Recent progress in (nano) biosensors: artificial intelligence (AI) application

Document Type : Systematic Review

Authors

1 Department of Biotechnology, Faculty of Advanced Sciences and Technologies,Tehran Medical Sciences, Islamic Azad University ,Tehran, Iran

2 Department of Nanobiotechnology, Faculty of Biological Science, Tarbiat Modares University, Tehran, Iran

3 Department of Biotechnology, Faculty of Basic Science, Ale-Taha institue of Higher Education, Tehran,Iran

Abstract
These days biosensors have worthy applications in different fields such as biomedicine, disease diagnosis, treatment monitoring, various aspects of the environment, food control, drug production, and assorted sides of medical science. Recently, different types of biosensors such as enzyme biosensors, immune, tissue, DNA, and thermal biosensors have been studied precisely by some research groups. These biosensors have many advantages such as simplicity in implementation, very high sensitivity, automatic performance, intrinsic and natural small size. Another valuable benefit of biosensors is that their high-affinity paring with biomolecules allows sensitive (high-sensitivity) and selective detection from a wide range of analytes. Artificial intelligence (AI) due to its high potency, if combined with biotechnology, like biosensors, can be effective in accurate prediction, diagnosis and treatment of some diseases, including cancer. Today, Machine learning (ML) as one of the branches of AI has become a beneficial tool in analyzing and categorizing obtained data from biosensors for bioanalysis. Using ML algorithms automates the complicated processes of extraction, processing, and assaying data achieved from biosensors. This article is a review for introducing and survey of various biosensors, their applications, and ways to apply them, focusing on cancer and Covid19 which are important diseases in the world obtained from previous studies, as a summary and providing information for researchers which working in this field.

