The necessity of managing stress levels is becoming increasingly apparent as the world suffers from different kinds of stresses including the extent of pandemic, the corona virus disease 2019 . Cortisol, a clinically confirmed stress hormone related to depression and anxiety, affects individuals mentally and physically. However, current cortisol monitoring methods require expert personnel, large and complex machines, and long time for data analysis. Here, we present a flexible and wearable cortisol aptasensor for simple and rapid cortisol real-time monitoring. The sensing channel was produced by electrospinning conducting polyacrylonitrile (PAN) nanofibers (NFs) and subsequent vapor deposition of carboxylated poly(3,4-ethylenedioxythiophene) (PEDOT). The conjugation of the cortisol aptamer on the PEDOT-PAN NFs provided the critical sensing mechanism for the target molecule. The sensing test was performed with a liquid-ion gated field-effect transistor (FET) on a polyester (polyethylene terepthalate). The sensor performance showed a detection limit of 10 pM (<5 s) and high selectivity in the presence of interference materials at 100 times higher concentrations. The practical usage and real-time monitoring of the cortisol aptasensor with a liquid-ion gated FET system was demonstrated by successful transfer to the swab and the skin. In addition, the real-time monitoring of actual sweat by applying the cortisol aptasensor was also successful since the aptasensor was able to detect cortisol approximately 1 nM from actual sweat in a few minutes. This wearable biosensor platform supports the possibility of further application and on-site monitoring for changes of other numerous biomarkers.

Triplet-triplet annihilation upconversion (TTA-UC) has been attracting attention in various fields as a promising tool to efficiently generate shorter-wavelength photon than the incident light. Compared to conventional UC technologies (e.g.,...

Lateral flow assay (LFA) technology has recently received interest in the biochemical field since it is simple, low-cost, and rapid, while conventional laboratory test procedures are complicated, expensive, and time-consuming. In this paper, we propose a robust smartphone-based analyte detection method that estimates the amount of analyte on an LFA strip using a smartphone camera. The proposed method can maintain high estimation accuracy under various illumination conditions without additional devices, unlike conventional methods. The robustness and simplicity of the proposed method are enabled by novel image processing and machine learning techniques. For the performance analysis, we applied the proposed method to LFA strips where the target analyte is albumin protein of human serum. We use two sets of training LFA strips and one set of testing LFA strips. Here, each set consists of five strips having different quantities of albumin—10 femtograms, 100 femtograms, 1 picogram, 10 picograms, and 100 picograms. A linear regression analysis approximates the analyte quantity, and then machine learning classifier, support vector machine (SVM), which is trained by the regression results, classifies the analyte quantity on the LFA strip in an optimal way. Experimental results show that the proposed smartphone application can detect the quantity of albumin protein on a test LFA set with 98% accuracy, on average, in real time.

No abstract