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Recent Advances in Photoelectrochemical Sensing of Alzheimer’s Biomarkers

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Abstract

Alzheimer's disease (AD) is the most prevalent cause of dementia, affecting one in nine people aged 65 years and older worldwide. Pathological hallmarks include the accumulation of senile plaques and neurofibrillary tangles, which are associated with changes in the levels of AD biomarkers, such as amyloid peptides and tau proteins. As the neuropathological process of AD commences decades before the onset of cognitive symptoms, an accurate assessment of AD biomarkers is critical for the early identification of at-risk AD patients. Photoelectrochemical (PEC) bioanalysis is a promising sensing methodology that exhibits superior sensing performance owing to the complete separation of the excitation source and detection signal. Numerous efforts have been made to develop PEC analytical platforms that target AD biomarkers. This review provides an overview of the recent advances in PEC sensing of AD biomarkers in body fluids. The major components of PEC sensing platforms (such as photoactive transducers, affinity agents, and targeted AD biomarkers) and PEC signaling strategies for detecting AD biomarkers are outlined. In addition, we summarize the current issues and strategies for advancing PEC sensing techniques to meet the ever-increasing demand for the early diagnosis of AD.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Nordberg, A.: Towards early diagnosis in Alzheimer disease. Nat. Rev. Neurol. 11, 69–70 (2015)

    Article  CAS  PubMed  Google Scholar 

  2. Gustavsson, A., et al.: Global estimates on the number of persons across the Alzheimer’s disease continuum. Alzheimer’s Dement. (2022). https://doi.org/10.1002/alz.12694

    Article  Google Scholar 

  3. Lleó, A., et al.: Cerebrospinal fluid biomarkers in trials for Alzheimer and Parkinson diseases. Nat. Rev. Neurol. 11, 41–55 (2015)

    Article  PubMed  Google Scholar 

  4. Ashton, N.J., et al.: A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. Sci. Adv. 5, eaau7220 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zetterberg, H., Schott, J.M.: Biomarkers for Alzheimer’s disease beyond amyloid and tau. Nat. Med. 25, 201–203 (2019)

    Article  CAS  PubMed  Google Scholar 

  6. Jansen, W.J., et al.: Association of cerebral amyloid-β aggregation with cognitive functioning in persons without dementia. JAMA Psychiat. 75, 84–95 (2018)

    Article  Google Scholar 

  7. Jung, H., et al.: Silica nanodepletors: targeting and clearing Alzheimer’s β-amyloid plaques. Adv. Funct. Mater. 30, 1910475 (2020)

    Article  CAS  Google Scholar 

  8. Lee, J.S., Park, C.B.: Microfluidic dissociation and clearance of Alzheimer’s β-amyloid aggregates. Biomaterials 31, 6789–6795 (2010)

    Article  CAS  PubMed  Google Scholar 

  9. Polanco, J.C., et al.: Amyloid-β and tau complexity — towards improved biomarkers and targeted therapies. Nat. Rev. Neurol. 14, 22 (2017)

    Article  PubMed  Google Scholar 

  10. Jang, M., Kim, H.N.: From single- to multi-organ-on-a-chip system for studying metabolic diseases. BioChip J. (2023). https://doi.org/10.1007/s13206-023-00098-z

    Article  Google Scholar 

  11. Scarpini, E., Schelterns, P., Feldman, H.: Treatment of Alzheimer’s disease current status and new perspectives. Lancet Neurol. 2, 539–547 (2003)

    Article  CAS  PubMed  Google Scholar 

  12. Chapman, R.M., et al.: Diagnosis of Alzheimer’s disease using neuropsychological testing improved by multivariate analyses. J. Clin. Exp. Neuropsychol. 32, 793–808 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nestor, P.J., Scheltens, P., Hodges, J.R.: Advances in the early detection of Alzheimer’s disease. Nat. Med. 10, S34 (2004)

    Article  PubMed  Google Scholar 

  14. McKhann, G.M., et al.: The diagnosis of dementia due to Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dementia 7, 263–269 (2011)

    Article  PubMed  Google Scholar 

  15. Teunissen, C.E., et al.: Blood-based biomarkers for Alzheimer’s disease: towards clinical implementation. Lancet Neurol. 21, 66–77 (2022)

