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Maternal human papillomavirus infection and the risk of congenital malformations: A nationwide population-based cohort study

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Journal of Medical Virology 2024年第4期96卷 e29549页

作者： Hsieh, Tina Yi Jin Chen, Thomas Yen Ting Liao, Pei-Lun Huang, Jing-Yang Ma, Kevin Sheng-Kai Hung, Yao-Min Chang, Renin Wei, James Cheng-Chung Department of Obstetrics and Gynecology Beth Israel Deaconess Medical Center Boston MA United States Department of Bioinformatics Harvard Medical School Boston MA United States Department of Otolaryngology-Head and Neck Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA United States Department of Dermatology Massachusetts General Hospital Boston MA United States Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taipei Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Recreation and Sports Management Tajen University Pingtung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan

Previous research has explored theories regarding the vertical transmission of human papillomavirus (HPV) infection and its association with adverse pregnancy and perinatal outcomes. However, the impact of maternal HPV infection on congenital anomalies (CAs) in offspring remains relatively understudied. We conducted a population-based cohort study linking the Taiwan Birth Registry, Taiwan Death Registry, and National Health Insurance Research database, in which newborns born in Taiwan between 2009 and 2015 were included. We established a maternal HPV infection cohort comprising 37 807 newborns and matched them with a comparison group of 151 228 newborns at a 1:4 ratio based on index year, age, and sex. The study examined a composite outcome and subgroups of different types of congenital malformations. Differences in cumulative incidence of CAs were assessed using Kaplan–Meier curves and log-rank tests. Adjusted hazard ratios (aHRs) were estimated using Cox proportional hazard regressions. No significant association was found between HPV infection and the broad spectrum of CAs (aHR: 1.04, 95% confidence interval [CI]: 0.98–1.10;log-rank test p = 0.14). However, we observed a 19% increased risk of musculoskeletal CAs in the maternal HPV infection group (aHR: 1.19;95% CI: 1.05–1.34) compared to those without maternal HPV exposure. Other factors, including the type of HPV (aHR: 0.65;95% CI: 0.16–2.63), the timing of exposure (during or before pregnancy), and maternal age (aHR for <30 years: 1.02, 95% CI: 0.94–1.1;aHR for 30–39 years: 1.05, 95% CI: 0.99–1.11;aHR for ≥40 years: 0.88, 95% CI: 0.67–1.17), did not significantly affect the risk for any CA. In conclusion, gestation detection of HPV infection was associated with musculoskeletal CAs but not other major CAs. Prospective studies are warranted to elucidate the necessity of prenatal screening in populations at risk. © 2024 Wiley Periodicals LLC.

关键词： adverse pregnancy congenital malformations human papillomavirus infection outcomes

Kernel-based parameter estimation of dynamical systems with unknown observation functions

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arXiv 2020年

作者： Lindenbaum, Ofir Sagiv, Amir Mishne, Gal Talmon, Ronen Program in Applied Mathematics Yale University 51 Prospect Street New HavenCT06511 United States Department of Applied Physics and Applied Mathematics Columbia University 500 West 120th Street New YorkNY10027 United States Halicioglu Data Science Institute UC San Diego 9500 Gilman Drive MS0555 United States SDSC 215E San DiegoCA92093-0555 United States Faculty of Electrical Engineering Technion - Israel Institute of Technology Haifa32000 Israel

A low-dimensional dynamical system is observed in an experiment as a high-dimensional signal;for example, a video of a chaotic pendulums system. Assuming that we know the dynamical model up to some unknown parameters, can we estimate the underlying system's parameters by measuring its time-evolution only once? The key information for performing this estimation lies in the temporal inter-dependencies between the signal and the model. We propose a kernel-based score to compare these dependencies. Our score generalizes a maximum likelihood estimator for a linear model to a general nonlinear setting in an unknown feature space. We estimate the system's underlying parameters by maximizing the proposed score. We demonstrate the accuracy and efficiency of the method using two chaotic dynamical systems - the double pendulum and the Lorenz'63 model. © 2020, CC BY.

