studies of the 3D chromatin organization could help identifying long-range regulatory effects and how current methods for mapping gene regulatory networks could better describe biological processes. We point out current gaps in knowledge along with technical limitations and benchmarking challenges of computational methods. Finally, we discuss newly emerging technologies, such as spatial transcriptomics, and outline strategies for creating a more general model of the cis-regulatory code that is more broadly applicable across cell types and individuals. © 2024, CC BY-NC-SA.

关键词： Molecular docking

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Identifying novel prognostic markers and genotype-phenotype associations in endometrioid endometrial carcinoma by computational analysis of histopathological images

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Medicine in Omics 2021年 1卷

作者： Jun Cheng Yuting Liu Wei Huang Wenhui Hong Lingling Wang Dong Ni National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Medical UltraSound Image Computing (MUSIC) Lab Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen China Department of Radiation Oncology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences School of Medicine South China University of Technology China

Hematoxylin and eosin stained slides are routinely used for the diagnosis and grading of endometrioid endometrial carcinoma (EEC). These images present a high degree of cellular heterogeneity, which may contain clinically relevant information such as prognosis and is difficult to be quantified objectively by eyes. Besides traditional microscopic image assessment, a lot of effort has been put in molecular characterization of tumors. How molecular events manifest at tumor tissue level is not well understood. In this paper, we investigated whether quantitative morphological features extracted from histopathological images are associated with patient survival and somatic mutation of genes in EEC using the multi-modality data from The Cancer Genome Atlas. A computational image analysis pipeline was developed to extract image features that characterize the size, shape, staining, and density of cell nuclei. For prognosis prediction, we built a prognostic model based on the image features. In a validation set, the risk score predicted by our model was an independent prognostic factor for overall survival in a multivariate Cox proportional hazards model (hazard ratio with 95% confidence interval: 3.38 [1.55–7.37], p = 2.15e−3). To link tumor tissue morphology with somatic mutation, a two-sided Mann-Whitney U test was used to compare the distribution of each feature between mutated and nonmutated cases for frequently mutated genes. We found that TP53 and TTN were significantly associated with tissue morphological changes. These findings show the promising potential of computational histopathology image analysis in predicting patient survival and exploring genotype-phenotype associations.

关键词： Endometrial carcinoma Computational pathology Somatic mutation Genotype-phenotype association

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Internal force-controlled (IFC) sonication with tunable conditions for the large-scale preparation of porous silicon nanoparticles

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Journal of Drug Delivery Science and technology 2025年 110卷

作者： Nam Cheol Hwang Seung Woo Baek Dongjae Choi Hwajun Jeong Dokyoung Kim Department of Biomedical Science Graduate School Kyung Hee University Seoul 02447 Republic of Korea College of Medicine Kyung Hee University Seoul 02447 Republic of Korea College of Medicine Yonsei University Seoul 03722 Republic of Korea Department of Anatomy College of Medicine Chung-Ang University Seoul 06974 Republic of Korea Department of Anatomy and Neurobiology College of Medicine Kyung Hee University Seoul 02447 Republic of Korea Department of Precision Medicine Graduate School Kyung Hee University Seoul 02447 Republic of Korea Medical Research Center for Bioreaction to Reactive Oxygen Species and Biomedical Science Institute School of Medicine Core Research Institute (CRI) Kyung Hee University Seoul 02447 Republic of Korea KHU-KIST Department of Converging Science and Technology Kyung Hee University Seoul 02447 Republic of Korea UC San Diego Materials Research Science and Engineering Center La Jolla CA 92093 USA

Porous silicon nanoparticles (pSiNPs) have garnered significant attention for various applications, including drug delivery and biosensing. Conventionally, their production has been restricted to small-scale synthesis, often resulting in nanoparticles of variable sizes, which has limited their commercialization and practical use. In this study, we present a novel method for the scalable production of pSiNPs on a gram scale in a relatively short time using internal force-controlled (IFC) sonication. IFC sonication ensures power adjustment (from 60 W to 300 W), short processing times (<3 h), high yield (63.0 ± 4.18 %), and high reproducibility. This method produces nanoparticles with a superior degree of uniformity and consistent sizes, thereby addressing size variation challenges observed in previous approaches. These advancements facilitate large-scale production and enhance the reproducibility and applicability of pSiNPs in various fields.

