Confinement-guided ultrasensitive optical assay with artificial intelligence for disease diagnostics

Wenjing Zhang; Yongfeng Lu; Chenyi Su; Yibo Wang; Yong-Fei Wang; Bo Zhang; Cheng Jiang; Keying Guo; Chuan Xu

doi:10.59717/j.xinn-med.2023.100023

The Innovation Medicine > 2023 Vol. 1 > No. 2 > 100023

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Confinement-guided ultrasensitive optical assay with artificial intelligence for disease diagnostics

1.
School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
2.
Department of Oncology & Cancer Institute, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu 610072, China
3.
Department of Laboratory Medicine and Sichuan Provincial Key Laboratory for Human Disease Gene Study, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610054, China
4.
Biotechnology and Food Engineering, Guangdong Technion-Israel Institute of Technology (GTIIT), Shantou 515063, China
5.
Faculty of Biotechnology and Food Engineering, Technion-Israel Institute of Technology (IIT), Haifa 3200003, Israel
6.
These authors contributed equally

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Corresponding authors: jiangcheng@cuhk.edu.cn (C.J.); keying.guo@gtiit.edu.cn (K.G.); xuchuan100@163.com (C.X.)
Received Date: 31 May 2023

Accepted Date: 31 August 2023

Published Online: 01 September 2023

The Innovation Medicine 1(2), https://doi.org/10.59717/j.xinn-med.2023.100023 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

PUBLIC SUMMARY
1. Advanced optical assays utilize external and internal confinement strategies for disease biomarker detection.
  
  Convergence of artificial intelligence (AI) with optical assays boosts diagnostic accuracy and prediction of disease paths.
  
  Embracing biotechnology and information technology permits the revolution of biomarker discovery.

ABSTRACT

ABSTRACT

The necessity for ultrasensitive detection is becoming increasingly apparent as it plays a pivotal role in disease early diagnostics and health management, particularly when it comes to detecting and monitoring low-abundance biomarkers or precious samples with tiny volumes. In many disease cases, such as cancer, infectious disease, autoimmune disorder, and neurodegenerative disease, low-abundant target biomarkers like circulating tumor cells (CTCs), extracellular vesicle (EV) subpopulations, and post-translational modified proteins (PTMs) are commonly existing and can be served as early indicators of disease onset or progression. However, these biomarkers often exist in ultra-low quantities in body fluids, surpassing the detection limits of conventional diagnostic tools like enzyme-linked immunosorbent assay (ELISA). This leads to the inability to probe disease evolution at a very early stage from molecular pathology perspective. In such regard, ultrasensitive optical assays have emerged as a solution to overcome these limitations and have witnessed significant progress in recent decades. This review provides a comprehensive overview of the recent advancements in ultrasensitive optical detection for disease diagnostics, particularly focusing on the conjunction of confinement within micro-/nano-structures and signal amplification to generate distinguishable optical readouts. The discussion begins with a meticulous evaluation of the advantages and disadvantages of these ultra-sensitive optical assays. Then, the spotlight is turned towards the implementation of artificial intelligence (AI) algorithms. The ability of AI to process large volumes of visible reporter signal and clinical data has proven invaluable in identifying unique patterns across multi-center cohort samples. Looking forward, the review underscores future advancements in developing convergent biotechnology (BT) and information technology (IT) toolbox, especially optical biosensors for high-throughput biomarker screening, point-of-care (PoC) testing with appropriate algorithms for their clinical translation are highlighted.

