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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Research Square

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Research Square 2021年

作者： Freedman, Matthew L. Baca, Sylvan C. Singler, Cassandra Zacharia, Soumya Seo, Ji-Heui Morova, Tunc Hach, Faraz Ding, Yi Schwarz, Tommer Huang, Chia-Chi Flora Kalita, Cynthia Groha, Stefan Pomerantz, Mark M. Wang, Victoria Linder, Simon Sweeney, Christopher J. Zwart, Wilbert Lack, Nathan A. Pasaniuc, Bogdan Takeda, David Y. Gusev, Alexander Department of Medical Oncology Dana-Farber Cancer Institute BostonMA United States Center for Functional Cancer Epigenetics Dana-Farber Cancer Institute BostonMA United States The Eli and Edythe L. Broad Institute CambridgeMA United States Laboratory of Genitourinary Cancer Pathogenesis Center for Cancer Research National Cancer Institute BethesdaMD United States Vancouver Prostate Centre University of British Columbia VancouverBC Canada Bioinformatics Interdepartmental Program UCLA Los AngelesCA United States Division of Genetics Brigham & Women’s Hospital BostonMA United States Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute BostonMA United States Department of Biostatistics Harvard School of Public Health BostonMA United States Division of Oncogenomics Oncode Institute The Netherlands Cancer Institute Amsterdam Netherlands Laboratory of Chemical Biology Institute for Complex Molecular Systems Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Medicine Koç University Istanbul Turkey Department of Computational Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine at UCLA Los AngelesCA United States

Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation. © 2021, CC BY.

关键词： Transcription

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Whole-genome and Transcriptome Sequencing of Prostate Cancer Identify New Genetic Alterations Driving Disease Progression

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European urology 2018年第3期73卷 322-339页

作者： Shancheng Ren Gong-Hong Wei Dongbing Liu Liguo Wang Yong Hou Shida Zhu Lihua Peng Qin Zhang Yanbing Cheng Hong Su Xiuqing Zhou Jibin Zhang Fuqiang Li Hancheng Zheng Zhikun Zhao Changjun Yin Zengquan He Xin Gao Haiyen E Zhau Chia-Yi Chu Jason Boyang Wu Colin Collins Stanislav V Volik Robert Bell Jiaoti Huang Kui Wu Danfeng Xu Dingwei Ye Yongwei Yu Lianhui Zhu Meng Qiao Hang-Mao Lee Yuehong Yang Yasheng Zhu Xiaolei Shi Rui Chen Yang Wang Weidong Xu Yanqiong Cheng Chuanliang Xu Xu Gao Tie Zhou Bo Yang Jianguo Hou Li Liu Zhensheng Zhang Yao Zhu Chao Qin Pengfei Shao Jun Pang Leland W K Chung Jianfeng Xu Chin-Lee Wu Weide Zhong Xun Xu Yingrui Li Xiuqing Zhang Jian Wang Huanming Yang Jun Wang Haojie Huang Yinghao Sun Department of Urology Shanghai Changhai Hospital Second Military Medical University Shanghai China. Biocenter Oulu Faculty of Biochemistry and Molecular Medicine University of Oulu Oulu Finland. BGI-Shenzhen Shenzhen China China National GeneBank-Shenzhen BGI-Shenzhen Shenzhen China. Division of Biomedical Statistics and Informatics Mayo Clinic College of Medicine Rochester MN USA. China National GeneBank-Shenzhen BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Engineering School of Life Sciences Fudan University Shanghai China. Division of Genomics and Bioinformatics CUHK-BGI Innovation Institute of Trans-Omics The Chinese University of Hong Kong Hong Kong China. BGI Education Center University of Chinese Academy of Sciences Shenzhen China. BGI-Shenzhen Shenzhen China. School of Biological Science and Medical Engineering Southeast University Nanjing China State Key Laboratory of Bioelectronics Southeast University Nanjing China. Department of Urology The First Affiliated Hospital of Nanjing Medical University Nanjing China. Department of Urology The Third Affiliated Hospital of Sun Yat-Sen University Guangzhou China. Uro-Oncology Research Program Department of Medicine Samuel Oschin Comprehensive Cancer Institute Cedars-Sinai Medical Center Los Angeles CA USA. Vancouver Prostate Centre and Department of Urologic Sciences University of British Columbia Vancouver BC Canada. Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles CA USA. Department of Urology Changzheng Hospital Second Military Medical University Shanghai China. Department of Urology Fudan University Shanghai Cancer Center Shanghai China. Department of Pathology Shanghai Changhai Hospital Second Military Medical University Shanghai China. State Key Laboratory of Genetic Engineering School of Life Sciences Fudan University Shanghai China Program for Personalized Cancer Care NorthShore University HealthS

