Stimulating Fungi Pleurotus ostreatus with Hydrocortisone

Abstract

Fungi cells can sense extracellular signals via reception, transduction, and response mechanisms, allowing them to communicate with their host and adapt to their environment. They feature effective regulatory protein expressions that enhance and regulate their response and adaptation to various triggers such as stress, hormones, physical stimuli such as light, and host factors. In our recent studies, we have shown that Pleurotus oyster fungi generate electrical potential impulses in the form of spike events in response to their exposure to environmental, mechanical, and chemical triggers, suggesting that the nature of stimuli may be deduced from the fungal electrical responses. In this study, we explored the communication protocols of fungi as reporters of human chemical secretions such as hormones, addressing whether fungi can sense human signals. We exposed Pleurotus oyster fungi to hydrocortisone, which was directly applied to the surface of a fungal-colonized hemp shavings substrate, and recorded the electrical activity of the fungi. Hydrocortisone is a medicinal hormone replacement that is similar to the natural stress hormone cortisol. Changes in cortisol levels released by the body indicate the presence of disease and can have a detrimental effect on physiological process regulation. The response of fungi to hydrocortisone was also explored further using X-rays to reveal changes in the fungi tissue, where receiving hydrocortisone by the substrate can inhibit the flow of calcium and, as a result, reduce its physiological changes. This research could open the way for future studies on adaptive fungal wearables capable of detecting human physiological states and biosensors built of living fungi.

Living Plants Ecosystem Sensing: A Quantum Bridge between Thermodynamics and Bioelectricity

“…In various demanding signal-processing systems and applications, using second-order statistics, such as the mean, variance, and correlation, becomes insufficient when the data deviate from a Gaussian distribution and the adaptive system is nonlinear [ 37 , 38 , 39 , 40 ]. In such scenarios, higher-order statistics are necessary to represent the characteristics of linear/nonlinear adaptive signal-processing systems with greater accuracy.…”

Pain Level and Pain-Related Behaviour Classification Using GRU-Based Sparsely-Connected RNNs

“…In several challenging signal processing systems [9], [12], [14] and applications, such as machine learning [56], when the data does not follow a Gaussian distribution and the adaptive system is nonlinear, second-order statistics (e.g., variance, correlation, and mean square error) are insufficient to derive adaptive features from the data. Such applications necessitate higher-order statistics of the data, in which the characteristics of linear/nonlinear adaptive signal processing systems, as well as machine learning applications, can be better represented by employing information-theoretic metrics such as entropy, Simpson diversity, expressiveness, and Lempel-Ziv complexity.…”

Bioelectrical Synchronisation of Spruce Trees during a Solar Eclipse

“…As in similar linear/nonlinear adaptive signal-processing systems, first-order statistics, such as mean, variance, and correlation, become inadequate when the data deviate from a Gaussian distribution and the adaptive system exhibits nonlinearity ( 24 ). Further, the concept of information theory suggests that the level of uncertainty determines the information value of data ( 25 ). Indeed, the data carry little information if an event is highly probable.…”