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CFD simulations for paper-based DNA amplification reaction (LAMP) of Mycobacterium tuberculosis—point-of-care diagnostic perspective

Authors :
Debayan Das
Pradipta Kumar Panigrahi
Source :
Medical & Biological Engineering & Computing. 58:271-289
Publication Year :
2019
Publisher :
Springer Science and Business Media LLC, 2019.

Abstract

Loop-mediated isothermal amplification or LAMP has been identified to be an efficient technology for point-of-care diagnostics. Paper-based LAMP technique has tremendous potential in replacing the existing tube-based technology as the manufacturing cost of a paper-based device is comparatively lower and easy-to-use. LAMP-based paper diagnostic device for Mycobacterium tuberculosis (MTB) detection is of extreme importance as it will help in early and rapid diagnosis of the affected patients. The fabrication of these devices requires assessment of design parameters on the extent of LAMP amplification reaction. Hence, CFD studies would be extremely beneficial from the design perspective. The current work presents an insight into the CFD simulations for LAMP amplification reaction on a porous paper membrane (nitrocellulose membrane). The convection-diffusion-reaction model is solved on a COMSOL Multiphysics 5.0 platform. Studies on effect of pore size, aspect ratio and initial DNA concentration on the extent of DNA amplification reaction have been carried out. The current paper-based technique is effective in detecting a minimum of 5 copies of DNA contrasting the previous semi-quantitative technique which demonstrated the detection of minimum 98 copies. Overall, the simulation results displayed almost 96% enhancement in the DNA amplification rate on paper membrane. Graphical abstract Graphical abstract for the computational study of DNA amplification reaction via LAMP technique on a porous paper membrane.

Details

ISSN :
17410444 and 01400118
Volume :
58
Database :
OpenAIRE
Journal :
Medical & Biological Engineering & Computing
Accession number :
edsair.doi.dedup.....0004e5476e8c6d0d34758b27f3789ec7