Network and systems medicine最新文献

{"title":"Integrative Data Analytic Framework to Enhance Cancer Precision Medicine.","authors":"Thomas Gaudelet, Noël Malod-Dognin, Nataša Pržulj","doi":"10.1089/nsm.2020.0015","DOIUrl":"10.1089/nsm.2020.0015","url":null,"abstract":"<p><p>With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data, to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms the competing methods and can identify new associations. Furthermore, it captures the underlying biology predictive of drug response. Through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problem.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":"4 1","pages":"60-73"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25540971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Consistency between Gene Expression and the Gene Regulatory Network of <i>Corynebacterium glutamicum</i>.","authors":"Doglas Parise, Mariana Teixeira Dornelles Parise, Evans Kataka, Rodrigo Bentes Kato, Markus List, Andreas Tauch, Vasco Ariston de Carvalho Azevedo, Jan Baumbach","doi":"10.1089/nsm.2020.0014","DOIUrl":"10.1089/nsm.2020.0014","url":null,"abstract":"<p><p><b>Background:</b> Transcriptional regulation of gene expression is crucial for the adaptation and survival of bacteria. Regulatory interactions are commonly modeled as Gene Regulatory Networks (GRNs) derived from experiments such as RNA-seq, microarray and ChIP-seq. While the reconstruction of GRNs is fundamental to decipher cellular function, even GRNs of economically important bacteria such as <i>Corynebacterium glutamicum</i> are incomplete. <b>Materials and Methods:</b> Here, we analyzed the predictive power of GRNs if used as in silico models for gene expression and investigated the consistency of the <i>C. glutamicum</i> GRN with gene expression data from the GEO database. <b>Results:</b> We assessed the consistency of the <i>C. glutamicum</i> GRN using real, as well as simulated, expression data and showed that GRNs alone cannot explain the expression profiles well. <b>Conclusion:</b> Our results suggest that more sophisticated mechanisms such as a combination of transcriptional, post-transcriptional regulation and signaling should be taken into consideration when analyzing and constructing GRNs.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":"4 1","pages":"51-59"},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8006670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25540970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Early Stage Researcher's Primer on Systems Medicine Terminology.","authors":"Massimiliano Zanin, Nadim A A Aitya, José Basilio, Jan Baumbach, Arriel Benis, Chandan K Behera, Magda Bucholc, Filippo Castiglione, Ioanna Chouvarda, Blandine Comte, Tien-Tuan Dao, Xuemei Ding, Estelle Pujos-Guillot, Nenad Filipovic, David P Finn, David H Glass, Nissim Harel, Tomas Iesmantas, Ilinka Ivanoska, Alok Joshi, Karim Zouaoui Boudjeltia, Badr Kaoui, Daman Kaur, Liam P Maguire, Paula L McClean, Niamh McCombe, João Luís de Miranda, Mihnea Alexandru Moisescu, Francesco Pappalardo, Annikka Polster, Girijesh Prasad, Damjana Rozman, Ioan Sacala, Jose M Sanchez-Bornot, Johannes A Schmid, Trevor Sharp, Jordi Solé-Casals, Vojtěch Spiwok, George M Spyrou, Egils Stalidzans, Blaž Stres, Tijana Sustersic, Ioannis Symeonidis, Paolo Tieri, Stephen Todd, Kristel Van Steen, Milena Veneva, Da-Hui Wang, Haiying Wang, Hui Wang, Steven Watterson, KongFatt Wong-Lin, Su Yang, Xin Zou, Harald H H W Schmidt","doi":"10.1089/nsm.2020.0003","DOIUrl":"10.1089/nsm.2020.0003","url":null,"abstract":"<p><p><b>Background:</b> Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. <b>Methods:</b> In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. <b>Results:</b> This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":"4 1","pages":"2-50"},"PeriodicalIF":0.0,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7919422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25435299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2020 Peer Reviewer Thank You","authors":"","doi":"10.1089/nsm.2021.29009.ack","DOIUrl":"https://doi.org/10.1089/nsm.2021.29009.ack","url":null,"abstract":"","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48846908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules.","authors":"Lucas A Gillenwater, Katherine A Pratte, Brian D Hobbs, Michael H Cho, Yonghua Zhuang, Eitan Halper-Stromberg, Charmion Cruickshank-Quinn, Nichole Reisdorph, Irina Petrache, Wassim W Labaki, Wanda K O'Neal, Victor E Ortega, Dean P Jones, Karan Uppal, Sean Jacobson, Gregory Michelotti, Christine H Wendt, Katerina J Kechris, Russell P Bowler","doi":"10.1089/nsm.2020.0009","DOIUrl":"10.1089/nsm.2020.0009","url":null,"abstract":"<p><p><b>Background:</b> Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. <b>Materials and Methods:</b> Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV₁]/forced vital capacity [FVC], and FEV₁ percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. <b>Results:</b> Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. <b>Conclusion:</b> A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":"3 