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Recent advances in quantum dots for biomedical applications

Journal of Pharmaceutical Investigation 2018年第2期48卷 209-214页

作者： Lee, Jae Seo Youn, Yun Hee Kwon, Il Keun Ko, Na Re Department of Dentistry Graduate School Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul 02447 South Korea Department of Dental Materials School of Dentistry Kyung Hee University 26 Kyungheedae-ro Dongdaemun-gu Seoul 02447 South Korea Interdisciplinary Program for Bioengineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul 08826 South Korea

Quantum dots (QDs) have been widely used for fluorescent nanoprobes as bioimaging agents. However, QD can have toxic side-effects as they include heavy metals. Many researchers suggest graphene quantum dots (GQDs) as an alternative material with low toxicity and good biocompatibility. GQDs have many attractive features due to their structure. These include outstanding mechanical properties, optical properties, and good physicochemical stability. These exceptional properties can be exploited for biomedical applications, especially in the fields of drug-delivery, bioimaging, and tissue-engineering. This article presents a comprehensive review of GQDs. The primary focus is on their unique properties as they relate to the biomedical field and recently reported applications. © 2018, The Korean Society of Pharmaceutical Sciences and Technology.

关键词： Bio-sensing agents Biomedical application Drug delivery system Graphene quantum dots Quantum dots Tissue engineering

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Physics-based learned design: Optimized coded-illumination for quantitative phase imaging

arXiv

引用

arXiv 2018年

作者： Kellman, Michael R. Bostan, Emrah Repina, Nicole Waller, Laura Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Graduate Program in Bioengineering University of California BerkeleyCA94720 United States

Coded-illumination can enable quantitative phase microscopy of transparent samples with minimal hardware requirements. Intensity images are captured with different source patterns and a non-linear phase retrieval optimization reconstructs the image. The non-linear nature of the processing makes optimizing the illumination pattern designs complicated. Traditional techniques for experimental design (e.g. condition number optimization, spectral analysis) consider only linear measurement formation models and linear reconstructions. Deep neural networks (DNNs) can efficiently represent the non-linear process and can be optimized over via training in an end-to-end framework. However, DNNs typically require a large amount of training examples and parameters to properly learn the phase retrieval process, without making use of the known physical models. Here, we aim to use both our knowledge of the physics and the power of machine learning together. We develop a new data-driven approach to optimizing coded-illumination patterns for a LED array microscope for a given phase reconstruction algorithm. Our method incorporates both the physics of the measurement scheme and the non-linearity of the reconstruction algorithm into the design problem. This enables efficient parameterization, which allows us to use only a small number of training examples to learn designs that generalize well in the experimental setting without retraining. We show experimental results for both a well-characterized phase target and mouse fibroblast cells using coded-illumination patterns optimized for a sparsity-based phase reconstruction algorithm. Our learned design results using 2 measurements demonstrate similar accuracy to Fourier Ptychography with 69 measurements. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

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Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation (vol 21, pg 432, 2018)

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NATURE NEUROSCIENCE 2018年第7期21卷 1015-1015页

作者： Preissl, Sebastian Fang, Rongxin Huang, Hui Zhao, Yuan Raviram, Ramya Gorkin, David U. Zhang, Yanxiao Sos, Brandon C. Afzal, Veena Dickel, Diane E. Kuan, Samantha Visel, Axel Pennacchio, Len A. Zhang, Kun Ren, Bing Ludwig Institute for Cancer Research La Jolla CA USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA US Department of Energy Joint Genome Institute Walnut Creek CA USA School of Natural Sciences University of California Merced Merced CA USA Comparative Biochemistry Program University of California Berkeley Berkeley CA USA Institute of Genomic Medicine Moores Cancer Center University of California San Diego School of Medicine La Jolla CA USA

Analysis of chromatin accessibility can reveal transcriptional regulatory sequences, but heterogeneity of primary tissues poses a significant challenge in mapping the precise chromatin landscape in specific cell types. Here we report single-nucleus ATAC-seq, a combinatorial barcoding-assisted single-cell assay for transposase-accessible chromatin that is optimized for use on flash-frozen primary tissue samples. We apply this technique to the mouse forebrain through eight developmental stages. Through analysis of more than 15,000 nuclei, we identify 20 distinct cell populations corresponding to major neuronal and non-neuronal cell types. We further define cell-type-specific transcriptional regulatory sequences, infer potential master transcriptional regulators and delineate developmental changes in forebrain cellular composition. Our results provide insight into the molecular and cellular dynamics that underlie forebrain development in the mouse and establish technical and analytical frameworks that are broadly applicable to other heterogeneous tissues.