    Article  CAS  PubMed  Google Scholar 

  16. Hansson, O., et al.: The Alzheimer’s association appropriate use recommendations for blood biomarkers in Alzheimer’s disease. Alzheimer’s Dementia 18, 2669–2686 (2022)

    Article  CAS  PubMed  Google Scholar 

  17. Dubois, B., et al.: Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 13, 614–629 (2014)

    Article  PubMed  Google Scholar 

  18. Zhao, W.-W., Xu, J.-J., Chen, H.-Y.: Photoelectrochemical immunoassays. Anal. Chem. 90, 615–627 (2018)

    Article  CAS  PubMed  Google Scholar 

  19. Son, M.H., Park, S.W., Sagong, H.Y., Jung, Y.K.: Recent advances in electrochemical and optical biosensors for cancer biomarker detection. BioChip J. 17, 44–67 (2023)

    Article  CAS  Google Scholar 

  20. Park, J.A., et al.: Recent trends in biosensors based on electrochemical and optical techniques for cyanobacterial neurotoxin detection. BioChip J. 16, 146–157 (2022)

    Article  CAS  Google Scholar 

  21. Assaifan, A.K., Alqahtani, F.A., Alnamlah, S., Almutairi, R., Alkhammash, H.I.: Detection and real-time monitoring of LDL-cholesterol by redox-free impedimetric biosensors. BioChip J. 16, 197–206 (2022)

    Article  CAS  Google Scholar 

  22. El-Said, W.A., et al.: Electrochemical microbiosensor for detecting COVID-19 in a patient sample based on gold microcuboids pattern. BioChip J. 15, 287–295 (2021)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Kwon, K., et al.: Fully automated system for rapid enrichment and precise detection of enterobacteria using magneto-electrochemical impedance measurements. BioChip J. 15, 233–242 (2021)

    Article  CAS  Google Scholar 

  24. Eisenberg, D., Ahn, H.S., Bard, A.J.: Enhanced photoelectrochemical water oxidation on bismuth vanadate by electrodeposition of amorphous titanium dioxide. J. Am. Chem. Soc. 136, 14011–14014 (2014)

    Article  CAS  PubMed  Google Scholar 

  25. Li, J., Cai, L., Shang, J., Yu, Y., Zhang, L.: Giant enhancement of internal electric field boosting bulk charge separation for photocatalysis. Adv. Mater. 28, 4059–4064 (2016)

    Article  CAS  PubMed  Google Scholar 

  26. Kubacka, A., Fernández-García, M., Colón, G.: Advanced nanoarchitectures for solar photocatalytic applications. Chem. Rev. 112, 1555–1614 (2012)

    Article  CAS  PubMed  Google Scholar 

  27. Schneider, J., et al.: Understanding TiO2 photocatalysis: mechanisms and materials. Chem. Rev. 114, 9919–9986 (2014)

    Article  CAS  PubMed  Google Scholar 

  28. Zhang, X., Zhang, J., Gao, Y., Yan, J., Song, W.: Controllable signal molecule release from Au NP-gated MSNs for photocathodic detection of ultralow level AβO. Chem. Commun. 58, 839–842 (2022)

    Article  CAS  Google Scholar 

  29. Kim, K., Park, C.B.: Femtomolar sensing of Alzheimer’s tau proteins by water oxidation-coupled photoelectrochemical platform. Biosens. Bioelectron. 154, 112075 (2020)

    Article  CAS  PubMed  Google Scholar 

  30. Gao, N., et al.: Ultrasensitive label-free photoelectrochemical immunosensor for the detection of amyloid β-protein based on Zn:SnO2/SnS2-Au nanocomposites. Sensors Actuators B: Chem. 308, 127576 (2020)

    Article  CAS  Google Scholar 

  31. Han, Q., Chi, H., Wang, H., Wu, D., Wei, Q.: Using PbS–Au heterodimers as signal quencher for the sensitive photoelectrochemical immunoassay of amyloid β-protein. Anal. Chim. Acta 1092, 85–92 (2019)