1","pages":"159-181"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8109053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10733284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informatics Inference of Exercise-Induced Modulation of Brain Pathways Based on Cerebrospinal Fluid Micro-RNAs in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.","authors":"Vaishnavi Narayan,&nbsp;Narayan Shivapurkar,&nbsp;James N Baraniuk","doi":"10.1089/nsm.2019.0009","DOIUrl":"10.1089/nsm.2019.0009","url":null,"abstract":"<p><p><b>Introduction:</b> The post-exertional malaise of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) was modeled by comparing micro-RNA (miRNA) in cerebrospinal fluid from subjects who had no exercise versus submaximal exercise. <b>Materials and Methods:</b> Differentially expressed miRNAs were examined by informatics methods to predict potential targets and regulatory pathways affected by exercise. <b>Results:</b> miR-608, miR-328, miR-200a-5p, miR-93-3p, and miR-92a-3p had higher levels in subjects who rested overnight (nonexercise <i>n</i>=45) compared to subjects who had exercised before their lumbar punctures (<i>n</i>=15). The combination was examined in DIANA MiRpath v3.0, TarBase, Cytoscape, and Ingenuity software^® to select the intersection of target mRNAs. DIANA found 33 targets that may be elevated after exercise, including <i>TGFBR1</i>, <i>IGFR1</i>, and <i>CDC42</i>. Adhesion and adherens junctions were the most frequent pathways. Ingenuity selected seven targets that had complementary mechanistic pathways involving GNAQ, ADCY3, RAP1B, and PIK3R3. Potential target cells expressing high levels of these genes included choroid plexus, neurons, and microglia. <b>Conclusion:</b> The reduction of this combination of miRNAs in cerebrospinal fluid after exercise suggested upregulation of phosphoinositol signaling pathways and altered adhesion during the post-exertional malaise of ME/CFS. Clinical Trial Registration Nos.: NCT01291758 and NCT00810225.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":" ","pages":"142-158"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1089/nsm.2019.0009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38333820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CovMulNet19, Integrating Proteins, Diseases, Drugs, and Symptoms: A Network Medicine Approach to COVID-19.","authors":"Nina Verstraete, Giuseppe Jurman, Giulia Bertagnolli, Arsham Ghavasieh, Vera Pancaldi, Manlio De Domenico","doi":"10.1089/nsm.2020.0011","DOIUrl":"10.1089/nsm.2020.0011","url":null,"abstract":"<p><p><b>Introduction:</b> We introduce in this study CovMulNet19, a comprehensive COVID-19 network containing all available known interactions involving SARS-CoV-2 proteins, interacting-human proteins, diseases and symptoms that are related to these human proteins, and compounds that can potentially target them. <b>Materials and Methods:</b> Extensive network analysis methods, based on a bootstrap approach, allow us to prioritize a list of diseases that display a high similarity to COVID-19 and a list of drugs that could potentially be beneficial to treat patients. As a key feature of CovMulNet19, the inclusion of symptoms allows a deeper characterization of the disease pathology, representing a useful proxy for COVID-19-related molecular processes. <b>Results:</b> We recapitulate many of the known symptoms of the disease and we find the most similar diseases to COVID-19 reflect conditions that are risk factors in patients. In particular, the comparison between CovMulNet19 and randomized networks recovers many of the known associated comorbidities that are important risk factors for COVID-19 patients, through identified similarities with intestinal, hepatic, and neurological diseases as well as with respiratory conditions, in line with reported comorbidities. <b>Conclusion:</b> CovMulNet19 can be suitably used for network medicine analysis, as a valuable tool for exploring drug repurposing while accounting for the intervening multidimensional factors, from molecular interactions to symptoms.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":" ","pages":"130-141"},"PeriodicalIF":0.0,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38333819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple Sclerosis Atlas: A Molecular Map of Brain Lesion Stages in Progressive Multiple Sclerosis.","authors":"Tobias Frisch,&nbsp;Maria L Elkjaer,&nbsp;Richard Reynolds,&nbsp;Tanja Maria Michel,&nbsp;Tim Kacprowski,&nbsp;Mark Burton,&nbsp;Torben A Kruse,&nbsp;Mads Thomassen,&nbsp;Jan Baumbach,&nbsp;Zsolt Illes","doi":"10.1089/nsm.2020.0006","DOIUrl":"https://doi.org/10.1089/nsm.2020.0006","url":null,"abstract":"<p><p><b>Introduction:</b> Multiple sclerosis (MS) is a chronic disorder of the central nervous system with an untreatable late progressive phase. Molecular maps of different stages of brain lesion evolution in patients with progressive multiple sclerosis (PMS) are missing but critical for understanding disease development and to identify novel targets to halt progression. <b>Materials and Methods:</b> The MS Atlas database comprises comprehensive high-quality transcriptomic profiles of 98 white matter (WM) brain samples of different lesion types (normal-appearing WM [NAWM], active, chronic active, inactive, remyelinating) from ten progressive MS patients and 25 WM areas from five non-neurological diseased cases. <b>Results:</b> We introduce the first MS brain lesion atlas (msatlas.dk), developed to address the current challenges of understanding mechanisms driving the fate on a lesion basis. The MS Atlas gives means for testing research hypotheses, validating biomarkers and drug targets. It comes with a user-friendly web interface, and it fosters bioinformatic methods for <i>de novo</i> network enrichment to extract mechanistic markers for specific lesion types and pathway-based lesion type comparison. We describe examples of how the MS Atlas can be used to extract systems medicine signatures and demonstrate the interface of MS Atlas. <b>Conclusion:</b> This compendium of mechanistic PMS WM lesion profiles is an invaluable resource to fuel future MS research and a new basis for treatment development.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":" ","pages":"122-129"},"PeriodicalIF":0.0,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1089/nsm.2020.0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38400975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining Gene-Disease Associations with Single-Cell Gene Expression Data Provides Anatomy-Specific Subnetworks in Age-Related Macular Degeneration.","authors":"Philip J Luthert, Christina Kiel","doi":"10.1089/nsm.2020.0005","DOIUrl":"10.1089/nsm.2020.0005","url":null,"abstract":"<p><p><b>Background:</b> Age-related macular degeneration (AMD) is the most common cause of visual impairment in the developed world. Despite some treatment options for late AMD, there is no intervention that blocks early AMD proceeding to the late and blinding forms. This is partly due to the lack of precise drug targets, despite great advances in genetics, epidemiology, and protein-protein interaction (PPI) networks proposed to be driving the disease pathology. A systems approach to narrow down PPI networks to specific protein drug targets would provide new therapeutic options. <b>Materials and Methods:</b> In this study we analyzed single cell RNAseq (RNA sequencing) datasets of 17 cell types present in choroidal, retinal pigment epithelium (RPE), and neural retina (NR) tissues to explore if a more granular analysis incorporating different cell types exposes more specific pathways and relationships. Furthermore, we developed a novel and systematic gene ontology database (SysGO) to explore if a subcellular classification of processes will further enhance the understanding of the pathogenesis of this complex disorder and its comorbidities with other age-related diseases. <b>Results:</b> We found that 57% of the AMD (risk) genes are among the top 25% expressed genes in ∼1 of the 17 choroidal/RPE/NR cell types, and 9% were among the top 1% of expressed genes. Using SysGO, we identified an enrichment of AMD genes in cell membrane and extracellular anatomical locations, and we found both functional enrichments (e.g., cell adhesion) and cell types (e.g., fibroblasts, microglia) not previously associated with AMD pathogenesis. We reconstructed PPI networks among the top expressed AMD genes for all 17 choroidal/RPE/NR cell types, which provides molecular and anatomical definitions of AMD phenotypes that can guide therapeutic approaches to target this complex disease. <b>Conclusion:</b> We provide mechanism-based AMD endophenotypes that can be exploited <i>in vitro</i>, using computational models and for drug discovery/repurposing.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":" ","pages":"105-121"},"PeriodicalIF":0.0,"publicationDate":"2020-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38267770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine.","authors":"Blandine Comte, Jan Baumbach, Arriel Benis, José Basílio, Nataša Debeljak, Åsmund Flobak, Christian Franken, Nissim Harel, Feng He, Martin Kuiper, Juan Albino Méndez Pérez, Estelle Pujos-Guillot, Tadeja Režen, Damjana Rozman, Johannes A Schmid, Jeanesse Scerri, Paolo Tieri, Kristel Van Steen, Sona Vasudevan, Steven Watterson, Harald H H W Schmidt","doi":"10.1089/nsm.2020.0004","DOIUrl":"10.1089/nsm.2020.0004","url":null,"abstract":"<p><p><b>Introduction:</b> Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. <b>Methods:</b> In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. <b>Results:</b> The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. <b>Conclusion:</b> In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.</p>","PeriodicalId":74262,"journal":{"name":"Network and systems medicine","volume":" ","pages":"67-90"},"PeriodicalIF":0.0,"publicationDate":"2020-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38400976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}