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Technology Roadmap for Flexible Sensors

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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Void distributions reveal structural link between jammed packings and protein cores

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Physical Review E 2019年第2期99卷 022416-022416页

作者： John D. Treado Zhe Mei Lynne Regan Corey S. O'Hern Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical & Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Chemistry Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics & Biochemistry Yale University New Haven Connecticut 06520 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction ϕ≈0.56, which is the same as dense, random packing of amino-acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino-acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. We measure the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation. We find that the cluster size statistics are similar for void percolation in packings of amino-acid-shaped particles and randomly placed spheres, but different from that for void percolation in jammed sphere packings. We propose that the connected void regions are a defining structural feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino-acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations.

关键词： Jamming Protein structure Packing & jamming problems Proteins Crystal structures Finite-size scaling Molecular dynamics Percolation theory

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Mitigation of Chromosome Loss in Clinical CRISPR-Cas9-Engineered T Cells

SSRN

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SSRN 2023年

作者： Tsuchida, Connor A. Brandes, Nadav Bueno, Raymund Trinidad, Marena Mazumder, Thomas Yu, Bingfei Hwang, Byungjin Chang, Christopher Liu, Jamin Sun, Yang Hopkins, Caitlin R. Parker, Kevin R. Qi, Yanyan Satpathy, Ansuman T. Stadtmauer, Edward A. Cate, Jamie H.D. Eyquem, Justin Fraietta, Joseph A. June, Carl H. Chang, Howard Y. Ye, Chun Jimmie Doudna, Jennifer A. University of California Berkeley - University of California San Francisco Graduate Program in Bioengineering University of California Berkeley BerkeleyCA United States Innovative Genomics Institute University of California Berkeley BerkeleyCA United States Division of Rheumatology Department of Medicine University of California San Francisco San FranciscoCA United States Center for Personal Dynamic Regulomes Stanford University StanfordCA United States Parker Institute for Cancer Immunotherapy Stanford University School of Medicine StanfordCA United States Biomedical Sciences Graduate Program University of California San Francisco San FranciscoCA United States Medical Scientist Training Program University of California San Francisco San FranciscoCA United States Department of Medicine University of California San Francisco San FranciscoCA United States Parker Institute for Cancer Immunotherapy San FranciscoCA United States Gladstone-UCSF Institute of Genomic Immunology San FranciscoCA United States Department of Biochemistry and Biophysics University of California San Francisco San FranciscoCA United States Abramson Cancer Center Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Center for Cellular Immunotherapies Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Microbiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Stanford University School of Medicine StanfordCA United States Division of Hematology-Oncology Department of Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department o

CRISPR-Cas9 genome editing has enabled advanced T cell therapies, but occasional loss of the targeted chromosome could be a safety concern. To investigate whether Cas9-induced chromosome loss is a universal phenomenon and evaluate its clinical significance, we conducted a systematic analysis in primary human T cells. Arrayed and pooled CRISPR screens revealed that partial or entire chromosome loss after Cas9-induced DNA cutting is a rare but generalized phenomenon, occurring also in pre-clinical chimeric antigen receptor T cells. T cells with chromosome loss persist for weeks in culture, implying potential interference with clinical use. A modified cell manufacturing process, employed in our first-in-human clinical trial of Cas9-engineered T cells, dramatically reduced chromosome loss while largely preserving genome editing efficacy. Expression of p53 correlated with the protection from chromosome loss observed with this protocol, suggesting both a mechanism and a strategy for T cell engineering that mitigates genotoxicity in the clinic. © 2023, The Authors. All rights reserved.