    Article  CAS  PubMed  Google Scholar 

  32. Feng, J., et al.: An amplification label of core–shell CdSe@CdS QD sensitized GO for a signal-on photoelectrochemical immunosensor for amyloid β-protein. J. Mater. Chem. B 7, 1142–1148 (2019)

    Article  CAS  PubMed  Google Scholar 

  33. Xu, R., et al.: A photoelectrochemical sensor for highly sensitive detection of amyloid beta based on sensitization of Mn:CdSe to Bi2WO6/CdS. Biosens. Bioelectron. 122, 37–42 (2018)

    Article  CAS  PubMed  Google Scholar 

  34. Wang, Y., et al.: Ultrasensitive photoelectrochemical immunosensor for the detection of amyloid β-protein based on SnO2/SnS2/Ag2S nanocomposites. Biosens. Bioelectron. 120, 1–7 (2018)

    Article  CAS  PubMed  Google Scholar 

  35. Alivisatos, A.P.: Semiconductor clusters, nanocrystals, and quantum dots. Science 271, 933–937 (1996)

    Article  CAS  Google Scholar 

  36. Alivisatos, A.P.: Perspectives on the physical chemistry of semiconductor nanocrystals. J. Phys. Chem. 100, 13226–13239 (1996)

    Article  CAS  Google Scholar 

  37. Lee, J., Feng, X., Chen, O., Bawendi, M.G., Huang, J.: Stable, small, specific, low-valency quantum dots for single-molecule imaging. Nanoscale 10, 4406–4414 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Martynenko, I.V., et al.: Application of semiconductor quantum dots in bioimaging and biosensing. J. Mater. Chem. B 5, 6701–6727 (2017)

    Article  CAS  PubMed  Google Scholar 

  39. Kim, S.-K., Sung, H., Hwang, S.-H., Kim, M.-N.: A new quantum dot-based lateral flow immunoassay for the rapid detection of influenza viruses. BioChip J. 16, 175–182 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tabrizi, M.A., Ferré-Borrull, J., Kapruwan, P., Marsal, L.F.: A photoelectrochemical sandwich immunoassay for protein S100β, a biomarker for Alzheimer’s disease, using an ITO electrode modified with a reduced graphene oxide-gold conjugate and CdS-labeled secondary antibody. Microchim. Acta 186, 117 (2019)

    Article  Google Scholar 

  41. Bu, Y., Zhang, M., Fu, J., Yang, X., Liu, S.: Black phosphorous quantum dots for signal-on cathodic photoelectrochemical aptasensor monoitoring amyloid β peptide. Anal. Chim. Acta 1189, 339200 (2022)

    Article  CAS  PubMed  Google Scholar 

  42. Li, S., et al.: A high-sensitivity thermal analysis immunochromatographic sensor based on au nanoparticle-enhanced two-dimensional black phosphorus photothermal-sensing materials. Biosens. Bioelectron. 133, 223–229 (2019)

    Article  CAS  PubMed  Google Scholar 

  43. Qu, G., et al.: Property-activity relationship of black phosphorus at the nano-bio interface: from molecules to organisms. Chem. Rev. 120, 2288–2346 (2020)

    Article  CAS  PubMed  Google Scholar 

  44. Usman, M., Mendiratta, S., Lu, K.-L.: Semiconductor metal-organic frameworks: future low-bandgap materials. Adv. Mater. 29, 1605071 (2017)

    Article  Google Scholar 

  45. Byun, M.J., et al.: Advances in nanoparticles for effective delivery of RNA therapeutics. BioChip J. 16, 128–145 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Gao, Y., et al.: Switchable multiplex photoelectrochemical immunoassay of Aβ42 and Aβ40 based on a pH-responsive i-Motif probe and pyrene-based MOF photocathode. Anal. Chem. 94, 6621–6627 (2022)

    Article  CAS  PubMed  Google Scholar 

  47. Wu, Y., Tilley, R.D., Gooding, J.J.: Challenges and solutions in developing ultrasensitive biosensors. J. Am. Chem. Soc. 141, 1162–1170 (2019)

    Article  CAS  PubMed  Google Scholar 

  48. Jing, M., Bowser, M.T.: Methods for measuring aptamer-protein equilibria: a review. Anal. Chim. Acta 686, 9–18 (2011)