关键词： Cell type diversity Chromatin Chromatin analysis Epigenomics

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Nanostethoscopy: Atomic force microscopy probe contact force versus measured amplitude of cardiomyocytic contractions

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Journal of Bionanoscience 2017年第4期11卷 319-322页

作者： Vyas, Varun Nagarajan, Neerajha Zorlutuna, Pinar Huey, Bryan D. Institute of Materials Science University of Connecticut StorrsCT06269 United States Department of Aerospace and Mechanical Engineering Bioengineering Graduate Program University of Notre Dame Notre DameIN46556 United States

Cardiomyocytes are one of the most active cells in the human body. Understanding their mechanical contractions via nanostethoscopy has given us better insight into the subtleties of contractions. There is extensive data available on the temporal behavior of contraction in a cardiomyocytes but little is known about how contact force of the AFM probe influences the measurement force of contractions i.e., mean beat force. This investigation suggests that in order to accurately measure mean beat force nanostethoscopy data should be analyzed from the lowest to the highest contact force. In this study optimal contact force was varied from 10 nN to 500 nN and it was observed that the maximum contraction i.e., mean beat force was highest at contact force of 250 nN. © 2017 American Scientific Publishers.

关键词： Atomic force microscopy

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Self-modulating therapeutic platform using engineered miRNA-responsive oligonucleotides

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Nano convergence 2025年第1期12卷 32页

作者： Doyeong Ku Hansol Kim JinA Lim Jayeon Song Junhyeok Yoon Liu Jun Su-Ji Min Ryeongeun Cho Namseok Lee Kyunghoon Hur Jong-Eun Park Luke P Lee Junshik Hong Yoosik Kim Hyun Gyu Park Department of Chemical and Biomolecular Engineering (BK 21+ Program) Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea. Department of Internal Medicine Seoul National University College of Medicine Seoul 03080 Republic of Korea. Cancer Research Institute Seoul National University College of Medicine Seoul 03080 Republic of Korea. Graduate School of Medical Science and Engineering KAIST Daejeon 34141 Republic of Korea. Department of Medicine Harvard Medical School Brigham Women's Hospital Boston MA USA. Department of Bioengineering Department of Electrical Engineering and Computer Science University of California at Berkeley Berkeley CA USA. Institute of Quantum Biophysics Department of Biophysics Sungkyunkwan University Suwon Republic of Korea. Biomedical Research Institute Seoul National University College of Medicine Seoul 03080 Republic of Korea. Department of Chemical and Biomolecular Engineering (BK 21+ Program) Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea. ysyoosik@kaist.ac.kr. KAIST Institute for BioCentury KAIST Daejeon 34141 Republic of Korea. ysyoosik@kaist.ac.kr. KAIST Institute for Health Science and Technology (KIHST) KAIST Daejeon 34141 Republic of Korea. ysyoosik@kaist.ac.kr. Department of Chemical and Biomolecular Engineering (BK 21+ Program) Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea. hgpark@kaist.ac.kr. KAIST Institute for BioCentury KAIST Daejeon 34141 Republic of Korea. hgpark@kaist.ac.kr.

Due to a pivotal role in the post-transcriptional regulation of genes implicated in numerous diseases, miRNAs serve as promising disease biomarkers and therapeutic targets. We introduce a new oligonucleotide probe termed miRNA-trigger, which selectively downregulates newly assigned target mRNAs by hijacking specific miRNAs. By engineering the miRNA-trigger to suppress the anti-apoptotic BCL-xL gene, we induce apoptosis selectively in breast cancer cells overexpressing specific miRNAs and further validate its therapeutic efficacy in vivo, by significantly reducing the tumor volume of the xenograft mouse upon its tail-vein injection. This approach establishes a new platform for self-modulating oligonucleotide therapy by redirecting disease-associated miRNAs.