BACKGROUND:Global disparities in prostate cancer (PCa) incidence highlight the urgent need to identify genomic abnormalities in prostate tumors in different ethnic populations including Asian men. OBJECTIVE:To systematically explore the genomic complexity and define disease-driven genetic alterations in PCa. DESIGN, SETTING, AND PARTICIPANTS:The study sequenced whole-genome and transcriptome of tumor-benign paired tissues from 65 treatment-naive Chinese PCa patients. Subsequent targeted deep sequencing of 293 PCa-relevant genes was performed in another cohort of 145 prostate tumors. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:The genomic alteration landscape in PCa was analyzed using an integrated computational pipeline. Relationships with PCa progression and survival were analyzed using nonparametric test, log-rank, and multivariable Cox regression analyses. RESULTS AND LIMITATIONS:We demonstrated an association of high frequency of CHD1 deletion with a low rate of TMPRSS2-ERG fusion and relatively high percentage of mutations in androgen receptor upstream activator genes in Chinese patients. We identified five putative clustered deleted tumor suppressor genes and provided experimental and clinical evidence that PCDH9, deleted/loss in approximately 23% of tumors, functions as a novel tumor suppressor gene with prognostic potential in PCa. Furthermore, axon guidance pathway genes were frequently deregulated, including gain/amplification of PLXNA1 gene in approximately 17% of tumors. Functional and clinical data analyses showed that increased expression of PLXNA1 promoted prostate tumor growth and independently predicted prostate tumor biochemical recurrence, metastasis, and poor survival in multi-institutional cohorts of patients with PCa. A limitation of this study is that other genetic alterations were not experimentally investigated. CONCLUSIONS:There are shared and salient genetic characteristics of PCa in Chinese and Caucasian men. Novel genetic alterations i

关键词： AR coactivator mutation Axon guidance pathway CHD1 deletion Chinese prostate cancer Clustered deleted tumor suppressor genes Personalized medicine Prognosis RNA-seq TMPRSS2-ERG fusion Whole genome sequencing

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Correction: Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer

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Clinical & experimental metastasis 2023年第4期40卷 373页

作者： Sophia M Orbach Michael D Brooks Yining Zhang Scott E Campit Grace G Bushnell Joseph T Decker Ryan J Rebernick Sriram Chandrasekaran Max S Wicha Jacqueline S Jeruss Lonnie D Shea Department of Biomedical Engineering University of Michigan Ann Arbor MI USA. Department of Internal Medicine University of Michigan Ann Arbor MI USA. Department of Chemical Engineering University of Michigan Ann Arbor MI USA. Program in Chemical Biology University of Michigan Ann Arbor MI USA. Medical Science Training Program University of Michigan Ann Arbor MI USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Rogel Cancer Center University of Michigan Ann Arbor MI USA. Department of Surgery University of Michigan Ann Arbor MI USA. Department of Pathology University of Michigan Ann Arbor MI USA. Department of Biomedical Engineering University of Michigan Ann Arbor MI USA. ldshea@umich.edu. Department of Chemical Engineering University of Michigan Ann Arbor MI USA. ldshea@umich.edu. Rogel Cancer Center University of Michigan Ann Arbor MI USA. ldshea@umich.edu.