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REFERENCES

[1]	Cheung, A.H.K., Chow, C., and To, K.F. (2018). Latest development of liquid biopsy. J. Thorac. Dis. 10: S1645-S1651. DOI: 10.21037/jtd.2018.04.68. View in Article CrossRef Google Scholar
[2]	Miller, D.B., and O’Callaghan, J.P. (2015). Biomarkers of Parkinson’s disease: Present and future. Metabolism 64: S40-S46. DOI: 10.1016/j.metabol.2014.10.030. View in Article CrossRef Google Scholar
[3]	Counts, S.E., Ikonomovic, M.D., Mercado, N., et al. (2017). Biomarkers for the early detection and progression of Alzheimer’s disease. Neurotherapeutics 14: 35-53. DOI: 10.1007/s13311-016-0481-z. View in Article CrossRef Google Scholar
[4]	Gangi, S., Fletcher, J., Nathan, M.A., et al. (2004). Time interval between abnormalities seen on CT and the clinical diagnosis of pancreatic cancer: Retrospective review of CT scans obtained before diagnosis. Am. J. Roentgenol. 182: 897-903. DOI: 10.2214/ajr.182.4.1820897. View in Article CrossRef Google Scholar
[5]	Metintas, M., Ak, G., Dundar, E., et al. (2010). Medical thoracoscopy vs CT scan-guided Abrams pleural needle biopsy for diagnosis of patients with pleural effusions: A randomized, controlled trial. Chest 137: 1362-1368. DOI: 10.1378/chest.09-0884. View in Article CrossRef Google Scholar
[6]	Helpern, J.A., Jensen, J., Lee, S.P., et al. (2004). Quantitative MRI assessment of Alzheimer's disease. J. Mol. Neurosci. 24: 45-48. DOI: 10.1385/JMN:24:1:045. View in Article CrossRef Google Scholar
[7]	Morrow, M., Waters, J., and Morris, E. (2011). MRI for breast cancer screening, diagnosis, and treatment. Lancet 378: 1804-1811. DOI: 10.1016/S0140-6736(11)61350-0. View in Article CrossRef Google Scholar
[8]	Gao, Q., Asthana, A., Tong, T., et al. (2014). Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation. Medical Imaging 2014: Image Processing. Spie 9034 : 36-41. DOI: 10.1117/12.2043893. View in Article Google Scholar
[9]	Braum, L.S., McGonagle, D., Bruns, A., et al. (2013). Characterisation of hand small joints arthropathy using high-resolution MRI—limited discrimination between osteoarthritis and psoriatic arthritis. Eur. radiol. 23: 1686-1693. DOI: 10.1007/s00330-012-2739-0. View in Article CrossRef Google Scholar
[10]	Zarovni, N., Corrado, A., Guazzi, P., et al. (2015). Integrated isolation and quantitative analysis of exosome shuttled proteins and nucleic acids using immunocapture approaches. Methods 87: 46-58. DOI: 10.1016/j.ymeth.2015.05.028. View in Article CrossRef Google Scholar
[11]	Chi, X., Huang, D., Zhao, Z., et al. (2012). Nanoprobes for in vitro diagnostics of cancer and infectious diseases. Biomaterials 33: 189-206. DOI: 10.1016/j.biomaterials.2011.09.032. View in Article CrossRef Google Scholar
[12]	Zhang, Z., and Chan, D. W. (2010). The Road from discovery to clinical diagnostics: Lessons learned from the first FDA-cleared in vitro diagnostic multivariate index assay of proteomic biomarkers. Cancer Epidem. Biomar. 19 : 2995-2999. DOI: 10.1158/1055-9965.EPI-10-0580. View in Article Google Scholar
[13]	Pritchard, C.C., Cheng, H.H., and Tewari, M. (2012). MicroRNA profiling: Approaches and considerations. Nat. Rev. Genet. 13: 358−369. DOI: 10.1038/nrg3198. View in Article CrossRef Google Scholar
[14]	St John, A., and Price, C.P. (2014). Existing and emerging technologies for point-of-care testing. Clin. Biochem. Rev. 35: 155−167. View in Article Google Scholar
[15]	Whitesides, G.M. (2006). The origins and the future of microfluidics. Nature 442: 368-373. DOI: 10.1038/nature05058. View in Article CrossRef Google Scholar
[16]	Han, F., Wang, T., Liu, G., et al. (2022). Materials with tunable optical properties for wearable epidermal sensing in health monitoring. Adv. Mater. 34: e2109055. DOI: 10.1002/adma.202109055. View in Article CrossRef Google Scholar
[17]	Kaur, J., Jiang, C., and Liu, G. (2019). Different strategies for detection of HbA1c emphasizing on biosensors and point-of-care analyzers. Biosens. Bioelectron. 123: 85-100. DOI: 10.1016/j.bios.2018.06.018. View in Article CrossRef Google Scholar
[18]	Liu, G., Jiang, C., Lin, X., et al. (2021). Point-of-care detection of cytokines in cytokine storm management and beyond: Significance and challenges. View 2: 20210003. DOI: 10.1002/VIW.20210003. View in Article CrossRef Google Scholar
[19]	Fu, Y., Jiang, C., Tofaris, G.K., et al. (2020). Facile impedimetric analysis of neuronal exosome markers in Parkinson’s disease diagnostics. Anal. Chem. 92: 13647−13651. DOI: 10.1021/acs.analchem.0c03092. View in Article CrossRef Google Scholar
[20]	Chen, R., Mias, G.I., Li-Pook-Than, J., et al. (2012). Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148: 1293-1307. DOI: 10.1016/j.cell.2012.02.009. View in Article CrossRef Google Scholar
[21]	Sharafeldin, M., Yan, S., Jiang, C., et al. (2023). Alternating magnetic field-promoted nanoparticle mixing: the on-chip immunocapture of serum neuronal exosomes for Parkinson’s disease diagnostics. Anal. Chem. 95: 7906–7913. DOI: 10.1021/acs.analchem.3c00357. View in Article CrossRef Google Scholar