关键词： T-cells

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Current md forcefields fail to capture key features of protein structure and fluctuations: a case study of cyclophilin a and t4 lysozyme

arXiv

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arXiv 2020年

作者： Mei, Zhe Grigas, Alex T. Treado, John D. Corres, Gabriel Melendez Vuorte, Maisa Sammalkorpi, Maria Regan, Lynne Levine, Zachary A. O’Hern, Corey S. Department of Chemistry Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Biology UPR Humacao Puerto RicoHumacao00792 Puerto Rico Department of Chemistry and Materials Science School of Chemical Engineering Aalto University Aalto Finland Department of Bioproducts and Biosystems School of Chemical Engineering Aalto University Aalto Finland Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Edinburgh United Kingdom Department of Pathology Yale School of Medicine New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unclear whether molecular dynamics (MD) simulations can accurately recapitulate protein conformations. We therefore perform extensive MD simulations across multiple force fields and sampling techniques to investigate the degree to which computer simulations can capture the ensemble of conformations observed in experiments. By analyzing fluctuations in the atomic coordinates and core packing, we show that conformations sampled in MD simulations both move away from and sample a larger conformational space than the ensemble of structures observed in NMR experiments. However, we find that adding inter-residue distance restraints that match those obtained via Nuclear Overhauser Effect measurements enables the MD simulations to sample more NMR-like conformations, though significant differences between the core packing features in restrained MD and the NMR ensemble remain. Given that the protein structures obtained from the MD simulations possess smaller and less dense protein cores compared to those solved by NMR, we suggest that future improvements to MD forcefields should aim to increase the packing of hydrophobic residues in protein cores. © 2020, CC BY.

关键词： Molecular dynamics

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Author Correction: O-GlcNAc modification of leucyl-tRNA synthetase 1 integrates leucine and glucose availability to regulate mTORC1 and the metabolic fate of leucine

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Nature communications 2022年第1期13卷 6253页

作者： Kibum Kim Hee Chan Yoo Byung Gyu Kim Sulhee Kim Yulseung Sung Ina Yoon Ya Chun Yu Seung Joon Park Jong Hyun Kim Kyungjae Myung Kwang Yeon Hwang Sunghoon Kim Jung Min Han Interdisciplinary Program of Integrated OMICS for Biomedical Science Graduate School Yonsei University Seoul 03722 South Korea. Yonsei Institute of Pharmaceutical Sciences College of Pharmacy Yonsei University Incheon 21983 South Korea. Center for Genomic Integrity Institute for Basic Science Ulsan 44919 South Korea. Department of Biotechnology College of Life Sciences and Biotechnology Korea University Seoul 02841 South Korea. Institute for Artificial Intelligence and Biomedical Research Medicinal Bioconvergence Research Center Yonsei University Incheon 21983 South Korea. College of Medicine Gangnam Severance Hospital Yonsei University Seoul 06273 South Korea. Department of Biochemistry School of Medicine Catholic University of Daegu Daegu 42472 South Korea. Department of Biomedical Engineering Ulsan National Institute of Science and Technology Ulsan 44919 South Korea. Interdisciplinary Program of Integrated OMICS for Biomedical Science Graduate School Yonsei University Seoul 03722 South Korea. jhan74@yonsei.ac.kr. Yonsei Institute of Pharmaceutical Sciences College of Pharmacy Yonsei University Incheon 21983 South Korea. jhan74@yonsei.ac.kr. POSTECH Biotech Center Pohang University of Science and Technology Pohang 37673 South Korea. jhan74@yonsei.ac.kr.

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Author Correction: Widespread exposure to SARS-CoV-2 in wildlife communities

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Nature communications 2024年第1期15卷 6940页

作者： Amanda R Goldberg Kate E Langwig Katherine L Brown Jeffrey M Marano Pallavi Rai Kelsie M King Amanda K Sharp Alessandro Ceci Christopher D Kailing Macy J Kailing Russell Briggs Matthew G Urbano Clinton Roby Anne M Brown James Weger-Lucarelli Carla V Finkielstein Joseph R Hoyt Department of Biological Sciences Virginia Tech Blacksburg VA USA. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. Department of Biomedical Sciences and Pathobiology Virginia Tech Blacksburg VA USA. Translational Biology Medicine and Health Graduate Program Virginia Tech Roanoke VA USA. Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg VA USA. Department of Biochemistry Virginia Tech Blacksburg VA USA. Data Services University Libraries Virginia Tech Blacksburg VA USA. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. Academy of Integrated Science Virginia Tech Blacksburg VA USA. Department of Biological Sciences Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. finkielc@vt.edu. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. finkielc@vt.edu. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Academy of Integrated Science Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Department of Biological Sciences Virginia Tech Blacksburg VA USA. hoytjosephr@***.

