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Liquid Chromatography Microfluidics for Detection and Quantification of Urine Albiumin using Linear Regression Method

Published:20 July 2021Publication History

ABSTRACT

Nearly a hundred per million of the Filipino population is diagnosed with Chronic Kidney Disease (CKD). The early stage of CKD has no symptoms and can only be discovered once the patient undergoes urinalysis. Over the years, different methods were discovered and used for the quantification of the urinary albumin such as the immunochemical assays where most of these methods require large machinery that has a high cost in maintenance and resources, and a dipstick test which is yet to be proven and is still debated as a reliable method in detecting early stages of microalbuminuria. This research study involves the use of the liquid chromatography concept in microfluidic instruments with biosensor as a means of separation and detection respectively, and linear regression to quantify human urinary albumin. The researchers’ main objective was to create a miniature system that quantifies and detect patients’ urinary albumin while reducing the amount of volume used per 5 test samples. For this study, 30 urine samples of unknown albumin concentrations were tested using VITROS Analyzer and the microfluidic system for comparison. Based on the data shared by both methods, the actual vs. predicted regression were able to create a positive linear relationship with an R2 of 0.9995 and a linear equation of y = 1.09x + 0.07, indicating that the predicted values and actual values are approximately equal. Furthermore, the microfluidic instrument uses 75% less in total volume – sample and reagents combined, compared to the VITROS Analyzer per 5 test samples.

