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Annual Review of Analytical Chemistry

Volume 11, 2018

Improving Lateral Flow Assay Performance Using Computational Modeling

Abstract

The performance, field utility, and low cost of lateral flow assays (LFAs) have driven a tremendous shift in global health care practices by enabling diagnostic testing in previously unserved settings. This success has motivated the continued improvement of LFAs through increasingly sophisticated materials and reagents. However, our mechanistic understanding of the underlying processes that drive the informed design of these systems has not received commensurate attention. Here, we review the principles underpinning LFAs and the historical evolution of theory to predict their performance. As this theory is integrated into computational models and becomes testable, the criteria for quantifying performance and validating predictive power are critical. The integration of computational design with LFA development offers a promising and coherent framework to choose from an increasing number of novel materials, techniques, and reagents to deliver the low-cost, high-fidelity assays of the future.

Keyword(s): computational modeling, lateral flow assay, optimization, reaction theory, transport theory
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2018-06-12
2024-04-25
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Literature Cited

  1. 1. Glob. Fund. 2017. Results Report 2017 Rep., Glob. Fund, Geneva. https://www.theglobalfund.org/media/6773/corporate_2017resultsreport_report_en.pdf
  2. 2.  Hsieh HV, Dantzler JL, Weigl BH 2017. Analytical tools to improve optimization procedures for lateral flow assays. Diagnostics 7:229
     [Google Scholar]
  3. 3.  Gasperino DJ, Huynh T, Weigl B 2017. Integrated system for the rapid, systematic development of lateral flow assays Presented at Int. Conf. Miniat. Syst. Chem. Life Sci., 21st Savannah, GA:
  4. 4.  Li H, Han D, Hegener MA, Pauletti GM, Steckl AJ 2017. Flow reproducibility of whole blood and other bodily fluids in simplified no reaction lateral flow assay devices. Biomicrofluidics 11:2024116
     [Google Scholar]
  5. 5.  Filipe V, Hawe A, Jiskoot W 2010. Critical evaluation of nanoparticle tracking analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates. Pharm. Res. 27:5796–810
     [Google Scholar]
  6. 6.  Battiato I, Tartakovsky DM 2011. Applicability regimes for macroscopic models of reactive transport in porous media. J. Contam. Hydrol. 120–121:C18–26
     [Google Scholar]
  7. 7.  Lund EW 1965. Guldberg and Waage and the law of mass action. J. Chem. Educ. 42:10548
     [Google Scholar]
  8. 8.  Lin S, Lee ASY, Lin C-C, Lee C-K 2006. Determination of binding constant and stoichiometry for antibody-antigen interaction with surface plasmon resonance. Curr. Proteom. 3:271–82
     [Google Scholar]
  9. 9.  Rhoden JJ, Dyas GL, Wroblewski VJ 2016. A modeling and experimental investigation of the effects of antigen density, binding affinity, and antigen expression ratio on bispecific antibody binding to cell surface targets. J. Biol. Chem. 291:2111337–47
     [Google Scholar]
  10. 10.  Kusnezow W, Syagailo Y V, Goychuk I, Hoheisel JD, Wild DG 2006. Antibody microarrays: the crucial impact of mass transport on assay kinetics and sensitivity. Expert Rev. Mol. Diagn. 6:1111–24
     [Google Scholar]
  11. 11.  Webster DM, Henry AH, Rees AR 1994. Antibody-antigen interactions. Curr. Opin. Struct. Biol. 4:1123–29
     [Google Scholar]
  12. 12.  Liang T, Robinson R, Houghtaling J, Fridley G, Ramsey SA, Fu E 2016. Investigation of reagent delivery formats in a multivalent malaria sandwich immunoassay and implications for assay performance. Anal. Chem. 88:42311–20
     [Google Scholar]
  13. 13.  Zhan L, Guo SZ, Song F, Gong Y, Xu F et al. 2017. The role of nanoparticle design in determining analytical performance of lateral flow immunoassays. Nano Lett 17:7207–12