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SARS-CoV-2 identified through crystallographic screening and computational docking

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Science Advances 2021年第16期7卷

作者： Schuller, Marion Correy, Galen J. Gahbauer, Stefan Fearon, Daren Wu, Taiasean Díaz, Roberto Efraín Young, Iris D. Martins, Luan Carvalho Smith, Dominique H. Schulze-Gahmen, Ursula Owens, Tristan W. Deshpande, Ishan Merz, Gregory E. Thwin, Aye C. Biel, Justin T. Peters, Jessica K. Moritz, Michelle Herrera, Nadia Kratochvil, Huong T. Aimon, Anthony Bennett, James M. Neto, Jose Brandao Cohen, Aina E. Dias, Alexandre Douangamath, Alice Dunnett, Louise Fedorov, Oleg Ferla, Matteo P. Fuchs, Martin R. Gorrie-Stone, Tyler J. Holton, James M. Johnson, Michael G. Krojer, Tobias Meigs, George Powell, Ailsa J. Rack, Johannes Gregor Matthias Rangel, Victor L. Russi, Silvia Skyner, Rachael E. Smith, Clyde A. Soares, Alexei S. Wierman, Jennifer L. Zhu, Kang O Brien, Peter Jura, Natalia Ashworth, Alan Irwin, John J. Thompson, Michael C. Gestwicki, Jason E. Von Delft, Frank Shoichet, Brian K. Fraser, James S. Ahel, Ivan Sir William Dunn School of Pathology University of Oxford South Parks Road OxfordOX1 3RE United Kingdom Department of Bioengineering and Therapeutic Sciences University of California San Francisco San FranciscoCA94158 United States Department of Pharmaceutical Chemistry University of California San Francisco San FranciscoCA94158 United States Diamond Light Source Ltd. Harwell Science and Innovation Campus DidcotOX11 0DE United Kingdom Institute for Neurodegenerative Disease University of California San Francisco San FranciscoCA94158 United States Chemistry and Chemical Biology Graduate Program University of California San Francisco San FranciscoCA94158 United States Tetrad Graduate Program University of California San Francisco San FranciscoCA94158 United States Coronavirus Research Group Structural Biology Consortium University of California San Francisco San FranciscoCA94158 United States Biochemistry Department Institute for Biological Sciences Federal University of Minas Gerais Belo Horizonte Brazil Helen Diller Family Comprehensive Cancer University of California San Francisco San FranciscoCA94158 United States Centre for Medicines Discovery University of Oxford South Parks Road HeadingtonOX3 7DQ United Kingdom Stanford Synchrotron Radiation Lightsource Slac National Accelerator Center Menlo ParkCA94025 United States Wellcome Centre for Human Genetics University of Oxford Old Road Campus OxfordOX3 7BN United Kingdom National Synchrotron Light Source Ii Brookhaven National Laboratory UptonNY11973 United States Department of Biochemistry and Biophysics University of California San Francisco San FranciscoCA94158 United States Department of Molecular Biophysics and Integrated Bioimaging Lawrence Berkeley National Laboratory BerkeleyCA94720 United States ChemPartner Corporation South San FranciscoCA94080 United States Structural Genomics Consortium University of Oxford Old Road Campus HeadingtonOX3 7DQ United Kingdom School of

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors. © 2021 American Association for the Advancement of Science. All rights reserved.

关键词： Coronavirus

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Analyses of protein cores reveal fundamental differences between solution and crystal structures

arXiv

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arXiv 2019年

作者： Mei, Zhe Treado, John D. Grigas, Alex T. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures|in the root-mean-square deviations of the Cα atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography. Copyright © 2019, The Authors. All rights reserved.

