Developing a mathematical model for immunoassay interference that is more general represents a critical stride in the realm of clinical diagnostics, offering a comprehensive framework to understand and mitigate potential sources of interference in a diverse array of immunoassays. Immunoassays are vital tools in diagnosing diseases, monitoring patient health, and conducting biomedical research. However, the accuracy of immunoassay results can be compromised by various interfering substances present in biological samples. A more general mathematical model seeks to capture the underlying principles governing interference across a broad spectrum of immunoassays, making it adaptable to different analytes, sample matrices, and assay formats. The proposed mathematical model typically involves a system of equations that encapsulates the interactions between antibodies, antigens, and potential interfering substances. These equations account for the kinetics of binding and dissociation between these molecular entities, considering factors such as affinity constants, concentrations, and reaction rates. Unlike more specific models tailored to individual assays, a general model aims to incorporate a wider range of parameters, allowing for a more nuanced representation of the complexities inherent in various immunoassay systems.
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Journal of Applied & Computational Mathematics received 1282 citations as per Google Scholar report
