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10.1016/j.envint.2020.106232 http://scihub22266oqcxt.onion/10.1016/j.envint.2020.106232 33223326!7831776!33223326     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Environ+Int 2021 ; 157 (ä): 106232 Nephropedia Template TP {{tp|p=33223326|t=2021. Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach |pdf=|usr=}}{{33223326}} gab.com Text Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach JO: Environ Int http://www.kidney.de/pget.php?&tw=1&pmid=33223326 #FOAvip BACKGROUND: Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19. OBJECTIVES: To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied. METHODS: As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis. RESULTS: We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising. CONCLUSIONS: Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction. Twit Text FOAVip Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach ????Environ Int http://www.kidney.de/pget.php?&tw=1&pmid=33223326 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33223326 #FOAvip English Wikipedia <ref>{{Cite pmid|33223326}}</ref> Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach #MMPMID33223326 Wu Q; Coumoul X; Grandjean P; Barouki R; Audouze K Environ Int 2021[Dec]; 157 (ä): 106232 PMID33223326show ga BACKGROUND: Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19. OBJECTIVES: To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied. METHODS: As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis. RESULTS: We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising. CONCLUSIONS: Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction. |*COVID-19[MESH] |*Endocrine Disruptors/toxicity[MESH] |Databases, Factual[MESH] |Humans[MESH] |SARS-CoV-2[MESH]Endocrine disrupting chemicals and COVID-19 relationships: A computati(epmc).pdf DeepDyve Pubget Overpricing