关键词： X ray crystallography

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Utility of plasma GFAP as a secondary endpoint for clinical trials in Alzheimer's disease

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The journal of prevention of Alzheimer's disease 2025年 100205页

作者： Sarah Abbas Pamela C L Ferreira Bruna Bellaver Guilherme Povala Francieli Rohden Cristiano Schaffer Aguzzoli Hussein Zalzale João Pedro Ferrari-Souza Douglas T Leffa Firoza Z Lussier Carolina Soares Guilherme Bauer-Negrini Markley Silva Oliveira-Junior Matheus Scarpatto Rodrigues Pampa Saha Emma Ruppert Marina Scop Medeiros Cécile Tissot Joseph Therriault Nesrine Rahmouni Stijn Servaes Andrea L Benedet Nicholas J Ashton Dana L Tudorascu Serge Gauthier Helmet Karim Chang Hyung Hong Hyun Woong Roh Eduardo R Zimmer Thomas K Karikari Henrik Zetterberg Kaj Blennow Anum Saeed Sang Joon Son Pedro Rosa-Neto Tharick Pascoal Department of Psychiatry School of Medicine University of Pittsburgh Pittsburgh PA USA. Department of Psychiatry School of Medicine University of Pittsburgh Pittsburgh PA USA Graduate Program in Biological Sciences: Biochemistry Universidade Federal do Rio Grande do Sul Porto Alegre RS Brazil. Brain Institute of Rio Grande do Sul PUCRS 6690 Ipiranga Avenue 90610-000 Porto Alegre RS Brazil. Translational Neuroimaging Laboratory McGill University Research Centre for Studies in Aging Alzheimer's Disease Research Unit Douglas Research Institute Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal Canada Department of Neurology and Neurosurgery Psychiatry and Pharmacology and Therapeutics McGill University Montreal QC Canada Department of Molecular Biophysics and Integrated Bioimaging Lawrence Berkeley National Laboratory Berkeley CA USA. Department of Neurology and Neurosurgery Psychiatry and Pharmacology and Therapeutics McGill University Montreal QC Canada. Translational Neuroimaging Laboratory McGill University Research Centre for Studies in Aging Alzheimer's Disease Research Unit Douglas Research Institute Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal Canada. Department of Psychiatry and Neurochemistry The Sahlgrenska Academy at the University of Gothenburg Mölndal Sweden Clinical Neurochemistry Laboratory Sahlgrenska University Hospital Gothenburg Sweden. Clinical Neurochemistry Laboratory Sahlgrenska University Hospital Gothenburg Sweden Wallenberg Centre for Molecular and Translational Medicine University of Gothenburg Gothenburg Sweden Department of Old Age Psychiatry Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Department of Bioengineering University of Pittsburgh Pittsburgh PA USA. Department of Psychiatry Ajou University School of Medicine Suwon Republic of Korea South Korea. Graduate Program

BACKGROUND:Clinical trials have recently incorporated plasma glial fibrillary acidic protein (GFAP) as an exploratory endpoint. To include plasma GFAP as a secondary endpoint, it is essential to characterize its longitudinal progression in target populations. OBJECTIVE:To evaluate the potential use of plasma GFAP changes as a secondary endpoint in Alzheimer's disease trials. METHODS:We longitudinally evaluated plasma GFAP in individuals with amyloid-beta (Aβ)-PET scans at baseline in three well-characterized cohorts. Cox proportional hazards regression tested the association between changes in plasma GFAP and cognitive function. Analysis of the 95 % confidence interval of annualized change in plasma GFAP provided statistical inference for a significant longitudinal change. Effect size was calculated as the group mean divided by the standard deviation (SD). We estimated the sample size needed to test a 25% drug effect with 80% power on reducing changes in GFAP. RESULTS:We assessed 487 individuals [176 cognitively unimpaired (CU; 29% Aβ positive) and 311 cognitively impaired (CI; 51% Aβ positive)] with some degree of cerebrovascular disease (Fazekas 1-3), over a mean (SD) follow-up of 1.84 (0.46) years. Changes in plasma GFAP were significantly associated with worsening in Clinical Dementia Rating sum of boxes (CDR-SB) score across the population (p < 0.0001). In CU, only Aβ positive individuals showed significant changes in GFAP (p < 0.001). On the other hand, both CI Aβ positive and negative individuals showed longitudinal progression in GFAP levels (p < 0.0001). The effect size of changes in plasma GFAP was higher in CU Aβ positive (0.44), followed by CI Aβ positive (0.42) and CI Aβ negative (0.38). Clinical trials focusing on CU Aβ positive would require 1320 individuals per study arm, while focusing on CI Aβ positive would require 1440 individuals per study arm. CONCLUSION:Plasma GFAP increased in parallel with cognitive decline, making it a candidate for monitor