关键词： Maximum likelihood estimation

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models

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arXiv 2020年

作者： Ezaki, Takahiro Himeno, Yu Watanabe, Takamitsu Masuda, Naoki Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan PRESTO JST 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Department of Aeronautics and Astronautics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Laboratory for Cognition Circuit Dynamics RIKEN Centre for Brain Science Saitama351-0198 Japan International Research Center for Neurointelligence The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants, and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data is relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors

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Journal of Medical Virology 2024年第5期96卷 e29648页

作者： Chen, Sheng-Yin Hsieh, Tina Yi Jin Hung, Yao-Min Oh, Jae Won Chen, Shen-Kai Wang, Shiow-Ing Chang, Renin Wei, James Cheng-Chung Clinical and Translational Epidemiology Unit Massachusetts General Hospital and Harvard Medical School Boston Morocco Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston Morocco Department of Epidemiology Harvard T. H. Chan School of Public Health Boston Morocco Department of Obstetrics & Gynecology Beth Israel Deaconess Medical Center Boston Morocco Department of Bioinformatics Harvard Medical School Boston Morocco Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Neurology Brigham and Women's Hospital Boston Morocco Department of Neurology Harvard Medical School Boston MA United States Department of Education Kaohsiung Chang Gung Memorial Hospital Boston MA United States Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Department of Nursing Jen-Teh Junior College of Medicine Nursing and Management Miaoli County Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Recreation and Sports Management Tajen University Pintung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Office of Research and Development Asia University Taichung Taiwan

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56–0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48–0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53–0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42–0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23–0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73–0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50–0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke even

关键词： cohort study COVID-19 vaccination strokes

scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

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Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

Small inter-event times govern epidemic spreading on temporal networks

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arXiv 2019年

作者： Masuda, Naoki Holme, Petter Department of Mathematics University at Buffalo State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Just like the degrees of human and animal interaction networks, the distribution of the times between interactions is known to often be right-skewed and fat-tailed. Both these distributions affect epidemic dynamics strongly, but, as we show in this Letter, for very different reasons. Whereas the high degrees of the tail are critical for facilitating epidemics, it is the small interevent times that control the dynamics of epidemics. We investigate this effect both analytically and numerically for different versions of the Susceptible-Infected-Recovered model on different types of networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Epidemiology

Comparative analyses for plant virus-based cancer immunotherapy drug development

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Cell Biomaterials

Cell Biomaterials 2025年

作者： Anthony O. Omole Hannah S. Newton Edward Cedrone Kimia Nematpour Shaojun Xie Yongmei Zhao Bao Tran Marina A. Dobrovolskaia Nicole F. Steinmetz Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California San Diego La Jolla San Diego CA 92093 USA Shu and K.C. Chien and Peter Farrell Collaboratory University of California San Diego La Jolla San Diego CA 92093 USA Center for Nano-ImmunoEngineering University of California San Diego La Jolla San Diego CA 92093 USA Moores Cancer Center University of California San Diego La Jolla San Diego CA 92093 USA Nanotechnology Characterization Lab Cancer Research Technology Program Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute Frederick MD 21702 USA Sequencing Facility Bioinformatics Group Biomedical Informatics and Data Science Directorate Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute Frederick MD 21702 USA CCR Sequencing Facility Cancer Research Technology Program Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute Frederick MD 21702 USA Department of Bioengineering University of California San Diego La Jolla San Diego CA 92093 USA Department of Radiology University of California San Diego La Jolla San Diego CA USA Institute for Materials Discovery and Design University of California San Diego La Jolla San Diego CA 92093 USA Center for Engineering in Cancer Institute of Engineering Medicine University of California San Diego La Jolla San Diego CA 92093 USA

Cowpea mosaic virus (CPMV) is a plant virus with potent anti-tumor efficacy. Intratumoral CPMV, but not cowpea chlorotic mottle virus (CCMV), reprograms the tumor microenvironment priming durable, systemic anti-tumor immunity. Here, we performed a side-by-side mechanism of action comparison between CPMV and CCMV. Through immune-multiplexing and trafficking studies we observed that CPMV and CCMV are taken up by innate immune cells at similar rates but are processed differently. While CCMV induces proinflammatory interleukins 22, 23, and 27, CPMV is a strong inducer of type I, II, and III interferons, which contribute to the activation and enhancement of immune cell function. We observed that CPMV RNAs activate Toll-like receptor (TLR) 7 upon delivery—and not CCMV RNAs; CPMV RNAs persist longer within cells compared with CCMV. Taken together, this work identifies determinants for CPMV’s unique mechanism for intratumoral immunotherapy. It also broadens our understanding of the utility of plant viruses for biomedical applications.

关键词： plant viruses intratumoral immunotherapy cowpea mosaic virus cowpea chlorotic mottle virus cancer nanotechnology