Wearable systems for monitoring biological signals have opened the door to personalized healthcare and have advanced a great deal over the past decade with the development of flexible electronics, efficient energy storage, wireless data transmission, and information processing technologies. As there are cumulative understanding of mechanisms underlying the mental processes and increasing desire for lifetime mental wellbeing, various wearable sensors have been devised to monitor the mental status from physiological activities, physical movements, and biochemical profiles in body fluids. This review summarizes the recent progress in wearable healthcare monitoring systems that can be utilized in mental healthcare, especially focusing on the biochemical sensors (i.e., biomarkers associated with mental status, sensing modalities, and device materials) and discussing their promises and challenges.

In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.

Neurotransmitters (NTs) are crucial regulatory molecules responsible for maintaining the neurophysiological functioning of the brain. Dysregulated levels of certain NTs, such as dopamine, serotonin, norepinephrine, epinephrine, glutamate, and gamma-aminobutyric acid, are often correlated with the pathogenesis of neurodegenerative diseases that involve the progressive and selective loss of structure or function of neuronal systems. Therefore, the identification and validation of relevant biomarkers are essential to diagnose these diseases much earlier. However, the quantitative analysis of NTs is challenging because of their dynamic release and presence of low concentrations. Accordingly, nanocomposite (NC)-based electrochemical sensors have been studied extensively and are gaining tremendous interest due to their high sensitivity, response rate, stability, portability, ease of use in point-of-care diagnostics, amenability to microprocessing, and low cost. In this review, we first briefly discuss the potential biomarkers of neurodegenerative diseases, NC-based electrochemical sensors and their advantages and disadvantages, and the properties of the NCs, which further increase the sensor performance. Finally, we summarized the future perspectives of NC-based electrochemical sensors in the clinical set-up for NTs detection to identify research gaps.

Photoacoustic agents are widely used in various theranostic applications. By evaluating the biodistribution obtained from photoacoustic images, the effectiveness of theranostic agents in terms of their delivery efficiency and treatment responses can be analyzed. Through this study, we evaluate and summarize the recent advances in photoacoustic-guided phototherapy, particularly in photothermal and photodynamic therapy. This overview can guide the future directions for theranostic development. Because of the recent applications of photoacoustic imaging in clinical trials, theranostic agents with photoacoustic monitoring have the potential to be translated into the clinical world.

Nanostructured materials offer the potential to drive future developments and applications of electrochemical devices, but are underutilized because their nanoscale cavities can impose mass transfer limitations that constrain electrochemical signal generation. Here, we report a new signal-generating mechanism that employs a molecular redox capacitor to enable nanostructured electrodes to amplify electrochemical signals even without an enhanced reactant mass transfer. The surface-tethered molecular redox capacitor engages diffusible reactants and products in redox-cycling reactions with the electrode. Such redox-cycling reactions are facilitated by the nanostructure that increases the probabilities of both reactant–electrode and product–redox-capacitor encounters (i.e., the nanoconfinement effect), resulting in substantial signal amplification. Using redox-capacitor-tethered Au nanopillar electrodes, we demonstrate improved sensitivity for measuring pyocyanin (bacterial metabolite). This study paves a new way of using nanostructured materials in electrochemical applications by engineering the reaction pathway within the nanoscale cavities of the materials.