Issue

Vol. 17 (2025)

Intelligent Point-of-Care Biosensing Platform Based on Luminescent Nanoparticles and Microfluidic Biochip with Machine Vision Algorithm Analysis

https://doi.org/10.1007/s40820-025-01745-w
Yuan Liu
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, People’s Republic of China
Xinyue Lao
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, People’s Republic of China
Man‑Chung Wong
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, People’s Republic of China
Menglin Song
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, People’s Republic of China
Yifei Zhao
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, People’s Republic of China
Yingjin Ma
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, People’s Republic of China
Qianqian Bai
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, People’s Republic of China
Jianhua Hao
Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, People’s Republic of China

Corresponding Author: Jianhua Hao

jh.hao@polyu.edu.hk

Nano-Micro Letters, Vol. 17 (2025), Article Number: 215

Abstract

Realizing the point-of-care tumor markers biodetection with good convenience and high sensitivity possesses great significance for prompting cancer monitoring and screening in biomedical study field. Herein, the quantum dots luminescence and microfluidic biochip with machine vision algorithm-based intelligent biosensing platform have been designed and manufactured for point-of-care tumor markers diagnostics. The employed quantum dots with excellent photoluminescent performance are modified with specific antibody as the optical labeling agents for the designed sandwich structure immunoassay. The corresponding biosensing investigations of the designed biodetection platform illustrate several advantages involving high sensitivity (~ 0.021 ng mL−1), outstanding accessibility, and great integrability. Moreover, related test results of human-sourced artificial saliva samples demonstrate better detection capabilities compared with commercially utilized rapid test strips. Combining these infusive abilities, our elaborate biosensing platform is expected to exhibit potential applications for the future point-of-care tumor markers diagnostic area.


Highlights:
1 A novel intelligent biosensing platform consisting of quantum dots luminescence and biochip with machine vision algorithm is proposed for point-of-care carcinoembryonic antigen (CEA) protein diagnostics.
2 The designed diagnostic platform possesses outstanding detection limitation of approximately 0.021 ng mL−1 of CEA concentration compared with some commercial biodetection devices of lateral flow assay strips.
3 The utilization of machine vision algorithm improves the detection features of portability and integration, which expands the potential of point-of-care biosensing applications.

