Potential biomarkers of emotional stress induced neurodegeneration

Electrochemical sensors for cortisol detection: Principles, designs, fabrication, and characterisation

Psychological stress is a major factor contributing to health discrepancies among individuals. Sustained exposure to stress triggers signalling pathways in the brain, which leading to the release of stress hormones in the body. Cortisol, a steroid hormone, is a significant biomarker for stress management due to its responsibility in the body's reply to stress. The release of cortisol in bloodstream prepares the body for a "fight or flight" response by increasing heart rate, blood pressure, metabolism, and suppressing the immune system. Detecting cortisol in biological samples is crucial for understanding its role in stress and personalized healthcare. Traditional techniques for cortisol detection have limitations, prompting researchers to explore alternative strategies. Electrochemical sensing has emerged as a reliable method for point-of-care (POC) cortisol detection. This review focuses on the progress made in electrochemical sensors for cortisol detection, covering their design, principle, and electroanalytical methodologies. The analytical performance of these sensors is also analysed and summarized. Despite significant advancements, the development of electrochemical cortisol sensors faces challenges such as biofouling, sample preparation, sensitivity, flexibility, stability, and recognition layer performance. Therefore, the need to develop more sensitive electrodes and materials is emphasized. Finally, we discussed the potential strategies for electrode design and provides examples of sensing approaches. Moreover, the encounters of translating research into real world applications are addressed.

An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress

Life in modern societies is fast-paced and full of stress-inducing demands. The development of stress monitoring methods is a growing area of research due to the personal and economic advantages that timely detection provides. Studies have shown that speech-based features can be utilised to robustly predict several physiological markers of stress, including emotional state, continuous heart rate, and the stress hormone, cortisol. In this contribution, we extend previous works by the authors, utilising three German language corpora including more than 100 subjects undergoing a Trier Social Stress Test protocol. We present cross-corpus and transfer learning results which explore the efficacy of the speech signal to predict three physiological markers of stress—sequentially measured saliva-based cortisol, continuous heart rate as beats per minute (BPM), and continuous respiration. For this, we extract several features from audio as well as video and apply various machine learning architectures, including a temporal context-based Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). For the task of predicting cortisol levels from speech, deep learning improves on results obtained by conventional support vector regression—yielding a Spearman correlation coefficient (ρ) of 0.770 and 0.698 for cortisol measurements taken 10 and 20 min after the stress period for the two corpora applicable—showing that audio features alone are sufficient for predicting cortisol, with audiovisual fusion to an extent improving such results. We also obtain a Root Mean Square Error (RMSE) of 38 and 22 BPM for continuous heart rate prediction on the two corpora where this information is available, and a normalised RMSE (NRMSE) of 0.120 for respiration prediction (−10: 10). Both of these continuous physiological signals show to be highly effective markers of stress (based on cortisol grouping analysis), both when available as ground truth and when predicted using speech. This contribution opens up new avenues for future exploration of these signals as proxies for stress in naturalistic settings.

Monoamine Oxidase as a Potential Biomarker of the Efficacy of Treatment of Mental Disorders

The review summarizes the results of our own studies and published data on the biological markers of psychiatric disorders, with special emphasis on the activity of platelet monoamine oxidase. Pharmacotherapy studies in patients with the mixed anxiety-depressive disorder and first episode of schizophrenia have shown that the activity of platelet monoamine oxidase could serve as a potential biomarker of the efficacy of therapeutic interventions in these diseases.