     [Google Scholar]
  14. 14.  Brogioli D 2013. Violation of the mass-action law in dilute chemical systems. J. Chem. Phys. 139:18184102
     [Google Scholar]
  15. 15.  Logan JD 2001. Transport Modeling in Hydrogeochemical Systems 15 New York: Springer
  16. 16.  Skaug MJ, Wang L, Ding Y, Schwartz DK 2015. Hindered nanoparticle diffusion and void accessibility in a three-dimensional porous medium. ACS Nano 9:22148–56
     [Google Scholar]
  17. 17.  Babayekhorasani F, Dunstan DE, Krishnamoorti R, Conrad JC 2016. Nanoparticle diffusion in crowded and confined media. Soft Matter 12:408407–16
     [Google Scholar]
  18. 18.  Millington RJ, Quirk JP 1961. Permeability of porous solids. Trans. Faraday Soc. 57:1200–7
     [Google Scholar]
  19. 19.  Ahmad AL, Low SC, Shukor SRA, Fernando WJN, Ismail A 2010. Hindered diffusion in lateral flow nitrocellulose membrane: experimental and modeling studies. J. Membr. Sci. 357:1–2178–84
     [Google Scholar]
  20. 20.  Delgado JMPQ 2007. Longitudinal and transverse dispersion in porous media. Chem. Eng. Res. Des. 85:91245–52
     [Google Scholar]
  21. 21.  Boccardo G, Crevacore E, Sethi R, Icardi M 2017. A robust upscaling of the effective particle deposition rate in porous media. J. Contam. Hydrol. In press. https://doi.org/10.1016/j.jconhyd.2017.09.002
    [Crossref]
  22. 22.  Molnar IL, Johnson WP, Gerhard JI, Willson CS, O'Carroll DM 2015. Predicting colloid transport through saturated porous media: a critical review. Water Resour. Res. 51:6804–45
     [Google Scholar]
  23. 23.  Babakhani P, Bridge J, Doong RAN, Phenrat T 2017. Continuum-based models and concepts for the transport of nanoparticles in saturated porous media: a state-of-the-science review. Adv. Colloid Interface Sci. 246:75–104
     [Google Scholar]
  24. 24.  Saiers JE, Hornberger GM, Liang L 1994. First- and second-order kinetics approaches for modeling the transport of colloidal particles in porous media. Water Resour. Res. 30:92499–506
     [Google Scholar]
  25. 25.  Boccardo G, Marchisio DL, Sethi R 2014. Microscale simulation of particle deposition in porous media. J. Colloid Interface Sci. 417:227–37
     [Google Scholar]
  26. 26.  Berg HC, Purcell EM 1977. Physics of chemoreception. Biophys. J. 20:2193–219
     [Google Scholar]
  27. 27.  Zwanzig R, Szabo A 1991. Time dependent rate of diffusion-influenced ligand binding to receptors on cell surfaces. Biophys. J. 60:3671–78
     [Google Scholar]
  28. 28.  Erickson J, Goldstein B, Holowka D, Baird B 1987. The effect of receptor density on the forward rate constant for binding of ligands to cell surface receptors. Biophys. J. 52:4657–62
     [Google Scholar]
  29. 29.  Zhdanov VP Cho N-J 2016. Kinetics of the formation of a protein corona around nanoparticles. Math. Biosci. 282:82–90
     [Google Scholar]
  30. 30.  Sahneh FD, Scoglio C, Riviere J 2013. Dynamics of nanoparticle-protein corona complex formation: analytical results from population balance equations. PLOS ONE 8:5e64690
     [Google Scholar]
  31. 31.  Saha B, Evers TH, Prins MWJ 2014. How antibody surface coverage on nanoparticles determines the activity and kinetics of antigen capturing for biosensing. Anal. Chem. 86:168158–66
     [Google Scholar]
  32. 32.  Shao Q, Hall CK 2016. Protein adsorption on nanoparticles: model development using computer simulation. J. Phys. Condens. Matter 28:41414019
     [Google Scholar]
  33. 33.  Tavanti F, Pedone A, Menziani MC 2015. A closer look into the ubiquitin corona on gold nanoparticles by computational studies. New J. Chem. 39:2474–82
     [Google Scholar]
  34. 34.  Vilanova O, Mittag JJ, Kelly PM, Milani S, Dawson KA et al. 2016. Understanding the kinetics of protein-nanoparticle corona formation. ACS Nano 10:1210842–50
     [Google Scholar]
  35. 35.  Malaspina DC, Longo G, Szleifer I 2017. Behavior of ligand binding assays with crowded surfaces: Molecular model of antigen capture by antibody-conjugated nanoparticles. PLOS ONE 12:9e0185518