Keywords

Point-of-care Luminescent nanoparticles Biochip Machine vision Biosensing
Liu, Yuan, Xinyue Lao, Man‑Chung Wong, Menglin Song, Yifei Zhao, Yingjin Ma, Qianqian Bai, and Jianhua Hao. 2025. “Intelligent Point-of-Care Biosensing Platform Based on Luminescent Nanoparticles and Microfluidic Biochip With Machine Vision Algorithm Analysis”. Nano-Micro Letters 17 (April):215. https://doi.org/10.1007/s40820-025-01745-w.
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. N.D. Huntington, J. Cursons, J. Rautela, The cancer-natural killer cell immunity cycle. Nat. Rev. Cancer 20(8), 437–454 (2020). https://doi.org/10.1038/s41568-020-0272-z
  2. I. Vitale, E. Shema, S. Loi, L. Galluzzi, Intratumoral heterogeneity in cancer progression and response to immunotherapy. Nat. Med. 27(2), 212–224 (2021). https://doi.org/10.1038/s41591-021-01233-9
  3. J.E. Visvader, Cells of origin in cancer. Nature 469(7330), 314–322 (2011). https://doi.org/10.1038/nature09781
  4. F. Bray, M. Laversanne, H. Sung, J. Ferlay, R.L. Siegel et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74(3), 229–263 (2024). https://doi.org/10.3322/caac.21834
  5. C. Gridelli, A. Rossi, D.P. Carbone, J. Guarize, N. Karachaliou et al., Non-small-cell lung cancer. Nat. Rev. Dis. Primers. 1, 15009 (2015). https://doi.org/10.1038/nrdp.2015.9
  6. J.D. Mizrahi, R. Surana, J.W. Valle, R.T. Shroff, Pancreatic cancer. Lancet 395(10242), 2008–2020 (2020). https://doi.org/10.1016/s0140-6736(20)30974-0
  7. M.J. Duffy, C. Sturgeon, R. Lamerz, C. Haglund, V.L. Holubec et al., Tumor markers in pancreatic cancer: a European group on tumor markers (EGTM) status report. Ann. Oncol. 21(3), 441–447 (2010). https://doi.org/10.1093/annonc/mdp332
  8. M.J. Leveridge, P.J. Bostrom, G. Koulouris, A. Finelli, N. Lawrentschuk, Imaging renal cell carcinoma with ultrasonography. CT and MRI. Nat. Rev. Urol. 7(6), 311–325 (2010). https://doi.org/10.1038/nrurol.2010.63
  9. C. Bouzigues, T. Gacoin, A. Alexandrou, Biological applications of rare-earth based nanops. ACS Nano 5(11), 8488–8505 (2011). https://doi.org/10.1021/nn202378b
  10. A. Jain, P.G.J. Fournier, V. Mendoza-Lavaniegos, P. Sengar, F.M. Guerra-Olvera et al., Functionalized rare earth-doped nanops for breast cancer nanodiagnostic using fluorescence and CT imaging. J. Nanobiotechnology 16(1), 26 (2018). https://doi.org/10.1186/s12951-018-0359-9
  11. F. Cheng, L. Su, C. Qian, Circulating tumor DNA: a promising biomarker in the liquid biopsy of cancer. Oncotarget 7(30), 48832–48841 (2016). https://doi.org/10.18632/oncotarget.9453
  12. R. Lubin, G. Zalcman, L. Bouchet, J. Trédanel, Y. Legros et al., Serum p53 antibodies as early markers of lung cancer. Nat. Med. 1(7), 701–702 (1995). https://doi.org/10.1038/nm0795-701
  13. N. Yonet-Tanyeri, B.Z. Ahlmark, S.R. Little, Advances in multiplexed paper-based analytical devices for cancer diagnosis: a review of technological developments. Adv. Mater. Technol. 6(8), 2001138 (2021). https://doi.org/10.1002/admt.202001138
  14. E.M. Beltrami, I.T. Williams, C.N. Shapiro, M.E. Chamberland, Risk and management of blood-borne infections in health care workers. Clin. Microbiol. Rev. 13(3), 385–407 (2000). https://doi.org/10.1128/CMR.13.3.385
  15. Z. Yaari, Y. Yang, E. Apfelbaum, C. Cupo, A.H. Settle et al., A perception-based nanosensor platform to detect cancer biomarkers. Sci. Adv. 7(47), eaj0852 (2021). https://doi.org/10.1126/sciadv.abj0852
  16. N. Kumar, V.S. Gowri, R. Khan, P. Ranjan, M.A. Sadique, S. Yadav et al., Efficiency of nanomaterials for electrochemical diagnostics based point-of-care detection of non-invasive oral cancer biomarkers. Adv. Mater. Lett. 12(8), 1–20 (2021). https://doi.org/10.5185/amlett.2021.081651
