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Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids.
Habibi, Mohammad Mehdi; Mousavi, Mitra; Shekofteh-Gohari, Maryam; Parsaei-Khomami, Anita; Hosseini, Monireh-Alsadat; Haghani, Elnaz; Salahandish, Razieh; Ghasemi, Jahan B.
Afiliación
  • Habibi MM; School of Chemistry, University College of Science, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.
  • Mousavi M; School of Chemistry, University College of Science, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.
  • Shekofteh-Gohari M; School of Chemistry, University College of Science, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.
  • Parsaei-Khomami A; School of Chemistry, University College of Science, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.
  • Hosseini MA; School of Chemistry, University College of Science, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.
  • Haghani E; Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada.
  • Salahandish R; Department of Electrical Engineering and Computer Science, Biomedical Engineering Program, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada.
  • Ghasemi JB; Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada. raziehs@yorku.ca.
Sci Rep ; 14(1): 8099, 2024 04 06.
Article en En | MEDLINE | ID: mdl-38582770
ABSTRACT
The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor's performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor's efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Nanotubos de Carbono / Nanocompuestos Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Nanotubos de Carbono / Nanocompuestos Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido