An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response.

Non-faradaic electrochemical impedimetric profiling of procalcitonin and C-reactive protein as a dual marker biosensor for early sepsis detection.

In this work, we demonstrate a robust, dual marker, biosensing strategy for specific and sensitive electrochemical response of Procalcitonin and C-reactive protein in complex body fluids such as human serum and whole blood for the detection of sepsis. Enhanced sensitivity is achieved by leveraging the physicochemical properties of zinc oxide at the electrode-solution interface. Characterization techniques such as SEM, EDAX, AFM, FTIR and fluorescence microscopy were performed to ensure a suitable biosensing surface.

Multiplexed electrochemical detection of three cardiac biomarkers cTnI, cTnT and BNP using nanostructured ZnO-sensing platform.

Aim: Development of a label-free multiplexed point-of-care diagnostic device for a panel of cardiac biomarkers - cardiac troponin-T (cTnT), troponin-I (cTnI) and B-type natriuretic peptide (BNP).

Methods: A nonfaradaic electrochemical immunoassay designed with anisotropic high surface area ZnO nanostructures grown using low-temperature hydrothermal methods was selectively immobilized with capture antibodies. Multiplexed detection in human serum using ZnO nanostructures based on complementary electrochemical measurement techniques - electrochemical impedance spectroscopy and Mott-Schottky.