  17. W. Li, H. Wang, Z. Zhao, H. Gao, C. Liu et al., Emerging nanotechnologies for liquid biopsy: the detection of circulating tumor cells and extracellular vesicles. Adv. Mater. 31(45), e1805344 (2019). https://doi.org/10.1002/adma.201805344
  18. J. Kaur, M. Preethi, R. Srivastava, V. Borse, Role of IL-6 and IL-8 biomarkers for optical and electrochemical based point-of-care detection of oral cancer. Biosens. Bioelectron. X 11, 100212 (2022). https://doi.org/10.1016/j.biosx.2022.100212
  19. F.P. de García Arquer, D.V. Talapin, V.I. Klimov, Y. Arakawa, M. Bayer et al., Semiconductor quantum dots: technological progress and future challenges. Science 373, eaaz8541 (2021). https://doi.org/10.1126/science
  20. K. Hennessy, A. Badolato, M. Winger, D. Gerace, M. Atatüre et al., Quantum nature of a strongly coupled single quantum dot–cavity system. Nature 445(7130), 896–899 (2007). https://doi.org/10.1038/nature05586
  21. S.Y. Lim, W. Shen, Z. Gao, Carbon quantum dots and their applications. Chem. Soc. Rev. 44(1), 362–381 (2015). https://doi.org/10.1039/c4cs00269e
  22. W.C. Chan, S. Nie, Quantum dot bioconjugates for ultrasensitive nonisotopic detection. Science 281(5385), 2016–2018 (1998). https://doi.org/10.1126/science.281.5385.2016
  23. T. Lee, B.J. Kim, H. Lee, D. Hahm, W.K. Bae et al., Bright and stable quantum dot light-emitting diodes. Adv. Mater. 34(4), 2106276 (2022). https://doi.org/10.1002/adma.202106276
  24. X. Liu, W. Wu, D. Cui, X. Chen, W. Li, Functional micro-/ nanomaterials for multiplexed biodetection. Adv. Mater. 33(30), e2004734 (2021). https://doi.org/10.1002/adma.202004734
  25. C. Grazon, R.C. Baer, U. Kuzmanović, T. Nguyen, M. Chen et al., A progesterone biosensor derived from microbial screening. Nat. Commun. 11(1), 1276 (2020). https://doi.org/10.1038/s41467-020-14942-5
  26. M. Chen, T.T. Nguyen, N. Varongchayakul, C. Grazon, M. Chern et al., Surface immobilized nucleic acid-transcription factor quantum dots for biosensing. Adv. Healthc. Mater. 9(17), e2000403 (2020). https://doi.org/10.1002/adhm.202000403
  27. K. Chen, L.Y.T. Chou, F. Song, W.C.W. Chan, Fabrication of metal nanoshell quantum-dot barcodes for biomolecular detection. Nano Today 8(3), 228–234 (2013). https://doi.org/10.1016/j.nantod.2013.04.009
  28. X. Pei, B. Zhang, J. Tang, B. Liu, W. Lai et al., Sandwich-type immunosensors and immunoassays exploiting nanostructure labels: a review. Anal. Chim. Acta 758, 1–18 (2013). https://doi.org/10.1016/j.aca.2012.10.060
  29. J. Baniukevic, I. Hakk Boyaci, A. Goktug Bozkurt, U. Tamer, A. Ramanavicius et al., Magnetic gold nanops in SERS-based sandwich immunoassay for antigen detection by well oriented antibodies. Biosens. Bioelectron. 43, 281–288 (2013). https://doi.org/10.1016/j.bios.2012.12.014
  30. H. Ueda, K. Tsumoto, K. Kubota, E. Suzuki, T. Nagamune et al., Open sandwich ELISA: a novel immunoassay based on the interchain interaction of antibody variable region. Nat. Biotechnol. 14(13), 1714–1718 (1996). https://doi.org/10.1038/nbt1296-1714
  31. M. Poudineh, C.L. Maikawa, E.Y. Ma, J. Pan, D. Mamerow et al., A fluorescence sandwich immunoassay for the real-time continuous detection of glucose and insulin in live animals. Nat. Biomed. Eng. 5(1), 53–63 (2021). https://doi.org/10.1038/s41551-020-00661-1
  32. Q. Zhang, Z. Dong, X. Dong, Q. Duan, J. Ji et al., Double-side-coated grid-type mechanical membrane biosensor based on AuNPs self-assembly and 3D printing. Adv. Mater. Interfaces 9(3), 2101461 (2022). https://doi.org/10.1002/admi.202101461
  33. Q. Xiong, C.Y. Lim, J. Ren, J. Zhou, K. Pu et al., Magnetic nanochain integrated microfluidic biochips. Nat. Commun. 9(1), 1743 (2018). https://doi.org/10.1038/s41467-018-04172-1