关键词： Homogenizer Large-scale fabrication Nanomaterials Porous silicon Ultrasonication

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Exponential Signal Reconstruction with Deep Hankel Matrix Factorization

arXiv

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arXiv 2020年

作者： Huang, Yihui Zhao, Jinkui Wang, Zi Orekhov, Vladislav Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center National Institute for Data Science in Health and Medicine Xiamen University Xiamen China The School of Computer and Information Engineering Xiamen University of Technology Xiamen China The Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg Sweden

Exponential function is a basic form of temporal signals, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data may be acquired but result in severe artifacts in its spectrum, which is the Fourier transform of exponentials. Thus, reliable spectrum reconstruction is highly expected in the fast data acquisition in many applications, such as chemistry, biology, and medical imaging. In this work, we propose a deep learning method whose neural network structure is designed by imitating the iterative process in the model-based state-of-the-art exponentials reconstruction method with low-rank Hankel matrix factorization. With the experiments on synthetic data and realistic biological magnetic resonance signals, we demonstrate that the new method yields much lower reconstruction errors and preserves the low-intensity signals much better than compared methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

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Myosin and \(\upalpha\)-actinin regulation of stress fiber contractility under tensile stress

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Scientific Reports 2023年第1期13卷 1-13页

作者： Haoran Ni Garegin A. Papoian Qin Ni Andreea Trache Yi Jiang Institute for Physical Science and Technology University of Maryland College Park MD USA Department of Chemistry and Biochemistry University of Maryland College Park MD USA Institute for NanoBioTechnology Johns Hopkins University Baltimore MD USA Department of Medical Physiology Texas A &M University Health Science Center Bryan TX USA Department of Biomedical Engineering Texas A &M University College Station TX USA Department of Mathematics and Statistics Georgia State University Atlanta GA USA

Stress fibers are actomyosin bundles that regulate cellular mechanosensation and force transduction. Interacting with the extracellular matrix through focal adhesion complexes, stress fibers are highly dynamic structures regulated by myosin motors and crosslinking proteins. Under external mechanical stimuli such as tensile forces, the stress fiber remodels its architecture to adapt to external cues, displaying properties of viscoelastic materials. How the structural remodeling of stress fibers is related to the generation of contractile force is not well understood. In this work, we simulate mechanochemical dynamics and force generation of stress fibers using the molecular simulation platform MEDYAN. We model stress fiber as two connecting bipolar bundles attached at the ends to focal adhesion complexes. The simulated stress fibers generate contractile force that is regulated by myosin motors and\(\alpha\)-actinin crosslinkers. We find that stress fibers enhance contractility by reducing the distance between actin filaments to increase crosslinker binding, and this structural remodeling ability depends on the crosslinker turnover rate. Under tensile pulling force, the stress fiber shows an instantaneous increase of the contractile forces followed by a slow relaxation into a new steady state. While the new steady state contractility after pulling depends only on the overlap between actin bundles, the short-term contractility enhancement is sensitive to the tensile pulling distance. We further show that this mechanical response is also sensitive to the crosslinker turnover rate. Our results provide new insights into the stress fiber mechanics that have significant implications for understanding cellular adaptation to mechanical signaling.

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FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction

arXiv

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arXiv 2021年

作者： Peng, Liang Wang, Nan Dvornek, Nicha Zhu, Xiaofeng Li, Xiaoxiao The Center for Future Media School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Technology East China Normal University Shanghai200062 China The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Radiology and Biomedical Imaging Yale University New HavenCT06511 United States Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis. Specifically, in medical applications, GCNs can be used for disease prediction on a population graph, where graph nodes represent individuals and edges represent individual similarities. However, GCNs rely on a vast amount of data, which is challenging to collect for a single medical institution. In addition, a critical challenge that most medical institutions continue to face is addressing disease prediction in isolation with incomplete data information. To address these issues, Federated Learning (FL) allows isolated local institutions to collaboratively train a global model without data sharing. In this work, we propose a framework, FedNI, to leverage network inpainting and inter-institutional data via FL. Specifically, we first federatively train missing node and edge predictor using a graph generative adversarial network (GAN) to complete the missing information of local networks. Then we train a global GCN node classifier across institutions using a federated graph learning platform. The novel design enables us to build more accurate machine learning models by leveraging federated learning and also graph learning approaches. We demonstrate that our federated model outperforms local and baseline FL methods with significant margins on two public neuroimaging datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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Correction: Isolation and characterization of apical papilla cells from root end of human third molar and their differentiation into cementoblast cells: an in vitro study

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Biological procedures online 2023年第1期25卷 14页

作者： Morvarid Ebadi Amirfarhang Miresmaeili Sarah Rajabi Shahrokh Shojaei Sareh Farhadi Department of Biomedical Engineering Central Tehran Branch Islamic Azad University Tehran Iran. Orthodontic Department of Hamadan University of Medical Sciences and Hamadan Dental Research Centre Hamadan Iran. Department of Cell Engineering Cell Science Research Center Royan Institute for Stem Cell Biology and Technology ACECR Tehran Iran. srajabi@***. Stem Cells Research Center Tissue Engineering and Regenerative Medicine Institute Central Tehran Branch Islamic Azad University Tehran Iran. Department of Oral & Maxillofacial Pathology Faculty of Dentistry Tehran Medical Sciences Islamic Azad University Tehran Iran.

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Long-term effects of tear film component deposition on the surface and optical properties of two different orthokeratology lenses

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Contact Lens and Anterior Eye 2023年第4期46卷 101852-101852页

作者： Su, Chen-Ying Yeh, Lung-Kun Huang, Pin-Hsuan Lin, Wen-Pin Huang, Hsueh-Fang Lai, Chi-Chun Fang, Hsu-Wei Department of Chemical Engineering and Biotechnology National Taipei University of Technology 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan Department of Ophthalmology Chang Gung Memorial Hospital Linkou No. 5 Fuxing St. Taoyuan 333 Taiwan College of Medicine Chang Gung University No. 259 Wenhua 1st Rd. Taoyuan 333 Taiwan Research and Development Center Brighten Optix Co. 6F-1 No. 150 Sec.4 Chengde Rd. Shilin Dist. Taipei 111 Taiwan Department of Optometry University of Kang Ning No. 137 Alley 75 Sec. 3 Kang Ning Road Neihu District Taipei 11485 Taiwan Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes No. 35 Keyan Road Zhunan Town Miaoli County 35053 Taiwan

Purpose: To understand the effects of long-term deposition of tear film components on the surface and optical properties of orthokeratology (ortho-k) lenses, two different lenses, Brighten 22 and Optimum Extra, were tested here. Methods: Ortho-k lenses were immersed in artificial tears and cleaned with a commercial care solution repeatedly for up to 90 days. Both the daily and accumulated lysozyme deposition amounts using an Enzyme-Linked ImmunoSorbent Assay were then analyzed. The base curve, central thickness, power, and transmission of visible light, ultraviolet A, and ultraviolet B were analyzed before and after repeated tear film component deposition procedures. The surface roughness using atomic force microscopy was observed and an energy dispersive spectrometer was used to analyze the composition of the deposits. Results: The highest levels of lysozyme were adsorbed on both lens materials during the first four days of the procedure and became saturated by day 6. For both lens materials, contamination on the lenses was easily observed by day 30, and the degree of surface roughness was higher. The transmission levels of different light spectrums were reduced showing that the optical characteristics of both lenses were also affected. Conclusions: The results provide in vitro evidence that lysozyme could not be completely removed from orthokeratology lenses. Both surface and optical properties were affected by the deposition of tear film components. However, only one commercial multipurpose care solution was used to clean the lens in this study when the main ingredient was a surfactant, and the results might be different when other care regimens with other key ingredients are used. In addition, whether tear film component deposition might result in increased risks of infection or corneal abrasion will require further investigation. © 2023