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Development and evaluation of a hospital-wide healthcare-associated surgical site infection detection algorithm

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Journal of Microbiology, Immunology and Infection 2015年第2期48卷 S43-S43页

作者： Yi-Ju Tseng Bo-Chiang Huang Hui-Chi Lin Ming-Yuan Chen Rung-Ji Shang Wang-Huei Sheng Yee-Chun Chen Feipei Lai Shan-Chwen Chang Children's Hospital Informatics Program Boston Children's Hospital Boston MA USA Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan Center for Infection Control National Taiwan University Hospital (NTUH) Taiwan Information Systems Office NTUH Taipei Taiwan Department of Internal Medicine NTUH and College of Medicine Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

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An atlas of dynamic chromatin landscapes in mouse fetal development (vol 583, pg 744, 2020)

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NATURE 2021年第7842期589卷 E4-E4页

作者： Gorkin, David U. Barozzi, Iros Zhao, Yuan Zhang, Yanxiao Huang, Hui Lee, Ah Young Li, Bin Chiou, Joshua Wildberg, Andre Ding, Bo Zhang, Bo Wang, Mengchi Strattan, J. Seth Davidson, Jean M. Qiu, Yunjiang Afzal, Veena Akiyama, Jennifer A. Plajzer-Frick, Ingrid Novak, Catherine S. Kato, Momoe Garvin, Tyler H. Pham, Quan T. Harrington, Anne N. Mannion, Brandon J. Lee, Elizabeth A. Fukuda-Yuzawa, Yoko He, Yupeng Preissl, Sebastian Chee, Sora Han, Jee Yun Williams, Brian A. Trout, Diane Amrhein, Henry Yang, Hongbo Cherry, J. Michael Wang, Wei Gaulton, Kyle Ecker, Joseph R. Shen, Yin Dickel, Diane E. Visel, Axel Pennacchio, Len A. Ren, Bing Ludwig Institute for Cancer Research La Jolla CA USA Center for Epigenomics 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 School of Medicine La Jolla CA USA Department of Cellular and Molecular Medicine University of California San Diego School of Medicine La Jolla CA USA Institute of Genomic Medicine University of California San Diego School of Medicine La Jolla CA USA Moores Cancer Center University of California San Diego School of Medicine La Jolla CA USA Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory Berkeley CA USA Department of Surgery and Cancer Imperial College London London UK US Department of Energy Joint Genome Institute Berkeley CA USA School of Natural Sciences University of California Merced Merced CA USA Comparative Biochemistry Program University of California Berkeley Berkeley CA USA Genomic Analysis Laboratory Salk Institute for Biological Studies La Jolla CA USA Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA Department of Biochemistry and Molecular Biology Penn State School of Medicine Hershey PA USA Stanford University School of Medicine Department of Genetics Stanford CA USA Howard Hughes Medical Institute Salk Institute for Biological Studies La Jolla CA USA Division of Biology and Biological Engineering California Institute of Technology Pasadena CA USA Institute for Human Genetics and University of California San Francisco San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA

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Transdermal nanolipoplex simultaneously inhibits subcutaneous melanoma growth and suppresses systemically metastatic melanoma by activating host immunity

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Nanomedicine: Nanotechnology, Biology, and Medicine 2023年 47卷 102628-102628页

