A portable analog front-end system for label-free sensing of proteins using nanowell array impedance sensors.

Proteins are useful biomarkers for a wide range of applications such as cancer detection, discovery of vaccines, and determining exposure to viruses and pathogens. Here, we present a low-noise front-end analog circuit interface towards development of a portable readout system for the label-free sensing of proteins using Nanowell array impedance sensing with a form factor of approximately 35cm2. The electronic interface consists of a low-noise lock-in amplifier enabling reliable detection of changes in impedance as low as 0.1% and thus detection of proteins down to the picoMolar level. The sensitivity of our system is comparable to that of a commercial bench-top impedance spectroscope when using the same sensors. The aim of this work is to demonstrate the potential of using impedance sensing as a portable, low-cost, and reliable method of detecting proteins, thus inching us closer to a Point-of-Care (POC) personalized health monitoring system. We have demonstrated the utility of our system to detect antibodies at various concentrations and protein (45 pM IL-6) in PBS, however, our system has the capability to be used for assaying various biomarkers including proteins, cytokines, virus molecules and antibodies in a portable setting.

Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review.

Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.

On-chip Multiwell Plate Impedance Analysis of Microwell Array Sensor for Label-free Detection of Cytokines in Rat Serum.

We present a novel method for label-free detection of cytokines in serum after ten minutes of stabilization at nanomolar concentrations. Detection of proteins in blood using label-free impedance-based techniques is difficult due to high salt concentration of the matrix, which results in screening of the charge of the target proteins. The microwell array design provides enhanced electrochemical sensitivity by electric field focusing in the wells. We describe an on-chip multiwell plate sensing configuration where sensitivity benefits from the high salt concentration of the matrix and demonstrate robust performance through testing in rat serum.