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Fitting the data of an electrochemical impedance spectroscopy (EIS) typically requires manual estimation of the initial values before regression algorithms such as complex nonlinear least squares (CNLS) can be applied. This makes the success rate of the fitting dependent on the user input. Furthermore, the Randles circuit consists of parameters with substantially differing magnitudes (e.g. capacitors and resistors), which can also strongly affect the success rate of the fitting due to numerical effects. The aim of this work is to investigate methods addressing the described limitations of fitting. Therefore, a Python implementation performing a fit for EIS is benchmarked with an equivalent open source library. The examined implementation optionally includes the normalization of the parameter values, the standardization of the impedances and a pre-fit. Applying the same equivalent circuit without additional signal processing steps and with fixed initial values defined by midpoints of the value ranges, both implementations were able to fit 46.50% of the simulated database with different spectra. Applying the normalization of the parameter values (76.25%) or the same method with additional pre-fit (97.75%) lead to a significant improvement of the success rate. The standardization of impedances did not affect the success rate.

We propose a new strategy using a sandwich approach for the detection of two HF biomarkers: tumor necrosis factor-α (TNF-α) and interleukin-10 (IL-10). For this purpose, magnetic nanoparticles (MNPs) (MNPs@aminodextran) were biofunctionalized with monoclonal antibodies (mAbs) using bis (sulfosuccinimidyl) suberate (BS3) as a cross-linker for the pre-concentration of two biomarkers (TNF-α and IL-10). In addition, our ISFETs were biofunctionalized with polyclonal antibodies (pAbs) (TNF-α and IL-10). The biorecognition between pAbs immobilized on the ISFET and the pre-concentrate antigen (Ag) on MNPs was monitored using electrochemical impedance spectroscopy (EIS). Our developed ImmunoFET showed a low detection limit (0.03 pg/mL) toward our target analyte when compared to previously published electrochemical immunosensors. It showed a higher sensitivity than for other HF biomarkers. Finally, the standard addition method was used to determine the unknown concentration in artificial saliva. The results matched with the expected values well. [ABSTRACT FROM AUTHOR]

We propose a salivary Interleukin‐10 detection strategy as part of an easily integrable Lab‐on‐Chip and Point‐of‐Care IMFET for multi‐detection Heart Failure (HF) biomarkers. Our developed IMFET showed good linearity between increasing IL‐10 concentration and the charge transfer resistance in both standard solutions and real saliva samples with a LOD of 0.02 pg mL−1. Moreover, the cross‐selectivity study showed that the developed IMFET was highly selective towards IL‐10 against other HF biomarkers. The precision of our IMFET was also evaluated, and the difference between the determined IL‐10 concentration using the standard addition method and its expected one is<20 %. [ABSTRACT FROM AUTHOR]