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1. Rocchitta, G., et al., Enzyme biosensors for biomedical applications: Strategies for safeguarding analytical performances in biological fluids. Sensors, 2016. 16(6): p. 780.
2. Hasan, M., et al., Analgesic and anti-inflammatory activities of leaf extract of Mallotus repandus (Willd.) Muell. Arg. BioMed research international, 2014. 2014.
3. Ma, J., Z. Wang, and L.-W. Wang, Interplay between plasmon and single-particle excitations in a metal nanocluster. Nature communications, 2015. 6(1): p. 1-11.
4. Hooda, V., et al., Recent trends and perspectives in enzyme based biosensor development for the screening of triglycerides: a comprehensive review. Artificial cells, nanomedicine, and biotechnology, 2018. 46(sup2): p. 626-635.
5. Li, Y.-C.E. and I. Lee, The current trends of biosensors in tissue engineering. Biosensors, 2020. 10(8): p. 88.
6. Acha, V., et al., Tissue-based biosensors, in Recognition Receptors in Biosensors. 2010, Springer. p. 365-381.
7. Mehrotra, P., Biosensors and their applications–A review. Journal of oral biology and craniofacial research, 2016. 6(2): p. 153-159.
8. Woodcock, E.A. and S.J. Matkovich, Cardiomyocytes structure, function and associated pathologies. The international journal of biochemistry & cell biology, 2005. 37(9): p. 1746-1751.
9. Yuan, Y., C.Y.-L. Chung, and T.-F. Chan, Advances in optical mapping for genomic research. Computational and Structural Biotechnology Journal, 2020.
10. Caluori, G., et al., Non-invasive electromechanical cell-based biosensors for improved investigation of 3D cardiac models. Biosensors and Bioelectronics, 2019. 124: p. 129-135.
11. Jin, X., et al., Artificial intelligence biosensors: Challenges and prospects. Biosensors and Bioelectronics, 2020. 165: p. 112412.
12. Kourou, K., et al., Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 2015. 13: p. 8-17.
13. Malik, P., et al., Nanobiosensors: concepts and variations. International Scholarly Research Notices, 2013. 2013.
14. Sharifi, M., et al., Cancer diagnosis using nanomaterials based electrochemical nanobiosensors. Biosensors and Bioelectronics, 2019. 126: p. 773-784.
15. Wang, L., Screening and biosensor-based approaches for lung cancer detection. Sensors, 2017. 17(10): p. 2420.
16. Amirinejad, R., Z. Shirvani-Farsani, and S. Mohebbi, The application of DNA-conjugated gold nanoparticles to detect metabolites and nucleic acids in personalized medicine. Personalized Medicine Journal, 2021. 6(21): p. 23-25.
17. Svitel, J. and J. Katrl, Optical biosensors. Essays Biochem, 2016. 60: p. 91-100.
18. Kavita, V., DNA biosensors-a review. J. Bioeng. Biomed. Sci, 2017. 7(2): p. 222.
19. Huo, B., et al., Recent advances on functional nucleic acid-based biosensors for detection of food contaminants. Talanta, 2021. 222: p. 121565.
20. Hassan, R., et al., Multistate outbreak of Salmonella Paratyphi B variant L (+) tartrate (+) and Salmonella Weltevreden infections linked to imported frozen raw tuna: USA, March–July 2015. Epidemiology & Infection, 2018. 146(11): p. 1461-1467.
21. Carrascosa, L.G., A. Calle, and L.M. Lechuga, Label-free detection of DNA mutations by SPR: application to the early detection of inherited breast cancer. Analytical and bioanalytical chemistry, 2009. 393(4): p. 1173-1182.
22. Falkowski, P., Z. Lukaszewski, and E. Gorodkiewicz, Potential of surface plasmon resonance biosensors in cancer detection. Journal of Pharmaceutical and Biomedical Analysis, 2021. 194: p. 113802.
23. Malhotra, B.D. and M.A. Ali, Nanomaterials in biosensors: fundamentals and applications. Nanomaterials for Biosensors, 2018: p. 1.
24. Rodrigues, J.F., et al., Big data and machine learning for materials science. Discover Materials, 2021. 1(1): p. 1-27.
25. Cui, F., et al., Advancing biosensors with machine learning. ACS sensors, 2020. 5(11): p. 3346-3364.
26. Oliveira Jr, O.N. and M.C.F. Oliveira, Sensing and Biosensing in the World of Autonomous Machines and Intelligent Systems. Frontiers in Sensors, 2021: p. 12.
27. Nikhil, B., et al., Introduction to biosensors. Essays Biochem, 2016. 60(1): p. 1-8.
28. Lee, Y.H. and R. Mutharasan, 6.1 Overview: What Is a Biosensor?
29. Morales, M.A. and J.M. Halpern, Guide to selecting a biorecognition element for biosensors. Bioconjugate chemistry, 2018. 29(10): p. 3231-3239.
30. Khlebtsov, N.G. and L.A. Dykman, Optical properties and biomedical applications of plasmonic nanoparticles. Journal of Quantitative Spectroscopy and Radiative Transfer, 2010. 111(1): p. 1-35.
31. Mohebbi, S., et al., RGD-HK Peptide-functionalized gold nanorods emerge as targeted biocompatible nanocarriers for biomedical applications. Nanoscale research letters, 2019. 14(1): p. 13.
32. R, M., C. N, and F.B. A, Application of Biosensor. Bulletin of Scientific Research, 2019. 1(1): p. 34-40.
33. Singh, V., et al., Biosensor Developments: Application in crime detection. International Journal of Engineering and Technical Research, 2014: p. 163-166.
34. Haleem, A., et al., Biosensors applications in medical field: A brief review. Sensors International, 2021: p. 100100.
35. Gui, Q., et al., The application of whole cell-based biosensors for use in environmental analysis and in medical diagnostics. Sensors, 2017. 17(7): p. 1623.
36. K Hussain, K., et al., Biosensors and Diagnostics for Fungal Detection. Journal of Fungi, 2020. 6(4): p. 349.
37. Asif, M., et al., The role of biosensors in COVID-19 outbreak. Current Opinion in Electrochemistry, 2020.
38. Djaileb, A., et al., A rapid and quantitative serum test for SARS-CoV-2 antibodies with portable surface plasmon resonance sensing. 2020.
39. Peng, M., et al., Artificial intelligence application in COVID-19 diagnosis and prediction. 2020.
40. Shafiee, A., et al., Biosensing technologies for medical applications, manufacturing, and regenerative medicine. Current Stem Cell Reports, 2018. 4(2): p. 105-115.
41. Bohunicky, B. and S.A. Mousa, Biosensors: the new wave in cancer diagnosis. Nanotechnology, science and applications, 2011. 4: p. 1.
42. Khanmohammadi, A., et al., Electrochemical biosensors for the detection of lung cancer biomarkers: A review. Talanta, 2020. 206: p. 120251.
43. Xing, L., E.A. Krupinski, and J. Cai, Artificial intelligence will soon change the landscape of medical physics research and practice. Medical physics, 2018. 45(5): p. 1791-1793.
44. Siddique, S. and J.C. Chow, Artificial intelligence in radiotherapy. Reports of Practical Oncology and Radiotherapy, 2020. 25(4): p. 656-666.
45. Moore, J.A. and J.C. Chow, Recent progress and applications of gold nanotechnology in medical biophysics using artificial intelligence and mathematical modeling. Nano Express, 2021.
46. Haick, H. and N. Tang, Artificial intelligence in medical sensors for clinical decisions. ACS nano, 2021. 15(3): p. 3557-3567.
47. Belknap, R., et al., Feasibility of an ingestible sensor-based system for monitoring adherence to tuberculosis therapy. PloS one, 2013. 8(1): p. e53373.
48. Mimee, M., et al., An ingestible bacterial-electronic system to monitor gastrointestinal health. Science, 2018. 360(6391): p. 915-918.
49. Chai, P.R., et al., Utilizing an ingestible biosensor to assess real-time medication adherence. Journal of Medical Toxicology, 2015. 11(4): p. 439-444.
50. Zhu, P., H. Peng, and A.Y. Rwei, Flexible, wearable biosensors for digital health. Medicine in Novel Technology and Devices, 2022: p. 100118.
51. Ha, T., et al., A chest‐laminated ultrathin and stretchable E‐Tattoo for the measurement of electrocardiogram, seismocardiogram, and cardiac time intervals. Advanced Science, 2019. 6(14): p. 1900290.
52. Arora, N., A.K. Banerjee, and M.L. Narasu, The role of artificial intelligence in tackling COVID-19. 2020, Future Medicine. p. 717-724.
53. Kavakiotis, I., et al., Machine learning and data mining methods in diabetes research. Computational and structural biotechnology journal, 2017. 15: p. 104-116.
54. Vashistha, R., et al., Futuristic biosensors for cardiac health care: an artificial intelligence approach. 3 Biotech, 2018. 8(8): p. 1-11.