作者： Chen, Chia-Hung Weng, Tzu-Han Chuang, Cheng-Hsun Huang, Kai-Yao Huang, Sih-Cheng Chen, Pin-Rong Huang, Hsiao-Hsuan Huang, Ling-Ya Shen, Pei-Chun Chuang, Po-Ya Huang, Hsiao-Yen Wu, Yi-Syuan Chang, Hao-Chiun Weng, Shun-Long Liao, Kuang-Wen Department of Medical Research Hsinchu MacKay Memorial Hospital Hsinchu City30071 Taiwan Dependent of Medical Education MacKay Memorial Hospital Taipei10449 Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Industrial Development Graduate Program of College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Medicine MacKay Medical College New Taipei City25245 Taiwan Department of Obstetrics and Gynecology Hsinchu MacKay Memorial Hospital Hsinchu City30071 Taiwan MacKay Junior College of Medicine Nursing and Management Taipei City11260 Taiwan Drug Development and Value Creation Research Center College of Dental Medicine Kaohsiung Medical University School of Dentistry Graduate Institute of Medicine College of Medicine Center for Cancer Research Kaohsiung Medical University Kaohsiung City80708 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Biotechnology and Bioindustry Sciences National Cheng Kung University Tainan City70101 Taiwan Ph.D. Degree Program of Biomedical Science and Engineering National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan

Benefit for clinical melanoma treatments, the transdermal neoadjuvant therapy could reduce surgery region and increase immunotherapy efficacy. Using lipoplex (Lipo-PEG-PEI-complex, LPPC) encapsulated doxorubicin (DOX) and carrying CpG oligodeoxynucleotide;the transdermally administered nano-liposomal drug complex (LPPC-DOX-CpG) would have high cytotoxicity and immunostimulatory activity to suppress systemic metastasis of melanoma. LPPC-DOX-CpG dramatically suppressed subcutaneous melanoma growth by inducing tumor cell apoptosis and recruiting immune cells into the tumor area. Animal studies further showed that the colonization and growth of spontaneously metastatic melanoma cells in the liver and lung were suppressed by transdermal LPPC-DOX-CpG. Furthermore, NGS analysis revealed IFN-γ and NF-κB pathways were triggered to recruit and activate the antigen-presenting-cells and effecter cells, which could activate the anti-tumor responses as the major mechanism responsible for the therapeutic effect of LPPC-DOX-CpG. Finally, we have successfully proved transdermal LPPC-DOX-CpG as a promising penetrative carrier to activate systemic anti-tumor immunity against subcutaneous and metastatic tumor. © 2022

关键词： Tumors

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Expression of concern: Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy

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Nanoscale 2024年第9期16卷 4919页

作者： Michael A Casasanta G M Jonaid Liam Kaylor William Y Luqiu Maria J Solares Mariah L Schroen William J Dearnaley Jarad Wilson Madeline J Dukes Deborah F Kelly Department of Biomedical Engineering Pennsylvania State University University Park PA 16802 USA. Debkelly@psu.edu. Materials Research Institute Pennsylvania State University University Park PA 16802 USA. Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Department of Electrical and Computer Engineering Duke University Durham NC 27708 USA. RayBiotech Life Peachtree Corners GA 30092 USA. Applications Science Protochips Inc. Morrisville NC 27560 USA.

Expression of concern for 'Microchip-based structure determination of low-molecular weight proteins using cryo-electron microscopy' by Michael A. Casasanta , , 2021, , 7285-7293, https://***/10.1039/D1NR00388G.

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Correction: Measurement and models accounting for cell death capture hidden variation in compound response

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Cell death & disease 2020年第8期11卷 699页

作者： Song Yi Bae Ning Guan Rui Yan Katrina Warner Scott D Taylor Aaron S Meyer Department of Pharmacology University of Minnesota-Twin Cities Minneapolis MN USA. Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA USA. Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA. Biological and Biomedical Sciences Program Harvard University Cambridge MA USA. Department of Bioengineering University of California Los Angeles CA USA. Department of Bioengineering University of California Los Angeles CA USA. a@***. Department of Bioinformatics University of California Los Angeles CA USA. a@***. Jonsson Comprehensive Cancer Center University of California Los Angeles CA USA. a@***. Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research University of California Los Angeles CA USA. a@***.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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