关键词： Lysozyme Optical property Orthokeratology lens Surface roughness Tear film components

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Electroacupuncture Stimulation of Language-Implicated Acupoint Tongli(HT 5) in Healthy Subjects: An fMRI Evaluation Study

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Chinese Journal of Integrative Medicine 2018年第11期24卷 822-829页

作者： LI Lin-ling LIU Xiao-wu WU Fang TONG Dong-chang YE Li-ping TAO Hong-xing LIU Peng QIU Yun-hai YANG Wan-zhang Research Center for Neural Engineering Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Department of Rehabilitation Medicine the Sixth People's Hospital in Shenzhen (Nanshan Hospital) Engineering Research Center of Molecular and Neuro Imaging Ministry of Education School of Life Science and Technology Xidian University

Objective： To explore brain activations associated with electroacupuncture simulation at Tongli（HT 5） and its comparison with brain activations during picture-naming task. Methods： Twenty healthy subjects were enrolled in this study. Half of them received electroacupuncture stimulation at HT 5（ACUP group） and the other half of them received stimulation at a nonmeridian sham acupoint（SHAM group）. All subjects performed picture-naming task. Each subject finished two runs of functional magnetic resonance imaging examinations in one session and picture-naming task was performed before electroacupuncture stimulation. Subjective brain activations were obtained using generalized linear model and inter-group analyses were performed after that. Results： The electroacupuncture stimulation at HT 5 induced significant brain activations in both the anterior and posterior language regions, including the left inferior frontal gyrus, which was in consistent with activations induced during picture-naming task. Group analysis showed a tendency of increased activation of ACUP group in left inferior frontal gyrus compared with SHAM group（P〈0.05 FDR corrected）. Conclusions： Electroacupuncture treatment at the acupoint HT 5 has modulation effect on typical language-implicated brain regions in healthy subjects, which provides supporting evidence for beneficial effects of needling at HT 5 for recovery of language function in aphasia.

关键词： electroacupuncture Tongli （HT 5） functional magnetic resonance imaging

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In vitro studies of factors affecting debridement of dental implants by tricalcium phosphate powder abrasive treatment

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Scientific Reports 2023年第1期13卷 1-10页

作者： Chen, I-Cheng Su, Chen-Ying Tu, Jia-Jun Kao, Daniel Wenkai Fang, Hsu-Wei Accelerator for Happiness and Health Industry National Taipei University of Technology No. 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology No. 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan Washington Dental Group No. 111 Sec. 3 Heping E. Rd. Da-an Dist. Taipei City 10675 Taiwan Department of Dentistry National Yang Ming University No. 155 Yixin Rd. Beitou Dist. Taipei City 11221 Taiwan Harvard School of Dental Medicine Boston 188 Longwood Ave Boston 02115 MA United States Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes No. 35 Keyan Road Zhunan Town Miaoli County 35053 Taiwan High-value Biomaterials Research and Commercialization Center National Taipei University of Technology No. 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan

Peri-implantitis is a common complication characterized by inflammation in tissues surrounding dental implants due to plaque accumulation, which can lead to implant failure. While air flow abrasive treatment has been found to be effective for debriding implant surfaces, little is known about the factors that affect its cleaning capacity. This study systematically examined the cleaning capacity of air powder abrasive (APA) treatment with β-tricalcium phosphate (β-TCP) powder, using various powder jetting strengths and different particle sizes. Three sizes of β-TCP powder (S, M, and L) were prepared, and different powder settings (low, medium, and high) were tested. The cleaning capacity was determined by quantifying ink removal, which simulated biofilm removal from the implant surfaces at different time points. The results of the systematic comparisons showed that the most efficient cleaning of implant surfaces was achieved using size M particles with medium setting. Additionally, the amount of powder consumed was found to be critical to cleaning efficiency, and the implant surfaces were altered in all tested groups. These systematically analyzed outcomes may provide insights into the development of potential non-surgical strategies for treating peri-implant diseases. © 2023, The Author(s).

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