[22]	Jiang, C., Alam, M.T., Silva, S.M., et al. (2016). Unique sensing interface that allows the development of an electrochemical immunosensor for the detection of tumor necrosis factor α in whole blood. ACS Sens. 1: 1432-1438. DOI: 10.1021/acssensors.6b00532. View in Article CrossRef Google Scholar
[23]	Cao, C., Zhang, Y., Jiang, C., et al. (2017). Advances on aryldiazonium salt chemistry based interfacial fabrication for sensing applications. ACS Appl. Mater. Interfaces 9: 5031-5049. DOI: 10.1021/acsami.6b16108. View in Article CrossRef Google Scholar
[24]	Vasilieva, A., Yurina, L., Azarova, D.Y., et al. (2022). Development of a diagnostic approach based on the detection of post-translation modifications of fibrinogen associated with oxidative stress by the method of high efficiency liquid chromatography. J. Phys. Chem. B 16: 118−122. DOI: 10.1134/s1990793122010316. View in Article CrossRef Google Scholar
[25]	Chiki, A. (2020). Development of novel methods and tools to decipher the huntingtin post-translation modifications code. PhD thesis (EPFL). DOI: 10.5075/epfl-thesis-7537. View in Article Google Scholar
[26]	Jiang, C., Hopfner, F., Katsikoudi, A., et al. (2020). Serum neuronal exosomes predict and differentiate Parkinson’s disease from atypical parkinsonism. J. Neurol. Neurosurg. Psychiatry 91: 720−729. DOI: 10.1136/jnnp-2019-322588. View in Article CrossRef Google Scholar
[27]	Mitchell, P.S., Parkin, R.K., Kroh, E.M., et al. (2008). Circulating microRNAs as stable blood-based markers for cancer detection. PNAS 105: 10513-10518. DOI: 10.1073/pnas.0804549105. View in Article CrossRef Google Scholar
[28]	Weber, J.A., Baxter, D.H., Zhang, S., et al. (2010). The microRNA spectrum in 12 body fluids. Clin. Chem. 56: 1733-1741. DOI: 10.1373/clinchem.2010.147405. View in Article CrossRef Google Scholar
[29]	Chen, X., Ba, Y., Ma, L., et al. (2008). Characterization of microRNAs in serum: A novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res. 18: 997−1006. DOI: 10.1038/cr.2008.282. View in Article CrossRef Google Scholar
[30]	Lu, X., Hu, C., Jia, D., et al. (2021). Amplification-free and mix-and-read analysis of multiplexed microRNAs on a single plasmonic microbead. Nano Lett. 21: 6718-6724. DOI: 10.1021/acs.nanolett.1c02473. View in Article CrossRef Google Scholar
[31]	Wang, G., Tian, W., Liu, X., et al. (2020). New CRISPR-derived microRNA sensing mechanism based on Cas12a self-powered and rolling circle transcription-unleashed real-time crRNA recruiting. Anal. Chem. 92: 6702-6708. DOI: 10.1021/acs.analchem.0c00680. View in Article CrossRef Google Scholar
[32]	Goetzl, L., Merabova, N., Darbinian, N., et al. (2018). Diagnostic potential of neural exosome cargo as biomarkers for acute brain injury. Ann. Clin. Transl. Neurol. 5: 4-10. DOI: 10.1002/acn3.499. View in Article CrossRef Google Scholar
[33]	Singh, K., Nalabotala, R., Koo, K.M., et al. (2021). Separation of distinct exosome subpopulations: Isolation and characterization approaches and their associated challenges. Analyst 146: 3731−3749. DOI: 10.1039/D1AN00024A. View in Article CrossRef Google Scholar
[34]	Picciolini, S., Gualerzi, A., Vanna, R., et al. (2018). Detection and characterization of different brain-derived subpopulations of plasma exosomes by surface plasmon resonance imaging. Anal. Chem. 90: 8873-8880. DOI: 10.1021/acs.analchem.8b00941. View in Article CrossRef Google Scholar
[35]	Yousif, G., Qadri, S., Haik, M., et al. (2021). Circulating exosomes of neuronal origin as potential early biomarkers for development of stroke. Mol. Diagn. Ther. 25: 163−180. DOI: 10.1007/s40291-020-00508-0. View in Article CrossRef Google Scholar
[36]	Yan, S., Jiang, C., Davis, J.J., et al. (2023). Methodological considerations in neuronal extracellular vesicle isolation for α-synuclein biomarkers. Brain 146 : e95-e97. DOI: 10.1093/brain/awad169. View in Article Google Scholar
[37]	Kang, Y.T., Hadlock, T., Lo, T.W., et al. (2020). Dual-isolation and profiling of circulating tumor cells and cancer exosomes from blood samples with melanoma using immunoaffinity‐based microfluidic interfaces. Adv. Sci. 7: 2001581. DOI: 10.1002/advs.202001581. View in Article CrossRef Google Scholar
[38]	Słomka, A., Wang, B., Mocan, T., et al. (2022). Extracellular vesicles and circulating tumour cells-complementary liquid biopsies or standalone concepts? Theranostics 12: 5836. DOI: 10.7150/thno.73400. View in Article CrossRef Google Scholar
[39]	Schwarzenbach, H., Hoon, D.S., and Pantel, K.J. (2011). Cell-free nucleic acids as biomarkers in cancer patients. Nat. Rev. Cancer 11: 426-437. DOI: 10.1038/nrc3066. View in Article CrossRef Google Scholar
[40]	Jahr, S., Hentze, H., Englisch, S., et al. (2001). DNA fragments in the blood plasma of cancer patients: Quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 61: 1659−1665. View in Article Google Scholar
[41]	Zhang, H.Y., Wang, S., and Fang, G.Z. (2011). Applications and recent developments of multi-analyte simultaneous analysis by enzyme-linked immunosorbent assays. J. Immunol. Methods 368: 1-23. DOI: 10.1016/j.jim.2011.02.011. View in Article CrossRef Google Scholar
[42]	Lequin, R.M. (2005). Enzyme immunoassay (EIA)/enzyme-linked immunosorbent assay (ELISA). Clin. Chem. 51: 2415-2418. DOI: 10.1373/clinchem.2005.051532. View in Article CrossRef Google Scholar