关键词： Apoptosis induction BCL-xL gene Gene regulation Oligonucleotide therapy miRNAs

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Towards decoding the coupled decision-making of metabolism and epithelial-mesenchymal transition in cancer

arXiv

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arXiv 2020年

作者： Jia, Dongya Park, Jun Hyoung Kaur, Harsimran Jung, Kwang Hwa Yang, Sukjin Tripathi, Shubham Galbraith, Madeline Deng, Youyuan Jolly, Mohit Kumar Kaipparettu, Benny Abraham Onuchic, José N. Levine, Herbert Center for Theoretical Biological Physics Rice University HoustonTX77005 United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX77030 United States Centre for BioSystems Science and Engineering Indian Institute of Science BangaloreKarnataka560012 India PhD Program in Systems Synthetic and Physical Biology Rice University HoustonTX77005 United States Department of Physics Northeastern University BostonMA02115 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Rice University HoustonTX77005 United States Dan L. Duncan Comprehensive Cancer Center Baylor College of Medicine HoustonTX77030 United States Department of Chemistry Rice University HoustonTX77005 United States Department of Biosciences Rice University HoustonTX77005 United States Department of Bioengineering Northeastern University BostonMA02115 United States

Cancer cells have the plasticity to adjust their metabolic phenotypes for survival and metastasis. During metastasis, a developmental program known as the epithelial-mesenchymal transition (EMT) plays a critical role. There is extensive cross-talk between metabolism and EMT, but how this leads to coordinated physiological changes is still uncertain. The elusive connection between metabolism and EMT compromises the efficacy of metabolic therapies targeting metastasis. In this review, we aim for clarifying causation between metabolism and EMT based on recent experimental studies and propose integrated theoretical-experimental efforts to better understand the coupled decision-making of metabolism and EMT. © 2020, CC-BY-NC-ND.

关键词： Metabolism

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Development of a pressure sensor system for unobtrusive monitoring of abdominal muscle activities

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Journal of Medical Engineering and Technology 2018年第3期42卷 163-174页

作者： Lee, Joonnyong Park, Jonghyun Lee, Kyu Jin Cho, Minkyung Kim, Keewon Kim, Hee Chan Chung, Sun Gun Interdisciplinary Program of Bioengineering Seoul National University Graduate School Seoul Korea Republic of Creta Co. Ltd. Seoul Korea Republic of Department of Rehabilitation Medicine Seoul National University Hospital Seoul Korea Republic of Institute of Medical and Biological Engineering Medical Research Center Department of Biomedical Engineering Seoul National University College of Medicine Seoul Korea Republic of Institute of Aging Seoul National University Seoul Korea Republic of Rheumatism Research Institute Medical Research Center Seoul National University Seoul Korea Republic of

Surface electromyogram (sEMG) is often used by to objectively measure muscular activity during rehabilitation exercises. sEMG is accurate, but it is unsuitable for uses outside the clinic, and patients can benefit from an unobtrusive device which can be readily used to ubiquitously measure abdominal muscle activation. In this study, we present a pressure sensor system which can be latched onto a belt to measure abdominal muscle activation. sEMG and pressure sensor output were measured in 15 healthy young males during isometric trunk flexion exercise (public trials registration number, KCT0002351), and the results were highly correlated (median R > 0.939). As initial contact force can change the pressure sensor sensitivity, the experiment was performed at two different levels of belt tightness, but the correlations did not significantly improve after tightening the belt, suggesting that the system can be used to ubiquitously and unobtrusively monitor abdominal muscle activity with minimal discomfort. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pressure sensors

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Technology Roadmap for Flexible Sensors

引用

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Machine learning active-nematic hydrodynamics

arXiv

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arXiv 2020年

作者： Colen, Jonathan Han, Ming Zhang, Rui Redford, Steven A. Lemma, Linnea M. Morgan, Link Ruijgrok, Paul V. Adkins, Raymond Bryant, Zev Dogic, Zvonimir Gardel, Margaret L. de Pablo, Juan J. Vitelli, Vincenzo Department of Physics University of Chicago ChicagoIL60637 United States James Franck Institute University of Chicago ChicagoIL60637 United States Pritzer School of Molecular Engineering University of Chicago ChicagoIL60637 United States Graduate Program in Biophysical Sciences University of Chicago ChicagoIL60637 United States Department of Physics Brandeis University WalthamMA02454 United States Department of Physics University of California at Santa Barbara Santa BarbaraCA92111 United States Department of Bioengineering Stanford University StanfordCA94305 United States Department of Structural Biology Stanford University Medical Center StanfordCA94305 United States Center for Molecular Engineering Argonne National Laboratory LemontIL60439 United States

