Machine learning approach using electrochemical immunosensor data for precise classification of Opisthorchis viverrini infection

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Machine learning approach using electrochemical immunosensor data for precise classification of Opisthorchis viverrini infection

References

  1. Ahmad, A. et al. Intracellular synthesis of gold nanoparticles by a novel alkalotolerant actinomycete,Rhodococcusspecies. Nanotechnology 14, 824–828 (2003).

    Google Scholar 

  2. Parkin, D. M. The global health burden of infection-associated cancers in the year 2002. Int. J. Cancer. 118, 3030–3044 (2006).

    Google Scholar 

  3. Lun, Z. R. et al. Clonorchiasis: a key foodborne zoonosis in China. Lancet Infect. Dis. 5, 31–41 (2005).

    Google Scholar 

  4. Sripa, B. et al. Opisthorchiasis and Opisthorchis-associated cholangiocarcinoma in Thailand and Laos. Acta Trop. 120 (Suppl 1), S158–168 (2011).

    Google Scholar 

  5. Sithithaworn, P., Yongvanit, P., Duenngai, K., Kiatsopit, N. & Pairojkul, C. Roles of liver fluke infection as risk factor for cholangiocarcinoma. J. Hepato-Biliary-Pancreat Sci. 21, 301–308 (2014).

    Google Scholar 

  6. Qian, M. B. et al. Accuracy of the Kato-Katz method and formalin-ether concentration technique for the diagnosis of clonorchis sinensis, and implication for assessing drug efficacy. Parasit. Vectors. 6, 314 (2013).

    Google Scholar 

  7. Johansen, M. V., Sithithaworn, P., Bergquist, R. & Utzinger, J. Towards improved diagnosis of zoonotic trematode infections in Southeast Asia. Adv. Parasitol. 73, 171–195 (2010).

    Google Scholar 

  8. Wongratanacheewin, S., Sermswan, R. W. & Sirisinha, S. Immunology and molecular biology of opisthorchis Viverrini infection. Acta Trop. 88, 195–207 (2003).

    Google Scholar 

  9. Haswell-Elkins, M. R. et al. Immune responsiveness and parasite-specific antibody levels in human hepatobiliary disease associated with opisthorchis Viverrini infection. Clin. Exp. Immunol. 84, 213–218 (1991).

    Google Scholar 

  10. Bettazzi, F., Marrazza, G., Minunni, M., Palchetti, I. & Scarano, S. Chapter one – biosensors and related bioanalytical tools. In Comprehensive Analytical Chemistry (eds Palchetti, I., Hansen, P.-D. & Barceló, D.) 77 1–33 (Elsevier, 2017).

  11. Menon, S., Mathew, M. R., Sam, S., Keerthi, K. & Kumar, K. G. Recent advances and challenges in electrochemical biosensors for emerging and re-emerging infectious diseases. J. Electroanal. Chem. 878, 114596 (2020).

    Google Scholar 

  12. Aye, N. N. S. et al. Functionalized graphene oxide–antibody conjugate-based electrochemical immunosensors to detect opisthorchis Viverrini antigen in urine. Mater. Adv. https://doi.org/10.1039/D3MA01075A (2024).

    Google Scholar 

  13. Cui, F., Yue, Y., Zhang, Y., Zhang, Z. & Zhou, H. S. Advancing biosensors with machine learning. ACS Sens. https://doi.org/10.1021/acssensors.0c01424 (2020).

    Google Scholar 

  14. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials informatics: recent applications and prospects. Npj Comput. Mater. 3, 1–13 (2017).

    Google Scholar 

  15. Ali, S. et al. Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens. Sci. Rep. 8, 5920 (2018).

    Google Scholar 

  16. Zeng, Z. et al. Nonintrusive monitoring of mental fatigue status using epidermal electronic systems and Machine-Learning algorithms. ACS Sens. 5, 1305–1313 (2020).

    Google Scholar 

  17. Uwaya, G. E., Sagrado, S. & Bisetty, K. Smart electrochemical sensing of xylitol using a combined machine learning and simulation approach. Talanta Open. 6, 100144 (2022).

    Google Scholar 

  18. Baghdadi, N. A. et al. Advanced machine learning techniques for cardiovascular disease early detection and diagnosis. J. Big Data. 10, 144 (2023).

