Stress is linked to several diseases and health issues, such as heart diseases, headaches, diabetes mellitus, asthma, autoimmune diseases, cancer, and more. Cortisol secretion is associated with stress response or low blood-glucose concentration. Cortisol is one of the steroid hormones. Its secretion is implemented by the hypothalamus, pituitary and adrenal glands in human body. Its release is bound to the circadian cycle and has an influence on immune system response. Furthermore, cortisol can enhance blood sugar through gluconeogenesis process. It has been demonstrated that cortisol responsiveness can be adopted as a marker for weight gain and obesity risk. I ndividuals that are high-cortisol responders have an increased consumption of sugar and fat rich foods in comparison with low-cortisol responders. In addition, maternal high cortisol levels might be linked to a child’s neuroendocrine-immune functioning. Cortisol levels in human body can be determined from sweat, hair, blood, saliva, urine and interstitial fluids. It is the most prominent biomarker for physiological stress. In human body, cortisol can be protein bound or free, the latter being responsible for biological activity of cortisol.
Electrochemical biosensing is one of the most promising methods used for detection of neurotransmitters. It allows for very precise detection with low analyte detection limits and great sensitivity. Furthermore, most electrochemical sensors that use cortisol as analyte are non-invasive and can be implemented using the body fluids for detection. There is a growing need for point-of-care devices that would provide real time information on human health. Another possible improvement aided by the point-of-care devices represents a shift of medical supervision from hospital to home. Very accurate sensors can be obtained using carbon-based materials or metals due to their phenomenal conducting properties. Carbonbased sensors (screen printed multi-walled carbon nanotubes, carbon fiber, fullerene, graphene) are very suitable for cortisol detection. It has been demonstrated that the devices are low-cost and comparable with commercial counterparts. Most commonly used biofluids for cortisol detection are sweat and saliva because of the plethora of medical information they contain and non-invasive methods of collection. Complex sp2-sp3 composite structure is very widely used for electrochemical sensors, improving their response time and analyte detection limit.
Transition metal oxide nanoparticles also can be used very successfully in the development of the sensitive layer of a cortisol sensor, on their own or together with carbon-based materials. A sweat cortisol sensor based on ZnO nanorods integrated in conductive carbon yarns has been successfully used for cortisol detection. ZnO transition metal oxide has been chosen because of its special mechanical properties, good chemical stability, biocompatibility and non-toxic nature. TiO2 can be integrated with conductive carbon yarn to obtain a highly selective electrochemical sensor. Tin oxide (SnO2) can also enhance the electrochemical properties of flexible carbon fiber modified electrodes, together making an excellent electrochemical sweat cortisol detector. Silver electrodes also are used for cortisol detection. A sweat cortisol and glucose sensor based on inkjet printed silver and graphene electrodes was investigated in the study.
Ab initio density functional theory simulations can be very handy in analyzing the interaction of an analyte (volatile organic compounds, hormones, hazardous molecules, amino-acids, toxic gasses, biomarkers of viral infections, etc.) with the sensitive layer of the device under investigation. Structure of the active layer of the sensor can be tuned in such a way as to increase the sensitivity of the detector, lowering the analyte detection limit and making it highly selective, comparing the analyte response to interferent responses given by the sensor. Machine learning techniques also can significantly improve the selection of the sensor composition and its performance. Using high throughput calculations can be determined the electronic structure modifications induced by the presence of the biomarkers. For each biomolecule and sensitive layer configuration, a specific pattern emerges for the electrical conductivity, creating a possibility to optimize the structure of the sensor and response analysis.