Preliminary study for simultaneous detection and quantification of androgenic anabolic steroids using ELISA and
Daniel Calvo1, Núria Tort, J Pablo Salvador
1Intelligent Signal Processing Group, Department of Electronics, University of Barcelona, Martí i Franques 1, 08028 Barcelona, Spain. dcalvo@el.ub.es
This study introduces a novel enzyme-linked immunosorbent assay (ELISA) method for detecting multiple anabolic androgenic steroids. The approach achieves 90.6% accuracy in identifying up to four steroids simultaneously, even with cross-reactivity challenges.
Area of Science:
- * Analytical Chemistry
- * Biochemistry
- * Forensic Science
Background:
- * Accurate detection of anabolic androgenic steroids (AAS) is crucial for doping control and clinical diagnostics.
- * Existing methods often struggle with simultaneous detection of multiple AAS or are affected by cross-reactivity.
- * Development of a sensitive and specific multidetection assay for AAS is needed.
Purpose of the Study:
- * To develop and validate a multidetection method for identifying and quantifying anabolic androgenic steroids (AAS).
- * To combine multiple competitive enzyme-linked immunosorbent assay (ELISA) formats with advanced data analysis for enhanced specificity.
- * To establish a reliable assay capable of simultaneous detection and concentration prediction of multiple AAS.
Main Methods:
- * Utilized multiple competitive enzyme-linked immunosorbent assay (ELISA) kits with distinct cross-reactivity profiles.
- * Applied multivariate data analysis, specifically principal component analysis (PCA).
- * Implemented a novel K-nearest line classifier for data interpretation and steroid identification.
Main Results:
- * Achieved simultaneous detection of up to four different anabolic androgenic steroids.
- * Demonstrated accurate detection within a concentration range of 0.1 to 316.2 nM.
- * Reported a total correct detection rate of 90.6%, robust even in the presence of cross-reactivities.
- * Developed a concentration prediction methodology yielding satisfactory results.
Conclusions:
- * The proposed method offers a significant advancement in the multidetection, identification, and quantification of AAS.
- * The combination of competitive ELISAs and multivariate analysis provides a powerful tool for complex steroid mixture analysis.
- * This approach shows promise for applications in sports doping control and clinical monitoring requiring simultaneous AAS detection.
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