Electrochemical biosensor platform for TNF-α cytokines detection in both artificial and human saliva: Heart failure

In the last decades, the possibility to assess physiological states, monitor disease onset and progression, as well as monitor post-treatment therapeutic outcomes through a non-invasive approach became one of the most desirable goals for healthcare research. In this respect, saliva analysis can represent a very interesting biological matrix which has high potential for the surveillance of general health and disease. Potentially important biomarkers are increased in saliva during local and systemic inflammation. In the present study, we have developed a highly sensitive biosensor for TNF-alpha detection inhuman saliva. Therefore, a fully integrated electrochemical biosensor platform was developed in order to increase the sensitivity of detection, decrease the time of analysis, and to simultaneously detect varying cytokine biomarkers using eight gold working microelectrodes (WE). The monoclonal antibodies (mAb) anti-human Tumor Necrosis Factor-alpha (anti-TNF-alpha) were immobilized onto gold WE through functionalization with carboxyl diazonium. Cyclic voltammetry (CV) was applied during the microelectrode functionalization process to characterize the gold microelectrode surface properties. Finally, electrochemical impedance spectroscopy (EIS) combined with standard added method was used to characterize the modified gold microelectrodes as well as for the detection of TNF-alpha both in standard solution and in artificial saliva. The high selectivity of the biosensor platform was demonstrated by analysing solution containing other cytokines may representing possible interferences. TNF-alpha cytokines were analysed in PBS buffer, artificial saliva and real human saliva within the range 1-100 pg/mL which is the critical range for patients suffering from heart failure. The first preliminary results of EIS analyses in human saliva gave a result of 3.1 pg/mL which is very promising for rapid analysis for cytokines detection. (C) 2017 Elsevier B.V. All rights reserved.