References

  1. Ahmad, M., Tundjungsari, V., Widianti, D., Amalia, P., Rachmawati, A.U., (2017). “Diagnostic decision support system of chronic kidney disease using support vector machine”. 2017 Second International Conference on Informatics and Computing (ICIC), doi: 10.1109/IAC.2017.8280576Google ScholarGoogle ScholarCross RefCross Ref
  2. Wibawa, H.A., Malik, I., Bahtiar, N., (2018). “Evaluation of Kernel-Based Extreme Learning Machine Performance for Prediction of Chronic Kidney Disease”. 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS), doi: 10.1109/ICICOS.2018.8621762Google ScholarGoogle ScholarCross RefCross Ref
  3. Levey, A.S., Coresh, J., (2011), “Chronic kidney disease” Lancet 2012, Volume 379, ISSUE 9811, p. 165-180. doi:10.1016/S0140-6736(11)60178-5Google ScholarGoogle ScholarCross RefCross Ref
  4. Noche, K. J. B., Villaverde, J. F. and Lazaro, J. "Portable non-invasive blood pressure measurement using pulse transmit time," 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Manila, 2017, pp. 1-4.Google ScholarGoogle Scholar
  5. Coskun, A.F., Nagi, R., Sadeghi, K., Phillips, S., Ozcan, A., (2013). “Albumin testing in urine using a smart-phone”. Lab Chip, 2013,13, 4231-4238. doi: 10.1039/C3LC50785HGoogle ScholarGoogle ScholarCross RefCross Ref
  6. Koroshi, A., (2007). “Microalbuminuria, is it so important?”. Hippokratia. 2007 Jul-Sep; 11(3): 105–107.Google ScholarGoogle Scholar
  7. Park, Ji & Baek, Hyunjeong & Kim, Bo & Jung, Hae Hyuk. (2017). Comparison of urine dipstick and albumin: Creatinine ratio for chronic kidney disease screening: A population-based study. PLOS ONE. 12. e0171106. doi: 10.1371/journal.pone.0171106. eCollection 2017.Google ScholarGoogle ScholarCross RefCross Ref
  8. Zamanzad, B. (2009). Accuracy of dipstick urinalysis as a screening method for detection of glucose, protein, nitrites and blood. Eastern Mediterranean Health Journal, 15 5, 1323-8.Google ScholarGoogle Scholar
  9. Gangaram, R & Ojwang, P & Moodley, Jack & Maharaj, Dushyant. (2005). The Accuracy of Urine Dipsticks as a Screening Test for Proteinuria in Hypertensive Disorders of Pregnancy. Hypertension in pregnancy : official journal of the International Society for the Study of Hypertension in Pregnancy. 24. 117-23. 10.1081/PRG-200059849.Google ScholarGoogle Scholar
  10. Nader R., Karen G., Lawrence M.S., Immunoturbidimetry: An attractive technique for the determination of urinary albumin and transferrin, Clinical Biochemistry, Volume 20, Issue 3, p. 179-181, ISSN 0009-9120, doi:10.1016/S0009-9120(87)80117-0.Google ScholarGoogle ScholarCross RefCross Ref
  11. Laiwattanapaisal, W., Songjaroen, T., Daniels, T., Lomas, T., Sappat, A., Tuantranont, A., (2009). On-Chip Immunoassay for Determination of Urinary Albumin. Sensors (Basel). 2009; 9(12): 10066–10079. doi: 10.3390/s91210066Google ScholarGoogle ScholarCross RefCross Ref
  12. Chatziharalambous, D., Lygirou, V., Latosinska, A., Stravodimos, K., Vlahou, A., Jankowski, V., Zoidakis, J. (2016). Analytical Performance of ELISA Assays in Urine: One More Bottleneck towards Biomarker Validation and Clinical Implementation. PloS one. doi: 11. e0149471. 10.1371/journal.pone.0149471.10Google ScholarGoogle Scholar
  13. Klapkova, E., Fortova, M., Richard, P., Moravcova, L., Kotaska, K. (2016). Determination of Urine Albumin by New Simple HighPerformance Liquid Chromatography Method. Journal of clinical laboratory analysis. 30. doi: 10.1002/jcla.22007.Google ScholarGoogle ScholarCross RefCross Ref
  14. Seegmiller, J. C., Sviridov, D., Larson, T. S., Borland, T. M., Hortin, G. L., & Lieske, J. C. (2009). Comparison of Urinary Albumin Quantification by Immunoturbidimetry, Competitive Immunoassay, and Protein-Cleavage Liquid Chromatography-Tandem Mass Spectrometry. Clinical Chemistry, 55(11), 1991–1994. doi:10.1373/clinchem.2009.129833Google ScholarGoogle ScholarCross RefCross Ref
  15. Chan, O. T. M., & Herold, D. A. (2009). Chip Electrophoresis as a Method for Quantifying Total Albumin in Cerebrospinal Fluid. JALA: Journal of the Association for Laboratory Automation, 14(1), 6–11. doi: 10.1016/j.jala.2008.05.003Google ScholarGoogle ScholarCross RefCross Ref
  16. Ketha, H., & Singh, R. J. (2016). Quantitation of albumin in urine by liquid chromatography tandem mass spectrometry. In Methods in Molecular Biology (Vol. 1378, pp. 31-36). (Methods in Molecular Biology; Vol. 1378). Humana Press Inc.. doi: 10.1007/978-1-4939-3182-8_4Google ScholarGoogle ScholarCross RefCross Ref
  17. Gerhardt, R.F., Peretzki, A.J., Piendl, S.K., & Belder, D. (2017). Seamless Combination of High-Pressure Chip-HPLC and Droplet Microfluidics on an Integrated Microfluidic Glass Chip. Analytical chemistry, 89 23, 13030-13037 .Google ScholarGoogle Scholar
  18. Strehlitz B, Nikolaus N, Stoltenburg R. (2008). Protein Detection with Aptamer Biosensors. Sensors. 2008; 8(7):4296-4307.Google ScholarGoogle Scholar
  19. Czaplicki, S., (2014). Chromatography in Bioactivity Analysis of Compounds, yColumn Chromatography, IntechOpen, doi: 10.5772/55620Google ScholarGoogle ScholarCross RefCross Ref
  20. Reichmuth, D., Shepodd, T., Kirby, B. (2005). Microchip HPLC of Peptides and Proteins. Analytical chemistry. 77. 2997-3000. 10.1021/ac048358r.Google ScholarGoogle Scholar
  21. Cruz, F. R. G., Magsipoc, C. M., Alinea, F. E. B.,Baronia, M. E. P., Jumahadi, M. M., Garcia, R. G., and Chung, W., "Application specific integrated circuit (ASIC) for Ion Sensitive Field Effect Transistor (ISFET) L-Asparagine biosensor," 2016 IEEE Region 10 Conference (TENCON), Singapore, 2016, pp. 2698-2702. doi: 10.1109/TENCON.2016.7848529Google ScholarGoogle ScholarCross RefCross Ref
  22. De Los Reyes, A. M. M., Reyes, A. C. A., Torres, J. L., Padilla, D. A and Villaverde, J. F., "Detection of Aedes Aegypti mosquito by digital image processing techniques and support vector machine," 2016 IEEE Region 10 Conference (TENCON), Singapore, 2016, pp. 2342-2345. doi: 10.1109/TENCON.2016.7848448Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Other conferences
    ICBET '21: Proceedings of the 2021 11th International Conference on Biomedical Engineering and Technology
    March 2021
    200 pages
    ISBN:9781450387897
    DOI:10.1145/3460238

    Copyright © 2021 ACM

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    Publication History

    • Published: 20 July 2021

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