     [Google Scholar]
  36. 36.  Patra A, Ding T, Engudar G, Wang Y, Dykas MM et al. 2016. Component-specific analysis of plasma protein corona formation on gold nanoparticles using multiplexed surface plasmon resonance. Small 12:91174–82
     [Google Scholar]
  37. 37.  Adamczyk Z 2012. Modeling adsorption of colloids and proteins. Curr. Opin. Colloid Interface Sci. 17:3173–86
     [Google Scholar]
  38. 38.  Haun JB, Hammer DA 2008. Quantifying nanoparticle adhesion mediated by specific molecular interactions. Langmuir 24:168821–32
     [Google Scholar]
  39. 39.  Liu Y, Shah S, Tan J 2012. Computational modeling of nanoparticle targeted drug delivery. Rev. Nanosci. Nanotechnol. 1:166–83
     [Google Scholar]
  40. 40.  Uma B, Swaminathan TN, Radhakrishnan R, Eckmann DM, Ayyaswamy PS 2011. Nanoparticle Brownian motion and hydrodynamic interactions in the presence of flow fields. Phys. Fluids 23:7073602
     [Google Scholar]
  41. 41.  Taghavy A, Pennell KD, Abriola LM 2015. Modeling coupled nanoparticle aggregation and transport in porous media: a Lagrangian approach. J. Contam. Hydrol. 172:48–60
     [Google Scholar]
  42. 42.  Berson SA, Yalow RS 1959. Quantitative aspects of the reaction between insulin and insulin-binding antibody. J. Clin. Investig. 38:1996–2016
     [Google Scholar]
  43. 43.  Yalow RS, Berson SA 1960. Immunoassay of endogenous plasma insulin in man. J. Clin. Investig. 39:1157–75
     [Google Scholar]
  44. 44.  Rodbard D, Lewald JE 1970. Computer analysis of radioligand assay and radioimmunoassay data. Acta Endocrinol 147:79–103
     [Google Scholar]
  45. 45.  Rodbard D, Catt KJ 1972. Mathematical theory of radioligand assays: the kinetics of separation of bound from free. J. Steroid Biochem. 3:3255–73
     [Google Scholar]
  46. 46.  Rodbard D 1973. Mathematics of hormone-receptor interaction. Receptors for Reproductive Hormones BW O'Malley, AR Means 289–326 Boston, MA: Springer
     [Google Scholar]
  47. 47.  Stenberg M, Nygren H 1988. Kinetics of antigen-antibody reactions at solid-liquid interfaces. J. Immunol. Methods 113:13–15
     [Google Scholar]
  48. 48.  Paek SH, Schramm W 1991. Modeling of immunosensors under nonequilibrium conditions. I. Mathematic modeling of performance characteristics. Anal. Biochem. 196:2319–25
     [Google Scholar]
  49. 49.  Schramm W, Paek SH 1991. Modeling of immunosensors under nonequilibrium conditions. II. Experimental determination of performance characteristics. Anal. Biochem. 196:2326–36
     [Google Scholar]
  50. 50.  Sadana A, Sii D 1992. Binding kinetics of antigen by immobilized antibody: influence of reaction order and external diffusional limitations. Biosens. Bioelectron. 7:8559–68
     [Google Scholar]
  51. 51.  Sadana A, Madugula A 1993. Binding kinetics of antigen by immobilized antibody or of antibody by immobilized antigen: influence of lateral interactions and variable rate coefficients. Biotechnol. Prog. 9:3259–66
     [Google Scholar]
  52. 52.  Chen Z, Sadana A 1996. An analysis of antigen-antibody binding kinetics for biosensor applications utilized as a model system: influence of non-specific binding. Biophys. Chem. 57:2–3177–87
     [Google Scholar]
  53. 53.  Sadana A, Chen Z 1996. Influence of non-specific binding on antigen-antibody binding kinetics for biosensor applications. Biosens. Bioelectron. 11:l–217–33
     [Google Scholar]
  54. 54.  Bicskei Z, Ylander P, Hänninen P 2006. Calibration of bioaffinity assays using kinetic data. J. Biochem. Biophys. Methods 67:175–85
     [Google Scholar]
  55. 55.  Ylander PJ, Hänninen P 2010. Modelling of multi-component immunoassay kinetics: a new node-based method for simulation of complex assays. Biophys. Chem. 151:3105–10
     [Google Scholar]
  56. 56.  Ylander P 2012. Simulation methods in the modelling of bioaffinity assays PhD Thesis, Univ. Turku, Finl.