    Article  CAS  PubMed  Google Scholar 

  49. Perchiacca, J.M., Ladiwala, A.R.A., Bhattacharya, M., Tessier, P.M.: Structure-based design of conformation- and sequence-specific antibodies against amyloid β. Proc. Natl. Acad. Sci. 109, 84–89 (2012)

    Article  CAS  PubMed  Google Scholar 

  50. Voskuil, J.: Commercial antibodies and their validation. F1000Res 3, 232–232 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  51. Dunn, M.R., Jimenez, R.M., Chaput, J.C.: Analysis of aptamer discovery and technology. Nat. Rev. Chem. 1, 0076 (2017)

    Article  CAS  Google Scholar 

  52. Kweon, S.Y., Park, J.P., Park, C.Y., Park, T.J.: Graphene oxide-mediated fluorometric aptasensor for okadaic acid detection. BioChip J. 16, 207–213 (2022)

    Article  CAS  Google Scholar 

  53. Kang, J., Kim, M.-G.: Advancements in DNA-assisted Immunosensors. BioChip J. 14, 18–31 (2020)

    Article  CAS  Google Scholar 

  54. Zhang, J., Qin, N., Wang, M., Hun, X.: Double-redox cycling signal amplification coupling Mo2C-graphyne-AuNPs modified electrode based photoelectrochemical assay for Aβ1-40 oligomers. Sensors Actuators B: Chem. 326, 128947 (2021)

    Article  CAS  Google Scholar 

  55. Teng, I.T., et al.: Identification and characterization of DNA Aptamers specific for phosphorylation epitopes of Tau protein. J. Am. Chem. Soc. 140, 14314–14323 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kutovyi, Y., et al.: Amyloid-beta peptide detection via aptamer-functionalized nanowire sensors exploiting single-trap phenomena. Biosens. Bioelectron. 154, 112053 (2020)

    Article  CAS  PubMed  Google Scholar 

  57. Guo, Q., et al.: A novel aptamer biosensor based on polydopamine quenched electrochemiluminescence of polyfluorene nanoparticles for amyloid-β oligomers detection. Sensors Actuators B: Chem. 368, 132204 (2022)

    Article  CAS  Google Scholar 

  58. Panza, F., Lozupone, M., Logroscino, G., Imbimbo, B.P.: A critical appraisal of amyloid-β-targeting therapies for Alzheimer disease. Nat. Rev. Neurol. 15, 73–88 (2019)

    Article  PubMed  Google Scholar 

  59. Benilova, I., Karran, E., De Strooper, B.: The toxic Aβ oligomer and Alzheimer’s disease: an emperor in need of clothes. Nat. Neurosci. 15, 349–357 (2012)

    Article  CAS  PubMed  Google Scholar 

  60. Lee, J.S., Lee, B.I., Park, C.B.: Photo-induced inhibition of Alzheimer’s β-amyloid aggregation in vitro by rose bengal. Biomaterials 38, 43–49 (2015)

    Article  CAS  PubMed  Google Scholar 

  61. Iadanza, M.G., Jackson, M.P., Hewitt, E.W., Ranson, N.A., Radford, S.E.: A new era for understanding amyloid structures and disease. Nat. Rev. Mol. Cell Biol. 19, 755–773 (2018)

    Article  CAS  PubMed  Google Scholar 

  62. Lee, J.S., Ryu, J., Park, C.B.: High-Throughput analysis of Alzheimer’s β-amyloid aggregation using a microfluidic self-assembly of monomersf. Anal. Chem. 81, 2751–2759 (2009)

    Article  CAS  PubMed  Google Scholar 

  63. Lee, J.S., Um, E., Park, J.-K., Park, C.B.: Microfluidic self-assembly of insulin monomers into amyloid fibrils on a solid surface. Langmuir 24, 7068–7071 (2008)

    Article  CAS  PubMed  Google Scholar 

  64. Kim, K., Lee, B.I., Chung, Y.J., Choi, W.S., Park, C.B.: Hematite-based photoelectrode materials for photoelectrocatalytic inhibition of Alzheimer’s β-amyloid self-assembly. Adv. Healthc. Mater. 6, 1601133 (2017)