[43]	Steyer, J.A., and Almers, W.J.N.r.M.c.b. (2001). A real-time view of life within 100 nm of the plasma membrane. Nat. Rev. Mol. Cell Biol. 2: 268-275. DOI: 10.1038/35067069. View in Article CrossRef Google Scholar
[44]	Axelrod, D. (2001). Total internal reflection fluorescence microscopy in cell biology. Traffic 2: 764-774. DOI: 10.1034/j.1600-0854.2001.21104.x. View in Article CrossRef Google Scholar
[45]	Hartmann, F.J., and Bendall, S.C. (2020). Immune monitoring using mass cytometry and related high-dimensional imaging approaches. Nat. Rev. Rheumatol. 16: 87-99. DOI: 10.1038/s41584-019-0338-z. View in Article CrossRef Google Scholar
[46]	Robinson, J.P., Rajwa, B., Patsekin, V., et al. (2012). Computational analysis of high-throughput flow cytometry data. Expert Opin. Drug Discov. 7: 679−693. DOI: 10.1517/17460441.2012.693475. View in Article CrossRef Google Scholar
[47]	Lu, Y., Cheng, H., Li, G.C., et al. (2022). Dynamic cryptography through plasmon-enhanced fluorescence blinking. Adv. Funct. Mater. 32: 2201372. DOI: 10.1002/adfm.202201372. View in Article CrossRef Google Scholar
[48]	Zheng, Y., Jiang, C., Ng, S.H., et al. (2016). Unclonable plasmonic security labels achieved by shadow-mask-lithography-assisted self-assembly. Adv. Mater. 28: 2330−2336. DOI: 10.1002/adma.201505022. View in Article CrossRef Google Scholar
[49]	Su, Q., Jiang, C., Gou, D., et al. (2021). Surface plasmon-assisted fluorescence enhancing and quenching: from theory to application. ACS Appl. Bio Mater. 4: 4684−4705. DOI: 10.1021/acsabm.1c00320. View in Article CrossRef Google Scholar
[50]	Zong, H., Wang, X., Mu, X., et al. (2019). Plasmon-enhanced fluorescence resonance energy transfer. Chem. Rec. 19: 818-842. DOI: 10.1002/tcr.201800181. View in Article CrossRef Google Scholar
[51]	Min, X., Cao, B., Huang, S., et al. (2023). Bioorthogonal chemistry-based high-efficient quantum dots binding boosts the detection sensitivity of plasmon-enhanced fluorescence platform for immunoassay. Sens. Actuators B Chem. 382: 133516. DOI: 10.1016/j.snb.2023.133516. View in Article CrossRef Google Scholar
[52]	Luan, J., Seth, A., Gupta, R., et al. (2020). Ultrabright fluorescent nanoscale labels for the femtomolar detection of analytes with standard bioassays. Nat. Biomed. Eng. 4: 518-530. DOI: 10.1038/s41551-020-0547-4. View in Article CrossRef Google Scholar
[53]	Fan, Z., Jiang, C., Wang, Y., et al. (2022). Engineered extracellular vesicles as intelligent nanosystem for next-generation of nanomedicine. Nanoscale Horiz. 7: 682-714. DOI: 10.1039/D2NH00070A. View in Article CrossRef Google Scholar
[54]	Jiang, C., Fu, Y., Liu, G., et al. (2022). Multiplexed profiling of extracellular vesicles for biomarker development. Nano-Micro Lett. 14: 3. DOI: 10.1007/s40820-021-00753-w. View in Article CrossRef Google Scholar
[55]	Jiang, C., Hopfner, F., Berg, D., et al. (2021). Validation of α-synuclein in L1CAM-immunocaptured exosomes as a biomarker for the stratification of Parkinsonian syndromes. Mov. Disord. 36: 2663−2669. DOI: 10.1002/mds.28591. View in Article CrossRef Google Scholar
[56]	Fan, W., Ren, W., and Liu, C. (2023). Advances in optical counting and imaging of micro/nano single-entity reactors for biomolecular analysis. Anal. Bioanal. Chem. 415: 97-117. DOI: 10.1007/s00216-022-04395-8. View in Article CrossRef Google Scholar
[57]	Zheng, Y., Soeriyadi, A.H., Rosa, L., et al. (2015). Reversible gating of smart plasmonic molecular traps using thermoresponsive polymers for single-molecule detection. Nat. Commun. 6: 8797. DOI: 10.1038/ncomms9797. View in Article CrossRef Google Scholar
[58]	Zheng, Y., Rosa, L., Thai, T., et al. (2015). Asymmetric gold nanodimer arrays: Electrostatic self-assembly and SERS activity. J. Mater. Chem. A 3: 240−249. DOI: 10.1039/C4TA05307A. View in Article CrossRef Google Scholar
[59]	Lu, Y., Chen, H., Cheng, H., et al. (2022). Plasmonic physical unclonable function labels based on tricolored silver nanoparticles: Implications for anticounterfeiting applications. ACS Appl. Nano Mater. 5: 9298−9305. DOI: 10.1021/acsanm.2c01622. View in Article CrossRef Google Scholar
[60]	Lu, Y., Cheng, H., Francis, P.S., et al. (2023). Nanomaterials and artificial intelligence in anti-counterfeiting. Intelligent Nanotechnology (Elsevier) 2023 : 361-398. DOI: 10.1016/B978-0-323-85796-3.00013-5. View in Article Google Scholar
[61]	Zhao, J., Liu, C., Li, Y., et al. (2020). Thermophoretic detection of exosomal microRNAs by nanoflares. J. Am. Chem. Soc. 142: 4996-5001. DOI: 10.1021/jacs.9b13960. View in Article CrossRef Google Scholar
[62]	Wu, X., Zhao, H., Natalia, A., et al. (2020). Exosome-templated nanoplasmonics for multiparametric molecular profiling. Sci. Adv. 6: eaba2556. DOI: 10.1126/sciadv.aba2556. View in Article CrossRef Google Scholar
[63]	Lu, Y., Chen, H., Cheng, H., et al. (2022). Plasmonic physical unclonable function labels based on tricolored silver nanoparticles: Implications for anticounterfeiting applications. ACS Appl. Nano Mater. 5: 9298−9305. DOI: 10.1021/acsanm.2c01622. View in Article CrossRef Google Scholar
[64]	Im, H., Shao, H., Park, Y.I., et al. (2014). Label-free detection and molecular profiling of exosomes with a nano-plasmonic sensor. Nat. Biotechnol. 32: 490-495. DOI: 10.1038/nbt.2886. View in Article CrossRef Google Scholar