    Google Scholar 

  19. Hastie, T., Rosset, S., Zhu, J. & Zou, H. Multi-class adaboost. Stat. Interface. 2, 349–360 (2009).

    Google Scholar 

  20. Kumari, S., Kumar, D. & Mittal, M. An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. Int. J. Cogn. Comput. Eng. 2, 40–46 (2021).

    Google Scholar 

  21. Rainio, O., Teuho, J. & Klén, R. Evaluation metrics and statistical tests for machine learning. Sci. Rep. 14, 6086 (2024).

    Google Scholar 

  22. Srithai, C., Chuangchaiya, S., Jaichuang, S. & Idris, Z. M. Prevalence of opisthorchis Viverrini and its associated risk factors in the phon Sawan district of Nakhon phanom Province, Thailand. Iran. J. Parasitol. 16, 474–482 (2021).

    Google Scholar 

  23. Thaewnongiew, K. et al. Prevalence and risk factors for opisthorchis Viverrini infections in upper Northeast Thailand. Asian Pac. J. Cancer Prev. APJCP. 15, 6609–6612 (2014).

    Google Scholar 

  24. Nirmalraj, S. et al. Permutation feature importance-based fusion techniques for diabetes prediction. Soft Comput. https://doi.org/10.1007/s00500-023-08041-y (2023).

    Google Scholar 

  25. Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer, W. P. SMOTE: synthetic minority Over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002).

    Google Scholar 

  26. Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L. & Muller, P. A. Deep learning for time series classification: a review. Data Min. Knowl. Discov. 33, 917–963 (2019).

    Google Scholar 

  27. Heyrovský, J. The development of Polarographic analysis. Analyst 81, 189–192 (1956).

    Google Scholar 

  28. Wang, C. et al. Machine learning-assisted cell-imprinted electrochemical impedance sensor for qualitative and quantitative analysis of three bacteria. Sens. Actuators B Chem. 384, 133672 (2023).

    Google Scholar 

  29. Worasith, C. et al. Comparing the performance of urine and copro-antigen detection in evaluating opisthorchis Viverrini infection in communities with different transmission levels in Northeast Thailand. PLoS Negl. Trop. Dis. 13, e0007186 (2019).

    Google Scholar 

  30. Worasith, C. et al. Effects of day-to-day variation of opisthorchis Viverrini antigen in urine on the accuracy of diagnosing opisthorchiasis in Northeast Thailand. PLOS ONE. 17, e0271553 (2022).

    Google Scholar 

  31. Thomas, L. B., Mastorides, S. M., Viswanadhan, N. A., Jakey, C. E. & Borkowski, A. A. Artificial intelligence: review of current and future applications in medicine. Fed. Pract. Health Care Prof. VA. DoD PHS. 38, 527–538 (2021).

    Google Scholar 

  32. Christodoulou, E. et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110, 12–22 (2019).

    Google Scholar 

  33. van der Ploeg, T., Austin, P. C. & Steyerberg, E. W. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med. Res. Methodol. 14, 137 (2014).

    Google Scholar 

  34. Jiang, Y. et al. Cardiovascular disease prediction by machine learning algorithms based on cytokines in Kazakhs of China. Clin. Epidemiol. 13, 417–428 (2021).

    Google Scholar 

  35. Alsaleh, M. et al. Characterisation of the urinary metabolic profile of liver Fluke-Associated cholangiocarcinoma. J. Clin. Exp. Hepatol. 9, 657–675 (2019).

    Google Scholar 

  36. Sripa, B. et al. Toward integrated opisthorchiasis control in Northeast thailand: the Lawa project. Acta Trop. 141, 361–367 (2015).

    Google Scholar 

  37. Worasith, C. et al. Advances in the diagnosis of human opisthorchiasis: development of opisthorchis Viverrini antigen detection in urine. PLoS Negl. Trop. Dis. 9, e0004157 (2015).

    Google Scholar 

  38. Maitongngam, K., Tipayamongkholgul, M. & Kosaisavee, V. Diagnostic accuracy of opisthorchis Viverrini antigen methods for human opisthorchiasis: systematic review and Meta-analysis. Thai J. Public. Health. 53, 465–481 (2023).

    Google Scholar 

  39. Khuntikeo, N. et al. Current perspectives on opisthorchiasis control and cholangiocarcinoma detection in Southeast Asia. Front. Med. 5, (2018).

  40. Panch, T., Szolovits, P. & Atun, R. Artificial intelligence, machine learning and health systems. J. Glob Health. 8, 020303 (2018).

    Google Scholar 

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