    Article  Google Scholar 

  65. Kim, K., Lee, S.H., Choi, D.S., Park, C.B.: Photoactive bismuth vanadate structure for light-triggered dissociation of Alzheimer’s β-amyloid aggregates. Adv. Funct. Mater. 28, 1802813 (2018)

    Article  Google Scholar 

  66. Heo, Y., Kim, K., Kim, J., Jang, J., Park, C.B.: Near-infrared-active copper bismuth oxide electrodes for targeted dissociation of Alzheimer’s β-amyloid aggregates. ACS Appl. Mater. Interfaces 12, 23667–23676 (2020)

    Article  CAS  PubMed  Google Scholar 

  67. Lee, J.C., Kim, S.J., Hong, S., Kim, Y.: Diagnosis of Alzheimer’s disease utilizing amyloid and tau as fluid biomarkers. Exp. Mol. Med. 51, 53 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  68. Nakamura, A., et al.: High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature 554, 249 (2018)

    Article  CAS  PubMed  Google Scholar 

  69. Janelidze, S., et al.: Plasma β -amyloid in Alzheimer ’ s disease and vascular disease. Sci. Rep. 6, 26801 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Mehta, P.D., et al.: Plasma and cerebrospinal fluid levels of amyloid β proteins 1–40 and 1–42 in Alzheimer disease. Arch. Neurol. 57, 100–105 (2000)

    Article  CAS  PubMed  Google Scholar 

  71. Jack, C.R., et al.: Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9, 119–128 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Niemantsverdriet, E., et al.: The Cerebrospinal Fluid Aβ1-42/Aβ1-40 Ratio Improves Concordance with Amyloid-PET for Diagnosing Alzheimer’s Disease in a Clinical Setting. J. Alzheimers Dis. 60, 561–576 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lewczuk, P., et al.: Cerebrospinal fluid Aβ42/40 corresponds better than Aβ42 to amyloid PET in Alzheimer’s Disease. J. Alzheimers Dis. 55, 813–822 (2017)

    Article  CAS  PubMed  Google Scholar 

  74. Reiss Allison, B., Arain Hirra, A., Stecker Mark, M., Siegart Nicolle, M., Kasselman Lora, J.: Amyloid toxicity in Alzheimer’s disease. Rev. Neurosci. 29, 613 (2018)

    Article  CAS  PubMed  Google Scholar 

  75. Lu, H., Zhu, X.-C., Jiang, T., Yu, J.-T., Tan, L.: Body fluid biomarkers in Alzheimer’s disease. Ann. Transl. Med. 3, 70–70 (2015)

    PubMed  PubMed Central  Google Scholar 

  76. Humpel, C.: Identifying and validating biomarkers for Alzheimer’s disease. Trends Biotechnol. 29, 26–32 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Shaw, L.M., Korecka, M., Clark, C.M., Lee, V.M.Y., Trojanowski, J.Q.: Biomarkers of neurodegeneration for diagnosis and monitoring therapeutics. Nat. Rev. Drug Discovery 6, 295 (2007)

    Article  CAS  PubMed  Google Scholar 

  78. Cristóvão, J.S., et al.: The neuronal S100B protein is a calcium-tuned suppressor of amyloid-β aggregation. Sci. Adv. 4, eaaq1702 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  79. Varhaug, K.N., Torkildsen, Ø., Myhr, K.-M., Vedeler, C.A.: Neurofilament light chain as a biomarker in multiple sclerosis. Front. Neurol. 10, 338 (2019)

    Article  PubMed  PubMed Central  Google Scholar 

  80. Mattsson, N., Andreasson, U., Zetterberg, H., Blennow, K., Alzheimer’s Disease Neuroimaging, I.: Association of plasma neurofilament light with neurodegeneration in patients With Alzheimer disease. JAMA Neurol. 74, 557–566 (2017)

    Article  PubMed  PubMed Central  Google Scholar 

  81. Preische, O., et al.: Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 25, 277–283 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Castellano, J.M., et al.: Human apoE isoforms differentially regulate brain amyloid-β peptide clearance. Sci Transl Med 3, 89ra57 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Wang, M., Qin, L., Tang, B.: MicroRNAs in Alzheimer’s Disease. Front. Genet. 10, 153 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lim, J., et al.: miRNA sensing hydrogels capable of self-signal amplification for early diagnosis of Alzheimer’s disease. Biosens. Bioelectron. 209, 114279 (2022)