  34. R. Dong, Y. Liu, L. Mou, J. Deng, X. Jiang, Microfluidics-based biomaterials and biodevices. Adv. Mater. 31(45), 1805033 (2019). https://doi.org/10.1002/adma.201805033
  35. L. Mou, X. Jiang, Materials for microfluidic immunoassays: a review. Adv. Healthc. Mater. 6, 1601403 (2017). https://doi.org/10.1002/adhm.201601403
  36. U. Hassan, T. Ghonge, B. Reddy Jr., M. Patel, M. Rappleye et al., A point-of-care microfluidic biochip for quantification of CD64 expression from whole blood for sepsis stratification. Nat. Commun. 8, 15949 (2017). https://doi.org/10.1038/ncomms15949
  37. Y. Zhang, M. Sun, H. Zhou, Y. Zhang, J. Qiu et al., Microfluidic biosensing platform integrated with flexible sensing array for cancer biomarker point-of-care testing. Sens. Actuat. B Chem. 427, 137148 (2025). https://doi.org/10.1016/j.snb.2024.137148
  38. J.W. Lee, A machine vision system for lane-departure detection. Comput. Vis. Image Underst. 86(1), 52–78 (2002). https://doi.org/10.1006/cviu.2002.0958
  39. Y. Wu, Y. Lu, An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine. Meas. Control 52, 1102–1110 (2019). https://doi.org/10.1177/0020294019858175
  40. W. Kong, L. Zhou, Y. Wang, J. Zhang, J. Liu et al., A system of driving fatigue detection based on machine vision and its application on smart device. J. Sens. 2015, 548602 (2015). https://doi.org/10.1155/2015/548602
  41. J. Zhou, P.T. Lin, Midinfrared multispectral detection for real-time and noninvasive analysis of the structure and composition of materials. ACS Sens. 3(7), 1322–1328 (2018). https://doi.org/10.1021/acssensors.8b00222
  42. X. Sun, K. Chen, E.P. Berg, J.D. Magolski, Predicting fresh beef color grade using machine vision imaging and support vector machine (SVM) analysis. J. Anim. Vet. Adv. 10(12), 1504–1511 (2011). https://doi.org/10.3923/javaa.2011.1504.1511
  43. Y. Yang, J. Wang, W. Huang, G. Wan, M. Xia et al., Integrated urinalysis devices based on interface-engineered field-effect transistor biosensors incorporated with electronic circuits. Adv. Mater. 34(36), e2203224 (2022). https://doi.org/10.1002/adma.202203224
  44. Y. Zhang, D. Chen, W. He, N. Chen, L. Zhou et al., Interface-engineered field-effect transistor electronic devices for biosensing. Adv. Mater. 2306252 (2023). https://doi.org/10.1002/adma.202306252
  45. A.K. Patel, S. Chatterjee, A.K. Gorai, Development of machine vision-based ore classification model using support vector machine (SVM) algorithm. Arab. J. Geosci. 10(5), 107 (2017). https://doi.org/10.1007/s12517-017-2909-0
  46. G.H. John, Robust Decision Trees: Removing Outliers from Databases. in KDD-95 Proc. (1995), pp. 174–179
  47. J. Hao, H. Liu, J. Miao, R. Lu, Z. Zhou et al., A facile route to synthesize CdSe/ZnS thick-shell quantum dots with precisely controlled green emission properties: towards QDs based LED applications. Sci. Rep. 9(1), 12048 (2019). https://doi.org/10.1038/s41598-019-48469-7
  48. N.T. Vo, H.D. Ngo, N.P. Do Thi, K.P. Nguyen Thi, A.P. Duong et al., Stability investigation of ligand-exchanged CdSe/ZnS-Y (Y = 3-mercaptopropionic acid or mercaptosuccinic acid) through Zeta potential measurements. J. Nanomater. 2016(1), 8564648 (2016). https://doi.org/10.1155/2016/8564648
  49. D.U. Lee, D.H. Kim, D.H. Choi, S.W. Kim, H.S. Lee et al., Microstructural and optical properties of CdSe/CdS/ZnS core-shell-shell quantum dots. Opt. Express 24(2), A350–A357 (2016). https://doi.org/10.1364/OE.24.00A350
  50. I. Coropceanu, M.G. Bawendi, Core/shell quantum dot based luminescent solar concentrators with reduced reabsorption and enhanced efficiency. Nano Lett. 14(7), 4097–4101 (2014). https://doi.org/10.1021/nl501627e