  57. 57.  Avella-Oliver M, Gimenez-Romero D, Morais S, González-Martínez MÁ, Bueno PR et al. 2013. INSEL: an in silico method for optimizing and exploring biorecognition assays. Chem. Commun. 49:9210868–70
     [Google Scholar]
  58. 58.  Lewin ER, Dasgupta A, Valdes R, Stickle DF 2017. Model analysis of bidirectional interference in two-stage labeled-ligand immunoassays. Clin. Biochem. 50:181188–97
     [Google Scholar]
  59. 59.  Sotnikov DV, Zherdev AV, Dzantiev BB 2017. Mathematical modeling of bioassays. Biochem 82:131744–66
     [Google Scholar]
  60. 60.  Alava M, Niskanen K 2006. The physics of paper. Reports Prog. Phys. 69:3669–723
     [Google Scholar]
  61. 61.  Lucas R 1918. Ueber das Zeitgesetz des kapillaren Aufstiegs von Flüssigkeiten. Kolloid-Z 23:115–22
     [Google Scholar]
  62. 62.  Washburn EW 1921. The dynamics of capillary flow. Phys. Rev. 17:3273–83
     [Google Scholar]
  63. 63.  Schoelkopf J, Gane PAC, Ridgway CJ, Matthews GP 2002. Practical observation of deviation from Lucas-Washburn scaling in porous media. Colloids Surf. A Physicochem. Eng. Asp. 206:1–3445–54
     [Google Scholar]
  64. 64.  Hyväluoma J, Raiskinmäki P, Jäsberg A, Koponen A, Kataja M, Timonen J 2006. Simulation of liquid penetration in paper. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 73:036705
     [Google Scholar]
  65. 65.  Toley BJ, McKenzie B, Liang T, Buser JR, Yager P, Fu E 2013. Tunable-delay shunts for paper micro fluidic devices. Anal. Chem. 85:2311545–52
     [Google Scholar]
  66. 66.  Perez-Cruz A, Stiharu I, Dominguez-Gonzalez A 2017. Two-dimensional model of imbibition into paper-based networks using Richards’ equation. Microfluid. Nanofluid. 21:598
     [Google Scholar]
  67. 67.  Masoodi R 2010. Modeling imbibation of liquids into rigid and swelling porous media PhD Thesis, Univ. Wisc. Milwaukee:
  68. 68.  Masoodi R, Pillai KM 2012. A general formula for capillary suction-pressure in porous media. J. Porous Media 15:8775–83
     [Google Scholar]
  69. 69.  Masoodi R, Pillai KM 2012. Wicking in Porous Materials: Traditional and Modern Modeling Approaches Boca Raton, FL: CRC Press
  70. 70.  Masoodi R, Tan H, Pillai KM 2011. Darcy's law-based numerical simulation for modeling 3D liquid absorption into porous wicks. AIChE J 57:51132–43
     [Google Scholar]
  71. 71.  Walji N 2015. Characterization of fluid flow in paper-based microfluidic devices Master's Thesis, Univ. Ont. Inst. Technol., Oshawa, Can.
  72. 72.  Mendez S, Fenton EM, Gallegos GR, Petsev DN, Sibbett SS et al. 2010. Imbibition in porous membranes of complex shape: quasi-stationary flow in thin rectangular segments. Langmuir 26:21380–85
     [Google Scholar]
  73. 73.  Elizalde E, Urteaga R, Berli CLA 2015. Rational design of capillary-driven flows for paper-based microfluidics. Lab Chip 15:102173–80
     [Google Scholar]
  74. 74.  Cummins BM, Chinthapatla R, Ligler FS, Walker GM 2017. Time-dependent model for fluid flow in porous materials with multiple pore sizes. Anal. Chem. 89:84377–81
     [Google Scholar]
  75. 75.  Buser JR 2016. Heat, Fluid, and Sample Control in Point-of-Care Diagnostics PhD Thesis, Univ. Wash.
