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MoFlus: An Open-Source Android Software for Fluorescence-Based Point of Care

*Panji Wisnu Wirawan scopus  -  Department of Informatics, Faculty of Science and Mathematics, Diponegoro University, Indonesia
Adi Wibowo orcid scopus  -  Department of Informatics, Faculty of Science and Mathematics, Diponegoro University, Indonesia

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Abstract

High-sensitivity fluorescence-based tests are utilized to monitor various activities in life science research. These tests are specifically used as health monitoring tools to detect diseases. Fluorescence-based test facilities in rural areas and developing countries, however, remain limited. Point-of-care (POC) tests based on fluorescence detection have become a solution to the limitations of fluorescence-based tools in developing countries. POC software for smartphone cameras was generally developed for specific devices and tools, and it ability to select the desired region of interest (ROI) is limited. In this work, we developed Mobile Fluorescence Spectroscopy (MoFlus), an open-source Android software for camera-based POC. We mainly aimed to develop camera-based POC software that can be used for the dynamic selection of ROI; the number of samples; and the types of detection, color, data, and for communication with servers. MoFlus facilitated the use of touch screens and data given that it was developed on the basis of the SurfaceView library in Android and Javascript object notation applications. Moreover, the function and endurance of the app when used multiple times and with different numbers of images were tested.

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Keywords: Point of Care, Android, SurfaceView, Fluorescence, ROI

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  1. W. F. An, “Fluorescence-based assays,” in Cell-Based Assays for High-Throughput Screening, Springer, 2009, pp. 97–107
  2. A. Hatch et al., “A rapid diffusion immunoassay in a T-sensor,” Nat. Biotechnol., vol. 19, no. 5, p. 461, 2001
  3. A. Bourouis, A. Zerdazi, M. Feham, and A. Bouchachia, “M-health: Skin disease analysis system using smartphone’s camera,” in Procedia Computer Science, 2013, vol. 19, pp. 1116–1120, doi: 10.1016/j.procs.2013.06.157
  4. W. K. Tam and H. J. Lee, “Accurate shade image matching by using a smartphone camera,” J. Prosthodont. Res., vol. 61, no. 2, pp. 168–176, 2017, doi: 10.1016/j.jpor.2016.07.004
  5. B. Berg et al., “Cellphone-based hand-held microplate reader for point-of-care testing of enzyme-linked immunosorbent assays,” ACS Nano, vol. 9, no. 8, pp. 7857–7866, 2015
  6. A. Rahangdale and S. Raut, “Machine Learning Methods for Ranking,” Int. J. Softw. Eng. Knowl. Eng., vol. 29, no. 06, pp. 729–761, Jun. 2019, doi: 10.1142/S021819401930001X
  7. D. Kim et al., “Enzyme-Free Nucleic Acid Amplification Assay Using a Cellphone-Based Well Plate Fluorescence Reader,” Anal. Chem., vol. 90, no. 1, pp. 690–695, 2017
  8. A. Priye, S. W. Bird, Y. K. Light, C. S. Ball, O. A. Negrete, and R. J. Meagher, “A smartphone-based diagnostic platform for rapid detection of Zika, chikungunya, and dengue viruses,” Sci. Rep., vol. 7, p. 44778, 2017
  9. W. Chen et al., “Mobile platform for multiplexed detection and differentiation of disease-specific nucleic acid sequences, using microfluidic loop-mediated isothermal amplification and smartphone detection,” Anal. Chem., vol. 89, no. 21, pp. 11219–11226, 2017
  10. U. M. Jalal, G. J. Jin, and J. S. Shim, “Paper--Plastic Hybrid Microfluidic Device for Smartphone-Based Colorimetric Analysis of Urine,” Anal. Chem., vol. 89, no. 24, pp. 13160–13166, 2017
  11. J. Song et al., “Smartphone-Based Mobile Detection Platform for Molecular Diagnostics and Spatiotemporal Disease Mapping,” Anal. Chem., vol. 90, no. 7, pp. 4823–4831, 2018
  12. B. Lin et al., “Point-of-care testing for streptomycin based on aptamer recognizing and digital image colorimetry by smartphone,” Biosens. Bioelectron., vol. 100, pp. 482–489, 2018
  13. K. Chan, P.-Y. Wong, C. Parikh, and S. Wong, “Moving toward rapid and low-cost point-of-care molecular diagnostics with a repurposed 3D printer and RPA,” Anal. Biochem., vol. 545, pp. 4–12, 2018
  14. S. Akraa et al., “A smartphone-based point-of-care quantitative urinalysis device for chronic kidney disease patients,” J. Netw. Comput. Appl., vol. 115, pp. 59–69, 2018
  15. N. Chondros and M. Roussopoulos, “Developing IntegrityCatalog, a software system for managing integrity-related metadata in digital repositories,” Softw. Pract. Exp., vol. 48, no. 1, pp. 45–64, Jan. 2018, doi: 10.1002/spe.2515
  16. M. Ozkaya, “Do the informal & formal software modeling notations satisfy practitioners for software architecture modeling?,” Inf. Softw. Technol., vol. 95, pp. 15–33, Mar. 2018, doi: 10.1016/j.infsof.2017.10.008
  17. “Github.” https://github.com
  18. L. do Nascimento Vale and M. de Almeida Maia, “Key Classes in Object-Oriented Systems: Detection and Assessment,” Int. J. Softw. Eng. Knowl. Eng., vol. 29, no. 10, pp. 1439–1463, Oct. 2019, doi: 10.1142/S0218194019500451
  19. G. Barbaglia, S. Murzilli, and S. Cudini, “Definition of REST web services with JSON schema,” Softw. Pract. Exp., vol. 47, no. 6, pp. 907–920, Jun. 2017, doi: 10.1002/spe.2466
  20. M. A. Paredes-Valverde, G. Alor-Hernández, A. Rodríguez-González, R. Valencia-García, and E. Jiménez-Domingo, “A systematic review of tools, languages, and methodologies for mashup development,” Softw. Pract. Exp., vol. 45, no. 3, pp. 365–397, Mar. 2015, doi: 10.1002/spe.2233
  21. “JSON Schema Validator.” https://www.jsonschemavalidator.net/
  22. T. H. Noor, S. Zeadally, A. Alfazi, and Q. Z. Sheng, “Journal of Network and Computer Applications Mobile cloud computing : Challenges and future research directions,” J. Netw. Comput. Appl., vol. 115, no. January, pp. 70–85, 2018, doi: 10.1016/j.jnca.2018.04.018

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