    Article  CAS  PubMed  Google Scholar 

  85. Zhang, N., et al.: A photoelectrochemical immunosensor based on CdS/CdTe-cosensitized SnO2 as a platform for the ultrasensitive detection of amyloid β-protein. Analyst 145, 619–625 (2020)

    Article  CAS  PubMed  Google Scholar 

  86. Son, E.J., et al.: Carbon nanotube-graphitic carbon nitride hybrid films for flavoenzyme-catalyzed photoelectrochemical cells. Adv. Funct. Mater. 28, 1705232 (2018)

    Article  Google Scholar 

  87. Kim, C.W., Son, Y.S., Kang, M.J., Kim, D.Y., Kang, Y.S.: (040)-crystal facet engineering of BiVO4 plate photoanodes for solar fuel production. Adv. Energy Mater. 6, 1501754 (2016)

    Article  Google Scholar 

  88. Kim, J.H., Lee, J.S.: Elaborately modified BiVO4 photoanodes for solar water splitting. Adv. Mater. 31, 1806938 (2019)

    Article  Google Scholar 

  89. Li, Y., Li, X., Meng, Y., Hun, X.: Photoelectrochemical platform for MicroRNA let-7a detection based on graphdiyne loaded with AuNPs modified electrode coupled with alkaline phosphatase. Biosens. Bioelectron. 130, 269–275 (2019)

    Article  CAS  PubMed  Google Scholar 

  90. Ovod, V., et al.: Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimer’s Dementia 13, 841–849 (2017)

    Article  PubMed  Google Scholar 

  91. Janelidze, S., et al.: CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios: better diagnostic markers of Alzheimer disease. Ann. Clin. Transl. Neurol. 3, 154–165 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Wiltfang, J., et al.: Amyloid β peptide ratio 42/40 but not Aβ42 correlates with phospho-Tau in patients with low- and high-CSF Aβ40 load. J. Neurochem. 101, 1053–1059 (2007)

    Article  CAS  PubMed  Google Scholar 

  93. Dai, W.-X., et al.: Hybrid PbS quantum dot/nanoporous NiO film nanostructure: preparation, characterization, and application for a self-powered cathodic photoelectrochemical biosensor. Anal. Chem. 89, 8070–8078 (2017)

    Article  CAS  PubMed  Google Scholar 

  94. Han, L., Guo, S., Wang, P., Dong, S.: Light-Driven, membraneless, hydrogen peroxide based fuel cells. Adv. Energy Mater. 5, 1400424 (2015)

    Article  Google Scholar 

  95. Kim, K., et al.: Metallic woodpile nanostructures for femtomolar sensing of Alzheimer’s neurofilament lights. ACS Nano 14, 10376–10384 (2020)

    Article  CAS  PubMed  Google Scholar 

  96. Frisoni, G.B., et al.: Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. Lancet Neurol. 16, 661–676 (2017)

    Article  PubMed  Google Scholar 

  97. Zhang, Y., et al.: Label-free photoelectrochemical immunosensor for amyloid β-protein detection based on SnO2/CdCO3/CdS synthesized by one-pot method. Biosens. Bioelectron. 126, 23–29 (2019)

    Article  CAS  PubMed  Google Scholar 

  98. Xu, Y.-T., et al.: Cathodic photoelectrochemical bioanalysis. TrAC Trends Anal. Chem. 114, 81–88 (2019)

    Article  CAS  Google Scholar 

  99. Kim, K., et al.: Clinically accurate diagnosis of Alzheimer’s disease via multiplexed sensing of core biomarkers in human plasma. Nat. Commun. 11, 119 (2020)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported by the research fund of Dankook University in 2022.

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  1. Department of Fiber Convergence Materials Engineering, Dankook University, Yongin-Si, Gyeonggi-Do, 16890, Republic of Korea

    Kayoung Kim

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Kim, K. Recent Advances in Photoelectrochemical Sensing of Alzheimer’s Biomarkers. BioChip J 17, 218–229 (2023). https://doi.org/10.1007/s13206-023-00105-3

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