  76. 76.  Krishnamoorthy S, Makhijani VB, Lei M, Giridharan MG, Tisone T et al. Computational studies of membrane-based test formats. Tech. Proc. 2000 Int. Conf. Model. Simul. Microsyst590–93
     [Google Scholar]
  77. 77.  Qian S, Bau HH 2003. A mathematical model of lateral flow bioreactions applied to sandwich assays. Anal. Biochem. 322:189–98
     [Google Scholar]
  78. 78.  Qian S 2004. Analysis of lateral flow biodetectors: competitive format analysis of lateral flow bio-detectors. Anal. Biochem. 326:2211–24
     [Google Scholar]
  79. 79.  Ragavendar MS, Anmol CM 2012. A mathematical model to predict the optimal test line location and sample volume for lateral flow immunoassays Presented at Annu. Int. Conf. IEEE Eng. Med. Biol. Soc San Diego, CA:
  80. 80.  Mosley GL, Nguyen P, Wu BM, Kamei DT 2016. Development of quantitative radioactive methodologies on paper to determine important lateral-flow immunoassay parameters. Lab Chip 16:152871–81
     [Google Scholar]
  81. 81.  Liu Z, Hu J, Li A, Feng S, Qu Z, Xu F 2017. The effect of report particle properties on lateral flow assays: a mathematical model. Sens. Actuators B Chem. 248:699–707
     [Google Scholar]
  82. 82.  Schaumburg F, Kler PA, Berli CLA 2018. Numerical prototyping of lateral flow biosensors. Sens. Actuators B Chem. 259:1099–1107
     [Google Scholar]
  83. 83.  Berli CLA, Kler PA 2016. A quantitative model for lateral flow assays. Microfluid. Nanofluid. 20:7104
     [Google Scholar]
  84. 84.  Sotnikov DV, Zherdev AV, Dzantiev BB 2017. Mathematical model of serodiagnostic immunochromatographic assay. Anal. Chem. 89:84419–27
     [Google Scholar]
  85. 85.  Sotnikov DV, Zherdev AV, Dzantiev BB 2018. Theoretical and experimental comparison of different formats of immunochromatographic serodiagnostics. Sensors 18:136
     [Google Scholar]
  86. 86.  Zeng N, Wang Z, Li Y, Du M, Liu X 2011. Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering. IEEE Trans. Biomed. Eng. 58:71959–66
     [Google Scholar]
  87. 87.  Zeng N, Wang Z, Li Y, Du M, Liu X 2012. Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach. IEEE Trans. Nanotechnol. 11:2321–27
     [Google Scholar]
  88. 88.  Zeng N, Wang Z, Li Y, Du M, Liu X 2012. A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models. IEEE/ACM Trans. Comput. Biol. Bioinf. 9:2321–29
     [Google Scholar]
  89. 89.  Zeng N, Wang Z, Li Y, Du M, Cao J, Liu X 2013. Time series modeling of nano-gold immunochromatographic assay via expectation maximization algorithm. IEEE Trans. Biomed. Eng. 60:123418–24
     [Google Scholar]
  90. 90.  Zeng N, Hung YS, Li Y, Du M 2014. A novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay. Expert Syst. Appl. 41:1708–15
     [Google Scholar]
  91. 91.  Zeng N, Wang Z, Zhang H, Alsaadi FE 2016. A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay. Cognit. Comput. 8:2143–52
     [Google Scholar]
  92. 92.  Zeng N, Wang Z, Zhang H 2016. Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter. Sci. China Inf. Sci. 59:112204
     [Google Scholar]
  93. 93. WHO (World Health Organ.). 2017. Malaria rapid diagnostic test performance: results of WHO product testing of malaria RDTs: round 7 (2016–2017) Rep WHO, Geneva:
  94. 94.  Bell D, Fleurent AE, Hegg MC, Boomgard JD, McConnico CC 2016. Development of new malaria diagnostics: matching performance and need. Malar. J. 15:406
     [Google Scholar]
  95. 95.  Piper RC, Buchanan I, Choi YH, Makler MT 2011. Opportunities for improving pLDH-based malaria diagnostic tests. Malar. J. 10:213
     [Google Scholar]
  96. 96.  Wang J, Johannsmann D, Hollas M, Thom V 2006. Visualization of capture line protein binding in nitrocellulose diagnostic membranes. Desalination 199:1–3232–33
     [Google Scholar]