[65]	Liang, K., Liu, F., Fan, J., et al. (2017). Nanoplasmonic quantification of tumour-derived extracellular vesicles in plasma microsamples for diagnosis and treatment monitoring. Nat. Biomed. Eng. 1: 0021. DOI: 10.1038/s41551-016-0021. View in Article CrossRef Google Scholar
[66]	Sriram, M., Markhali, B.P., Nicovich, P.R., et al. (2018) A rapid readout for many single plasmonic nanoparticles using dark-field microscopy and digital color analysis. Biosens. Bioelectron. 117 : 530-536. DOI: 10.1016/j.bios.2018.06.066. View in Article Google Scholar
[67]	Zhang, Y., Shuai, Z., Zhou, H., et al. (2018). Single-molecule analysis of microRNA and logic operations using a smart plasmonic Nanobiosensor. J. Am. Chem. Soc. 140: 3988-3993. DOI: 10.1021/jacs.7b12772. View in Article CrossRef Google Scholar
[68]	Lee, K., Fraser, K., Ghaddar, B., et al. (2018). Multiplexed profiling of single extracellular vesicles. ACS Nano 12: 494-503. DOI: 10.1021/acsnano.7b07060. View in Article CrossRef Google Scholar
[69]	Craw, P., and Balachandran, W. (2012). Isothermal nucleic acid amplification technologies for point-of-care diagnostics: A critical review. Lab Chip 12: 2469−2486. DOI: 10.1039/c2lc40100b. View in Article CrossRef Google Scholar
[70]	Zhao, Y., Xiang, J., Cheng, H., et al. (2021). Flexible photoelectrochemical biosensor for ultrasensitive microRNA detection based on concatenated multiplex signal amplification. Biosens. Bioelectron. 194: 113581. DOI: 10.1016/j.bios.2021.113581. View in Article CrossRef Google Scholar
[71]	Fu, R., and Xianyu, Y. (2023). Gold nanomaterials-implemented CRISPR-Cas systems for biosensing. Small 19 : e2300057. DOI: 10.1002/smll.202300057. View in Article Google Scholar
[72]	Long, Y., Zhou, X., and Xing, D. (2011). Sensitive and isothermal electrochemiluminescence gene-sensing of Listeria monocytogenes with hyperbranching rolling circle amplification technology. Biosens. Bioelectron. 26: 2897-2904. DOI: 10.1016/j.bios.2010.11.034. View in Article CrossRef Google Scholar
[73]	Long, Y., Zhou, X., and Xing, D. (2013). An isothermal and sensitive nucleic acids assay by target sequence recycled rolling circle amplification. Biosens. Bioelectron. 46: 102-107. DOI: 10.1016/j.bios.2013.02.003. View in Article CrossRef Google Scholar
[74]	Wu, C., Garden, P.M., and Walt, D.R. (2020). Ultrasensitive detection of attomolar protein concentrations by dropcast single molecule assays. J. Am. Chem. Soc. 142: 12314-12323. DOI: 10.1021/jacs.0c04331. View in Article CrossRef Google Scholar
[75]	Wang, W., Wu, J., Zhao, Z., et al. (2022). Ultrasensitive automatic detection of small molecules by membrane imaging of single molecule assays. ACS Appl. Mater. Interfaces 14: 54914-54923. DOI: 10.1021/acsami.2c15373. View in Article CrossRef Google Scholar
[76]	Jiang, C., Wang, G., Hein, R., et al. (2020). Antifouling strategies for selective in vitro and in vivo sensing. Chem. Rev. 120: 3852-3889. DOI: 10.1021/acs.chemrev.9b00739. View in Article CrossRef Google Scholar
[77]	Sun, J., Wang, G., Cheng, H., et al. (2022). An antifouling electrochemical aptasensor based on hyaluronic acid functionalized polydopamine for thrombin detection in human serum. Bioelectrochemistry 145: 108073. DOI: 10.1016/j.bioelechem.2022.108073. View in Article CrossRef Google Scholar
[78]	Yan, H., Wen, Y., Tian, Z., et al. (2023). A one-pot isothermal Cas12-based assay for the sensitive detection of microRNAs. Nat. Biomed. Eng. 7 : 1583-1601. DOI: 10.1038/s41551-023-01033-1. View in Article Google Scholar
[79]	Zhu, Z., Guo, Y., Wang, C., et al. (2023). An ultra-sensitive one-pot RNA-templated DNA ligation rolling circle amplification-assisted CRISPR/Cas12a detector assay for rapid detection of SARS-CoV-2. Biosens. Bioelectron. 228: 115179. DOI: 10.1016/j.bios.2023.115179. View in Article CrossRef Google Scholar
[80]	Wu, J., Lin, Z., Zou, Z., et al. (2022). Identifying the phenotypes of tumor-derived extracellular vesicles using size-coded affinity microbeads. J. Am. Chem. Soc. 144: 23483-23491. DOI: 10.1021/jacs.2c10042. View in Article CrossRef Google Scholar
[81]	Akama, K., Iwanaga, N., Yamawaki, K., et al. (2019). Wash- and amplification-free digital immunoassay based on single-particle motion analysis. ACS Nano 13: 13116-13126. DOI: 10.1021/acsnano.9b05917. View in Article CrossRef Google Scholar
[82]	Zhou, Y., Zhao, W., Feng, Y., et al. (2023). Artificial intelligence-assisted digital immunoassay based on a programmable-particle-decoding technique for multitarget ultrasensitive detection. Anal. Chem. 95: 1589−1598. DOI: 10.1021/acs.analchem.2c04703. View in Article CrossRef Google Scholar
[83]	Gong, F., Yang, Y., Shan, X., et al. (2023). A microchamber-free and enzyme-free digital assay based on ultrabright fluorescent microspheres. Sens. Actuators B Chem. 380: 133358. DOI: 10.1016/j.snb.2023.133358. View in Article CrossRef Google Scholar
[84]	Zhang, J., Li, Y., Chai, F., et al. (2022). Ultrasensitive point-of-care biochemical sensor based on metal-AIEgen frameworks. Sci. Adv. 8: eabo1874. DOI: 10.1126/sciadv.abo1874. View in Article CrossRef Google Scholar
