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10.1016/j.bios.2020.112716 http://scihub22266oqcxt.onion/10.1016/j.bios.2020.112716 33068880!7550051!33068880     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 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       Biosens+Bioelectron 2021 ; 171 (ä): 112716 Nephropedia Template TP {{tp|p=33068880|t=2021. A computational simulation platform for designing real-time monitoring systems with application to COVID-19 |pdf=|usr=}}{{33068880}} gab.com Text A computational simulation platform for designing real-time monitoring systems with application to COVID-19 JO: Biosens Bioelectron http://www.kidney.de/pget.php?&tw=1&pmid=33068880 #FOAvip With the aim of contributing to the fight against the coronavirus disease 2019 (COVID-19), numerous strategies have been proposed. While developing an effective vaccine can take months up to years, detection of infected patients seems like one of the best ideas for controlling the situation. The role of biosensors in containing highly pathogenic viruses, saving lives and economy is evident. A new competitive numerical platform specifically for designing microfluidic-integrated biosensors is developed and presented in this work. Properties of the biosensor, sample, buffer fluid and even the microfluidic channel can be modified in this model. This feature provides the scientific community with the ability to design a specific biosensor for requested point-of-care (POC) applications. First, the validation of the presented numerical platform against experimental data and then results and discussion, highlighting the important role of the design parameters on the performance of the biosensor is presented. For the latter, the baseline case has been set on the previous studies on the biosensors suitable for SARS-CoV, which has the highest similarity to the 2019 nCoV. Subsequently, the effects of concentration of the targeted molecules in the sample, installation position and properties of the biosensor on its performance were investigated in 11 case studies. The presented numerical framework provides an insight into understanding of the virus reaction in the design process of the biosensor and enhances our preparation for any future outbreaks. Furthermore, the integration of biosensors with different devices for accelerating the process of defeating the pandemic is proposed. Twit Text FOAVip A computational simulation platform for designing real-time monitoring systems with application to COVID-19 ????Biosens Bioelectron http://www.kidney.de/pget.php?&tw=1&pmid=33068880 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33068880 #FOAvip English Wikipedia <ref>{{Cite pmid|33068880}}</ref> A computational simulation platform for designing real-time monitoring systems with application to COVID-19 #MMPMID33068880 Shahbazi F; Jabbari M; Esfahani MN; Keshmiri A Biosens Bioelectron 2021[Jan]; 171 (ä): 112716 PMID33068880show ga With the aim of contributing to the fight against the coronavirus disease 2019 (COVID-19), numerous strategies have been proposed. While developing an effective vaccine can take months up to years, detection of infected patients seems like one of the best ideas for controlling the situation. The role of biosensors in containing highly pathogenic viruses, saving lives and economy is evident. A new competitive numerical platform specifically for designing microfluidic-integrated biosensors is developed and presented in this work. Properties of the biosensor, sample, buffer fluid and even the microfluidic channel can be modified in this model. This feature provides the scientific community with the ability to design a specific biosensor for requested point-of-care (POC) applications. First, the validation of the presented numerical platform against experimental data and then results and discussion, highlighting the important role of the design parameters on the performance of the biosensor is presented. For the latter, the baseline case has been set on the previous studies on the biosensors suitable for SARS-CoV, which has the highest similarity to the 2019 nCoV. Subsequently, the effects of concentration of the targeted molecules in the sample, installation position and properties of the biosensor on its performance were investigated in 11 case studies. The presented numerical framework provides an insight into understanding of the virus reaction in the design process of the biosensor and enhances our preparation for any future outbreaks. Furthermore, the integration of biosensors with different devices for accelerating the process of defeating the pandemic is proposed. |Algorithms[MESH] |Betacoronavirus/*isolation & purification[MESH] |Biosensing Techniques[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques[MESH] |Computer Simulation[MESH] |Computer Systems[MESH] |Coronavirus Infections/*diagnosis[MESH] |Humans[MESH] |Hydrodynamics[MESH] |Microfluidic Analytical Techniques[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis[MESH] |Point-of-Care Systems[MESH]A computational simulation platform for designing real-time monitoring(epmc).pdf DeepDyve Pubget Overpricing