  51. M. Song, M. Yang, J. Hao, Pathogenic virus detection by optical nanobiosensors. Cell Rep. Phys. Sci. 2(1), 100288 (2021). https://doi.org/10.1016/j.xcrp.2020.100288
  52. A.D. Chowdhury, K. Takemura, T.-C. Li, T. Suzuki, E.Y. Park, Electrical pulse-induced electrochemical biosensor for hepatitis E virus detection. Nat. Commun. 10, 3737 (2019). https://doi.org/10.1038/s41467-019-11644-5
  53. S. Zhou, D. Tu, Y. Liu, W. You, Y. Zhang et al., Ultrasensitive point-of-care test for tumor marker in human saliva based on luminescence-amplification strategy of lanthanide nanoprobes. Adv. Sci. 8(5), 2002657 (2021). https://doi.org/10.1002/advs.202002657
  54. M. Song, M.-C. Wong, L. Li, F. Guo, Y. Liu et al., Rapid point-of-care detection of SARS-CoV-2 RNA with smartphone-based upconversion luminescence diagnostics. Biosens. Bioelectron. 222, 114987 (2023). https://doi.org/10.1016/j.bios.2022.114987
  55. Y. Chen, W. Chu, W. Liu, X. Guo, Distance-based carcinoembryonic antigen assay on microfluidic paper immunodevice. Sens. Actuat. B Chem. 260, 452–459 (2018). https://doi.org/10.1016/j.snb.2017.12.197
  56. P. Li, W. Li, Z. Xie, H. Zhan, L. Deng et al., A label-free and signal-amplifiable assay method for colorimetric detection of carcinoembryonic antigen. Biotechnol. Bioeng. 119, 504–512 (2022). https://doi.org/10.1002/bit.28003
  57. S. Shi, J. Chen, X. Wang, M. Xiao, A.R. Chandrasekaran et al., Biointerface engineering with nucleic acid materials for biosensing applications. Adv. Funct. Mater. 32(37), 2201069 (2022). https://doi.org/10.1002/adfm.202201069
  58. P. Gao, D. Wang, C. Che, Q. Ma, X. Wu et al., Regional and functional division of functional elements of solid-state nanochannels for enhanced sensitivity and specificity of biosensing in complex matrices. Nat. Protoc. 16(9), 4201–4226 (2021). https://doi.org/10.1038/s41596-021-00574-6
  59. G. Kabay, J. DeCastro, A. Altay, K. Smith, H.W. Lu et al., Emerging biosensing technologies for the diagnostics of viral infectious diseases. Adv. Mater. 34(30), 2201085 (2022). https://doi.org/10.1002/adma.202201085
  60. S.A. Taylor, CCD and CMOS imaging array technologies: technology review. UK: Xerox Res. Cent. Eur. 1–14 (1998)
  61. H. Liu, Z. Li, R. Shen, Z. Li, Y. Yang et al., Point-of-care pathogen testing using photonic crystals and machine vision for diagnosis of urinary tract infections. Nano Lett. 21(7), 2854–2860 (2021). https://doi.org/10.1021/acs.nanolett.0c04942
  62. R.W.G. Hunt, M.R. Pointer, Measuring colour (John Wiley & Sons, New York, 2011)
  63. P.B. Catrysse, N. Zhao, W. Jin, S. Fan, Subwavelength Bayer RGB color routers with perfect optical efficiency. Nanophotonics 11(10), 2381–2387 (2022). https://doi.org/10.1515/nanoph-2022-0069
  64. J. Menser, F. Schneider, T. Dreier, S.A. Kaiser, Multi-pulse shadowgraphic RGB illumination and detection for flow tracking. Exp. Fluids 59(6), 90 (2018). https://doi.org/10.1007/s00348-018-2541-0
  65. K.J. Söderholm, R. Mukherjee, J. Longmate, Filler leachability of composites stored in distilled water or artificial saliva. J. Dent. Res. 75(9), 1692–1699 (1996). https://doi.org/10.1177/00220345960750091201
  66. F. Naim, S. Messier, L. Saucier, G. Piette, Postprocessing in vitro digestion challenge to evaluate survival of Escherichia coli O157:H7 in fermented dry sausages. Appl. Environ. Microbiol. 70(11), 6637–6642 (2004). https://doi.org/10.1128/AEM.70.11.6637-6642.2004
Read More
Author biographies are not available.
Download this PDF file
PDF SUPPLEMENTARY FILE
Statistic
Read Counter : 354 Download : 266

Most read articles by the same author(s)