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to derive from microscopics [1]. Seldom is this challenge more apparent than in active matter where the energy cascade mechanisms responsible for autonomous large-scale dynamics are poorly understood [2-6]. Here, we use active nematics [7-11] to demonstrate that neural networks [12, 13] can extract the spatio-temporal variation of hydrodynamic parameters directly from experiments. Our algorithms analyze microtubule-kinesin [7, 14-16] and actin-myosin [10, 17] experiments as computer vision problems. Unlike existing methods, neural networks can determine how multiple parameters such as activity and elastic constants vary with ATP and motor concentration. In addition, we can forecast the evolution of these chaotic many-body systems solely from image-sequences of their past by combining autoencoder and recurrent networks with residual architecture [18]. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical [17, 19] and biological [20-23] systems even when no knowledge of the underlying dynamics exists. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

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Protect transgender scientists

引用

Science (New York, N.Y.) 2025年第6753期388卷 1283-1284页

作者： Nasa Sinnott-Armstrong Desiree Forsythe Jane M Benoit Callie R Chappell Lisa S Y Coe Bruno Francesco Rodrigues de Oliveira Natalya Evans Anna C Fagre Jonathan M Gilligan Maria Hamilton Colin Moran Henneberry Suzanne L Ishaq Juliet Johnston Erin Krichilsky Jamie Alcira Lopez Kay McMonigal Melanie Ortiz Alvarez de la Campa Rahmeen Rahman Nicole E Schwartz Lauren Talluto Eric J Taylor José M Vargas-Muñiz J L Weissman Herbold Computational Biology Program Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Biology Santa Clara University Santa Clara CA USA. Department of Biological Science Florida State University Tallahassee FL USA. Department of Biology Stanford University Stanford CA USA. Department of Biology and Genetics Institute University of Florida Gainesville FL USA. School of Teaching and Learning College of Education University of Florida Gainesville FL USA. Department of Microbiology and Parasitology Biomedical Institute Fluminense Federal University Niterói RJ Brazil. Marine Science Institute University of California Santa Barbara Santa Barbara CA USA. Department of Microbiology Immunology and Pathology Colorado State University Fort Collins CO USA. Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA. Department Civil and Environmental Engineering Vanderbilt University Nashville TN USA. Department of Marine Sciences University of Georgia Athens GA USA. Department of Ecology and Evolution Stony Brook University Stony Brook NY USA. Department of Animal and Veterinary Sciences University of Maine Orono ME USA. Microbes and Social Equity working group Orono ME USA. School of Science and Math Hampshire College Amherst MA USA. American Museum of Natural History Richard Gilder Graduate School New York NY USA. Department of Ecology Evolution and Environmental Biology Columbia University New York NY USA. Department of Bioengineering Stanford University Stanford CA USA. Department of Applied Physics Stanford University Stanford CA USA. College of Fisheries and Ocean Sciences University of Alaska Fairbanks Fairbanks AK USA. Department of Molecular Microbiology and Immunology Brown University Providence RI USA.. Department of Infectious Disease Imperial College London London UK. Depar

Transgender and gender nonconforming (TGnC) people are a primary target of the Trump administration. Multiple executive orders seek to erase TGnC protections; mandate denial of gender identity; and ban diversity, equity, and inclusion (DEI) (1). Changes to federal funding policies related to TGnC individuals and DEI threaten to curtail academic diversity and freedom (2). Institutions have been hesitant to resist, but for many TGnC scientists—especially TGnC individuals of color, immigrants, and those with disabilities—invisibility is not an option. Anticipatory obedience (3, 4), and even passive allyship, is insufficient; institutions must abandon neutrality and defend targeted communities to minimize further harm (5, 6). As queer and TGnC scientists, we call on researchers at all levels—from undergraduates to university presidents—to take direct, consistent, and rapid action.

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