[85]	Wang, X., and Yan, X.P. (2018). Analyte-driven self-assembly of graphene oxide sheets onto hydroxycamptothecin-functionalized upconversion nanoparticles for the determination of type I topoisomerases in cell extracts. Anal. Bioanal. Chem. 410: 6761-6769. DOI: 10.1007/s00216-018-1234-0. View in Article CrossRef Google Scholar
[86]	Li, Y.F., Lin, Z.Z., Hong, C.Y., et al. (2021). Histamine detection in fish samples based on indirect competitive ELISA method using iron-cobalt co-doped carbon dots labeled histamine antibody. Food Chem. 345: 128812. DOI: 10.1016/j.foodchem.2020.128812. View in Article CrossRef Google Scholar
[87]	Yang, H., Liu, Y., Guo, Z., et al. (2019). Hydrophobic carbon dots with blue dispersed emission and red aggregation-induced emission. Nat. Commun. 10: 1789. DOI: 10.1038/s41467-019-09830-6. View in Article CrossRef Google Scholar
[88]	Sun, C., Liu, L., Pérez, L., et al. (2022). Droplet-microfluidics-assisted sequencing of HIV proviruses and their integration sites in cells from people on antiretroviral therapy. Nat. Biomed. Eng. 6: 1004-1012. DOI: 10.1038/s41551-022-00864-8. View in Article CrossRef Google Scholar
[89]	Tian, T., Shu, B., Jiang, Y., et al. (2021). An ultralocalized Cas13a assay enables universal and nucleic acid amplification-free single-molecule RNA diagnostics. ACS Nano 15: 1167-1178. DOI: 10.1021/acsnano.0c08165. View in Article CrossRef Google Scholar
[90]	Mou, L., Hong, H., Xu, X., et al. (2021). Digital hybridization human papillomavirus assay with attomolar sensitivity without amplification. ACS Nano 15: 13077-13084. DOI: 10.1021/acsnano.1c02311. View in Article CrossRef Google Scholar
[91]	Liu, C., Xu, X., Li, B., et al. (2018). Single-exosome-counting immunoassays for cancer diagnostics. Nano Lett. 18: 4226-4232. DOI: 10.1021/acs.nanolett.8b01184. View in Article CrossRef Google Scholar
[92]	Yelleswarapu, V., Buser, J.R., Haber, M., et al. (2019). Mobile platform for rapid sub–picogram-per-milliliter, multiplexed, digital droplet detection of proteins. PNAS 116: 4489-4495. DOI: 10.1073/pnas.1814110116. View in Article CrossRef Google Scholar
[93]	Chen, Z.P., Yang, P., Yang, Z.Z., et al. (2022). One-step digital droplet auto-catalytic nucleic acid amplification with high-throughput fluorescence imaging and droplet tracking computation. Anal. Chem. 94: 9166-9175. DOI: 10.1021/acs.analchem.2c01754. View in Article CrossRef Google Scholar
[94]	Wang, Y., Shah, V., Lu, A., et al. (2021). Counting of enzymatically amplified affinity reactions in hydrogel particle-templated drops. Lab Chip 21 : 3438-3448. DOI: 10.1039/d1lc00344e. View in Article Google Scholar
[95]	Song, H., Chen, D.L., and Ismagilov, R.F. (2006). Reactions in droplets in microfluidic channels. Angew. Chem. Int. Ed. Engl. 45: 7336−7356. DOI: 10.1002/anie.200601554. View in Article CrossRef Google Scholar
[96]	Garstecki, P., Fuerstman, M.J., Stone, H.A., et al. (2006). Formation of droplets and bubbles in a microfluidic T-junction—scaling and mechanism of break-up. Lab Chip 6: 437-446. DOI: 10.1039/b510841a. View in Article CrossRef Google Scholar
[97]	Rissin, D.M., Kan, C.W., Campbell, T.G., et al. (2010). Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. Nat. Biotechnol. 28: 595−599. DOI: 10.1038/nbt.1641. View in Article CrossRef Google Scholar
[98]	Leirs, K., Dal Dosso, F., Perez-Ruiz, E., et al. (2022). Bridging the gap between digital assays and point-of-care testing: Automated, low cost, and ultrasensitive detection of thyroid stimulating hormone. Anal. Chem. 94: 8919−8927. DOI: 10.1021/acs.analchem.2c00480. View in Article CrossRef Google Scholar
[99]	Sun, J.J., Hu, J.M., Gou, T., et al. (2019). Power-free polydimethylsiloxane femtoliter-sized arrays for bead-based digital immunoassays. Biosens. Bioelectron. 139: 111339. DOI: 10.1016/j.bios.2019.111339. View in Article CrossRef Google Scholar
[100]	Li, Q., Bencherif, S.A., and Su, M. (2021). Edge-enhanced microwell immunoassay for highly sensitive protein detection. Anal. Chem. 93: 10292-10300. DOI: 10.1021/acs.analchem.1c01754. View in Article CrossRef Google Scholar
[101]	Song, Y., Zhao, J., Cai, T., et al. (2021). Machine learning-based cytokine microarray digital immunoassay analysis. Biosens. Bioelectron. 180: 113088. DOI: 10.1016/j.bios.2021.113088. View in Article CrossRef Google Scholar
[102]	Jeng, M.-J., Sharma, M., Sharma, L., et al. (2019). Raman spectroscopy analysis for optical diagnosis of oral cancer detection. J. Clin. Med. 8: 1313, DOI: 10.3390/jcm8091313. View in Article CrossRef Google Scholar
[103]	Alkhuder, K. (2023). Raman scattering-based optical sensing of chronic liver diseases. Photodiagn. Photodyn. Ther. 42: 103505. DOI: 10.1016/j.pdpdt.2023.103505. View in Article CrossRef Google Scholar
[104]	Lü, G., Zheng, X., Lü, X., et al. (2021). Label-free detection of echinococcosis and liver cirrhosis based on serum Raman spectroscopy combined with multivariate analysis. Photodiagn. Photodyn. Ther. 33: 102164. DOI: 10.1016/j.pdpdt.2020.102164. View in Article CrossRef Google Scholar
[105]	Wang, C., Zhang, T., Wang, P., et al. (2021). Bone metabolic biomarker-based diagnosis of type 2 diabetes osteoporosis by support vector machine. Ann. Transl. Med. 9 : 316. DOI: 10.21037/atm-20-3388. View in Article Google Scholar