  97. 97.  Powell MJD 2009. The BOBYQA algorithm for bound constrained optimization without derivatives Work. Pap., Univ Cambridge, UK:
  98. 98.  Markwalter CF, Jang IK, Burton RA, Domingo GJ, Wright DW 2017. Biolayer interferometry predicts ELISA performance of monoclonal antibody pairs for Plasmodium falciparum histidine-rich protein 2. Anal. Biochem. 534:10–13
     [Google Scholar]
  99. 99.  Li J, Duan H, Xu P, Huang X, Xiong Y 2016. Effect of different-sized spherical gold nanoparticles grown layer by layer on the sensitivity of an immunochromatographic assay. RSC Adv 6:3126178–85
     [Google Scholar]
  100. 100.  Rasmussen A, Mavriplis C, Zaghloul ME, Mikulchenko O, Mayaram K 2001. Simulation and optimization of a microfluidic flow sensor. Sens. Actuators A Phys. 88:2121–32
     [Google Scholar]
  101. 101.  Kim DR, Zheng X 2008. Numerical characterization and optimization of the microfluidics for nanowire biosensors. Nano Lett 8:103233–37
     [Google Scholar]
  102. 102.  Feuz L, Jönsson P, Jonsson MP, Höök F 2010. Improving the limit of detection of nanoscale sensors by directed binding to high-sensitivity areas. ACS Nano 4:42167–77
     [Google Scholar]
  103. 103.  Yang K, Wu J 2010. Numerical study of in situ preconcentration for rapid and sensitive nanoparticle detection. Biomicrofluidics 4:3034106
     [Google Scholar]
  104. 104.  Selmi M, Gazzah MH, Belmabrouk H 2017. Optimization of microfluidic biosensor efficiency by means of fluid flow engineering. Sci. Rep. 7:15721
     [Google Scholar]
  105. 105.  Schmidt MD, Vallabhajosyula RR, Jenkins JW, Hood JE, Soni AS et al. 2011. Automated refinement and inference of analytical models for metabolic networks. Phys. Biol. 8:555011
     [Google Scholar]
  106. 106.  Brunton SL, Proctor JL, Kutz JN 2016. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. PNAS 113:153932–37
     [Google Scholar]
  107. 107.  Zhang T, Murphy M, Yu H, Huh C, Bryant SL 2016. Mechanistic model for nanoparticle retention in porous media. Transp. Porous Media 115:2387–406
     [Google Scholar]
  108. 108.  Molnar IL, Sanematsu PC, Gerhard JI, Willson CS, O'Carroll DM 2016. Quantified pore-scale nanoparticle transport in porous media and the implications for colloid filtration theory. Langmuir 32:317841–53
     [Google Scholar]
  109. 109.  Nelson KE, Ginn TR 2011. New collector efficiency equation for colloid filtration in both natural and engineered flow conditions. Water Resour. Res. 47:5W05543
     [Google Scholar]
  110. 110.  Taranova NA, Urusov AE, Sadykhov EG, Zherdev AV, Dzantiev BB 2017. Bifunctional gold nanoparticles as an agglomeration-enhancing tool for highly sensitive lateral flow tests: a case study with procalcitonin. Microchim. Acta 184:104189–95
     [Google Scholar]
  111. 111.  Grant BD, Smith CA, Karvonen K, Richards-Kortum R 2016. Highly sensitive two-dimensional paper network incorporating biotin-streptavidin for the detection of malaria. Anal. Chem. 88:52553–57
     [Google Scholar]
  112. 112.  Moghadam BY, Connelly KT, Posner JD 2015. Two orders of magnitude improvement in detection limit of lateral flow assays using isotachophoresis. Anal. Chem. 87:21009–17
     [Google Scholar]
  113. 113.  Mosley GL, Pereira DY, Han Y, Lee SY, Wu CM et al. 2017. Improved lateral-flow immunoassays for chlamydia and immunoglobulin M by sequential rehydration of two-phase system components within a paper-based diagnostic. Microchim. Acta 184:104055–64
     [Google Scholar]
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Improving Lateral Flow Assay Performance Using Computational Modeling
Annual Review of Analytical Chemistry 11, 219 (2018); https://doi.org/10.1146/annurev-anchem-061417-125737
/content/journals/10.1146/annurev-anchem-061417-125737
/content/journals/10.1146/annurev-anchem-061417-125737
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