关键词： Biomarker Clinical trial Plasma GFAP

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Polygenic risk modeling for prediction of epithelial ovarian cancer risk (vol 30, pg 349, 2021)

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EUROPEAN JOURNAL OF HUMAN GENETICS 2022年第5期30卷 630-631页

作者： Dareng, Eileen O. Tyrer, Jonathan P. Barnes, Daniel R. Jones, Michelle R. Yang, Xin Aben, Katja K. H. Adank, Muriel A. Agata, Simona Andrulis, Irene L. Anton-Culver, Hoda Antonenkova, Natalia N. Aravantinos, Gerasimos Arun, Banu K. Augustinsson, Annelie Balmana, Judith Bandera, Elisa V. Barkardottir, Rosa B. Barrowdale, Daniel Beckmann, Matthias W. Beeghly-Fadiel, Alicia Benitez, Javier Bermisheva, Marina Bernardini, Marcus Q. Bjorge, Line Black, Amanda Bogdanova, Natalia V. Bonanni, Bernardo Borg, Ake Brenton, James D. Budzilowska, Agnieszka Butzow, Ralf Buys, Saundra S. Cai, Hui Caligo, Maria A. Campbell, Ian Cannioto, Rikki Cassingham, Hayley Chang-Claude, Jenny Chanock, Stephen J. Chen, Kexin Chiew, Yoke-Eng Chung, Wendy K. Claes, Kathleen B. M. Colonna, Sarah Cook, Linda S. Couch, Fergus J. Daly, Mary B. Dao, Fanny Davies, Eleanor de la Hoya, Miguel de Putter, Robin Dennis, Joe DePersia, Allison Devilee, Peter Diez, Orland Ding, Yuan Chun Doherty, Jennifer A. Domchek, Susan M. Dork, Thilo du Bois, Andreas Durst, Matthias Eccles, Diana M. Eliassen, Heather A. Engel, Christoph Evans, Gareth D. Fasching, Peter A. Flanagan, James M. Fortner, Renee T. Machackova, Eva Friedman, Eitan Ganz, Patricia A. Garber, Judy Gensini, Francesca Giles, Graham G. Glendon, Gord Godwin, Andrew K. Goodman, Marc T. Greene, Mark H. Gronwald, Jacek Hahnen, Eric Haiman, Christopher A. Hakansson, Niclas Hamann, Ute Hansen, Thomas V. O. Harris, Holly R. Hartman, Mikael Heitz, Florian Hildebrandt, Michelle A. T. Hogdall, Estrid Hogdall, Claus K. Hopper, John L. Huang, Ruea-Yea Huff, Chad Hulick, Peter J. Huntsman, David G. Imyanitov, Evgeny N. Isaacs, Claudine Jakubowska, Anna James, Paul A. Janavicius, Ramunas Jensen, Allan Johannsson, Oskar Th. John, Esther M. Jones, Michael E. Kang, Daehee Karlan, Beth Y. Karnezis, Anthony Kelemen, Linda E. Khusnutdinova, Elza Kiemeney, Lambertus A. Kim, Byoung-Gie Kjaer, Susanne K. Komenaka, Ian Kupryjanczyk, Jolanta Kurian, Allison W. Kwong, Ava Lambrechts, Diether Larson, Melissa C. Lazaro, Conxi L University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands The Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands Veneto Institute of Oncology IOV—IRCCS Immunology and Molecular Oncology Unit Padua Italy Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada University of Toronto Department of Molecular Genetics Toronto ON Canada University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus ‘Agii Anargiri’ Cancer Hospital Athens Greece University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden Vall d’Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain University Hospital of Vall d’Hebron Department of Medical Oncology Barcelona Spain Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA Landspitali University Hospital Department of Pathology Reykjavik Iceland University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany Vanderbilt University School of Medicine Division of Epidemiology Department

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

关键词： Clinical genetics Genetic markers Risk factors Biomedicine general Human Genetics Bioinformatics Gene Expression Cytogenetics

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