[106]	Eke, C.S., Jammeh, E., Li, X., et al. (2020). Early detection of Alzheimer's disease with blood plasma proteins using support vector machines. IEEE J. Biomed. Health. Inf. 25: 218−226. DOI: 10.1109/JBHI.2020.2984355. View in Article CrossRef Google Scholar
[107]	Javeed, A., Zhou, S., Yongjian, L., et al. (2019). An intelligent learning system based on random search algorithm and optimized random forest model for improved heart disease detection. IEEE Access 7: 180235-180243. DOI: 10.1109/ACCESS.2019.2952107. View in Article CrossRef Google Scholar
[108]	Wang, S., Wang, Y., Wang, D., et al. (2020). An improved random forest-based rule extraction method for breast cancer diagnosis. Appl. Soft Comput. 86: 105941. DOI: 10.1016/j.asoc.2019.105941. View in Article CrossRef Google Scholar
[109]	Kumar, A., and Sodhi, S.S. (2020). Comparative analysis of gaussian filter, median filter and denoise autoenocoder. 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE 2020 : 45-51. DOI: 10.23919/INDIACom49435.2020.9083712. View in Article Google Scholar
[110]	Noor, A., Zhao, Y., Khan, R., et al. (2020). Median filters combined with denoising convolutional neural network for Gaussian and impulse noises. Multimed. Tools Appl. 79: 18553−18568. DOI: 10.1007/s11042-020-08657-4. View in Article CrossRef Google Scholar
[111]	Feng, N., Wang, S., Wei, L., et al. (2023). Artificial intelligence-based imaging transcoding system for multiplex screening of viable foodborne pathogens. Anal. Chem. 95: 8649-8659. DOI: 10.1021/acs.analchem.3c01142. View in Article CrossRef Google Scholar
[112]	Zhang, F., Kaufman, H.L., Deng, Y., et al. (2013). Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood. BMC Med. Genomics 6: 1−10, DOI: 10.1186/1755-8794-6-1. View in Article CrossRef Google Scholar
[113]	Zheng, R., Su, R., Xing, F., et al. (2022). Metabolic-dysregulation-based iEESI-MS reveals potential biomarkers associated with early-stage and progressive colorectal cancer. Anal. Chem. 94: 11821-11830. DOI: 10.1021/acs.analchem.2c02072. View in Article CrossRef Google Scholar
[114]	Li, Y., Pan, J., Zhou, N., et al. (2021). A random forest model predicts responses to infliximab in Crohn’s disease based on clinical and serological parameters. Scand. J. Gastroenterol. 56: 1030−1039. DOI: 10.1080/00365521.2021.1939411. View in Article CrossRef Google Scholar
[115]	Wager, S., and Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113: 1228-1242. DOI: 10.1080/01621459.2017.1319839. View in Article CrossRef Google Scholar
[116]	Tabib, S., and Larocque, D. (2020). Non-parametric individual treatment effect estimation for survival data with random forests. Bioinform. 36: 629-636. DOI: 10.1093/bioinformatics/btz602. View in Article CrossRef Google Scholar
[117]	Waljee, A.K., Joyce, J.C., Wang, S., et al. (2010). Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines. CGH 8: 143−150. DOI: 10.1016/j.cgh.2009.09.031. View in Article CrossRef Google Scholar
[118]	Ellahham, S. (2020). Artificial intelligence: The future for diabetes care. AM. J. Med. 133: 895−900. DOI: 10.1016/j.amjmed.2020.03.033. View in Article CrossRef Google Scholar
[119]	Blanco, F.C., Bigi, F., and Soria, M.A. (2014). Identification of potential biomarkers of disease progression in bovine tuberculosis. Vet. Immunol. Immunopathol. 160: 177-183. DOI: 10.1016/j.vetimm.2014.04.008. View in Article CrossRef Google Scholar
[120]	Rizk-Jackson, A., Stoffers, D., Sheldon, S., et al. (2011). Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington's disease using machine learning techniques. NeuroImage 56: 788-796. DOI: 10.1016/j.neuroimage.2010.04.273. View in Article CrossRef Google Scholar
[121]	Lin, C.-H., Chiu, S.-I., Chen, T.-F., et al. (2020). Classifications of neurodegenerative disorders using a multiplex blood biomarkers-based machine learning model. Int. J. Mol. Sci. 21: 6914. DOI: 10.3390/ijms21186914. View in Article CrossRef Google Scholar
[122]	Ghiti Moghadam, M., Lamers-Karnebeek, F.B., Vonkeman, H.E., et al. (2018). Multi-biomarker disease activity score as a predictor of disease relapse in patients with rheumatoid arthritis stopping TNF inhibitor treatment. PLoS One 13: e0192425. DOI: 10.1371/journal.pone.0192425. View in Article CrossRef Google Scholar
[123]	Diao, X., Li, X., Hou, S., et al. (2023). Machine learning-based label-free SERS profiling of exosomes for accurate fuzzy diagnosis of cancer and dynamic monitoring of drug therapeutic processes. Anal. Chem. 19: 7552-−7559. DOI: 10.1021/acs.analchem.3c00026. View in Article CrossRef Google Scholar
[124]	Guinney, J., Dienstmann, R., Wang, X., et al. (2015). The consensus molecular subtypes of colorectal cancer. Nat. Med. 21: 1350-1356. DOI: 10.1038/nm.3967. View in Article CrossRef Google Scholar
[125]	Menyhárt, O., and Győrffy, B. (2021). Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput. Struct. Biotechnol. J. 19: 949-960. DOI: 10.1016/j.csbj.2021.01.009. View in Article CrossRef Google Scholar
[126]	Khater, I.M., Nabi, I.R., and Hamarneh, G. (2020). A review of super-resolution single-molecule localization microscopy cluster analysis and quantification methods. Patterns 1: 100038. DOI: 10.1016/j.patter.2020.100038. View in Article CrossRef Google Scholar
[127]	Scalisi, S., Pisignano, D., Cella Zanacchi, F. (2023). Single-molecule localization microscopy goes quantitative. Microsc. Res. Tech. 86: 494-504. DOI: 10.1002/jemt.24281. View in Article CrossRef Google Scholar
[128]	Mahmoudpour M, Jouyban A, Soleymani J, et al. (2022) Rational design of smart nano-platforms based on antifouling-nanomaterials toward multifunctional bioanalysis. Adv. Colloid Interface Sci. 302, 102637. View in Article Google Scholar
[129]	Dou, B., Zhu, Z., Merkurjev, E., et al. (2023). Machine learning methods for small data challenges in molecular science. Chem. Rev. 123: 8736-8780. DOI: 10.1021/acs.chemrev.3c00189. View in Article CrossRef Google Scholar
[130]	Fan, W., Dong, Y., Ren, W., and Liu, C. (2023). Single microentity analysis-based ultrasensitive bioassays: Recent advances, applications, and perspectives. TrAC, Trends Anal. Chem. 162: 117035. DOI: 10.1016/j.trac.2023.117035. View in Article CrossRef Google Scholar
[131]	Wang, F., Li, Y., Han, Y., et al. (2019). Single-particle enzyme activity assay with spectral-resolved dark-field optical microscopy. Anal. Chem. 91: 6329-6339. DOI: 10.1021/acs.analchem.9b01300. View in Article CrossRef Google Scholar
[132]	Cohen, L., Cui, N., Cai, Y., et al. (2020). Single molecule protein detection with attomolar sensitivity using droplet digital enzyme-linked immunosorbent assay. ACS Nano 14: 9491-9501. DOI: 10.1021/acsnano.0c02378. View in Article CrossRef Google Scholar
[133]	Schmidt, K., Hageneder, S., Lechner, B., et al. (2022). Rolling circle amplification tailored for plasmonic biosensors: from ensemble to single-molecule detection. ACS Appl. Mater. Interfaces 14: 55017-55027. DOI: 10.1021/acsami.2c14500. View in Article CrossRef Google Scholar
[134]	Chen, D., Zhang, X., Zhu, L., et al. (2022). All on size-coded single bead set: a modular enrich-amplify-amplify strategy for attomolar level multi-immunoassay. Chem. Sci. 13: 3501-3506. DOI: 10.1039/D1SC07048G. View in Article CrossRef Google Scholar
[135]	You, M., Peng, P., Xue, Z., et al. (2021). A fast and ultrasensitive ELISA based on rolling circle amplification. Analyst 146: 2871-2877. DOI: 10.1039/D1AN00355K. View in Article CrossRef Google Scholar
[136]	Cui, J.Q., Liu, F.X., Park, H., et al. (2022). Droplet digital recombinase polymerase amplification (ddRPA) reaction unlocking via picoinjection. Biosens. Bioelectron. 202: 114019. DOI: 10.1016/j.bios.2022.114019. View in Article CrossRef Google Scholar
[137]	Xu, S., Wu, J., Chen, C., et al. (2021). A micro-chamber free digital biodetection method via the “sphere-labeled-sphere” strategy. Sens. Actuators B: Chem. 337: 129794. DOI: 10.1016/j.snb.2021.129794. View in Article CrossRef Google Scholar
[138]	Wang, F., Han, Y., Wang, S., et al. (2019). Single-particle LRET aptasensor for the sensitive detection of aflatoxin B(1) with upconversion nanoparticles. Anal. Chem. 91: 11856-11863. DOI: 10.1021/acs.analchem.9b02599. View in Article CrossRef Google Scholar
[139]	Farka, Z., Mickert, M.J., Hlavacek, A., et al. (2017). Single molecule upconversion-linked immunosorbent assay with extended dynamic range for the sensitive detection of diagnostic biomarkers. Anal. Chem. 89: 11825-11830. DOI: 10.1021/acs.analchem.7b03542. View in Article CrossRef Google Scholar
[140]	Guan, Z., Zou, Y., Zhang, M., et al. (2014). A highly parallel microfluidic droplet method enabling single-molecule counting for digital enzyme detection. Biomicrofluidics 8: 014110. DOI: 10.1063/1.4866766. View in Article CrossRef Google Scholar
[141]	Zhou, W. (2018). Development of immunomagnetic droplet-based digital immuno-PCR for the quantification of prostate specific antigen. Anal. Methods 10: 3690-3695. DOI: 10.1039/C8AY00921J. View in Article CrossRef Google Scholar
[142]	Maley, A.M., Garden, P.M., and Walt, D.R. (2020). Simplified digital enzyme-linked immunosorbent assay using tyramide signal amplification and fibrin hydrogels. ACS Sens. 5: 3037-3042. DOI: 10.1021/acssensors.0c01661. View in Article CrossRef Google Scholar
[143]	Hu, J., Gou, T., Wu, W., et al. (2019). Proximity ligation assays for precise quantification of femtomolar proteins in single cells using self-priming microfluidic dPCR chip. Anal. Chim. Acta 1076: 118-124. DOI: 10.1016/j.aca.2019.05.034. View in Article CrossRef Google Scholar
[144]	Morasso, C., Ricciardi, A., Sproviero, D., et al. (2022). Fast quantification of extracellular vesicles levels in early breast cancer patients by Single Molecule Detection Array (SiMoA). Breast Cancer Res Treat. 192: 65-74. DOI: 10.1007/s10549-021-06474-3. View in Article CrossRef Google Scholar

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Zhang W., Lu Y., Su C., et al., (2023). Confinement-guided ultrasensitive optical assay with artificial intelligence for disease diagnostics. The Innovation Medicine 1(2), 100023. https://doi.org/10.59717/j.xinn-med.2023.100023

Zhang W., Lu Y., Su C., et al., (2023). Confinement-guided ultrasensitive optical assay with artificial intelligence for disease diagnostics. The Innovation Medicine 1(2), 100023. https://doi.org/10.59717/j.xinn-med.2023.100023

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