Background Virtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the vast amounts of digital log that is acquired by digital technologies develop and using computational analysis techniques. Objective This study was conducted to evaluate the mood state or episode, activity, sleep, light exposure, and heart rate during a period of about 2 years by acquiring various digital log data through wearable devices and smartphone apps as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms. Methods We performed a prospective observational cohort study on 55 patients with mood disorders (major depressive disorder [MDD] and bipolar disorder type 1 [BD I] and 2 [BD II]) for 2 years. A smartphone app for self-recording daily mood scores and detecting light exposure (using the installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest. Results The mood state prediction accuracies for the next 3 days in all patients, MDD patients, BD I patients, and BD II patients were 65%, 65%, 64%, and 65% with 0.7, 0.69, 0.67, and 0.67 area under the curve (AUC) values, respectively. The accuracies of all patients for no episode (NE), depressive episode (DE), manic episode (ME), and hypomanic episode (HME) were 85.3%, 87%, 94%, and 91.2% with 0.87, 0.87, 0.958, and 0.912 AUC values, respectively. The prediction accuracy in BD II patients was distinctively balanced as high showing 82.6%, 74.4%, and 87.5% of accuracy (with generally good sensitivity and specificity) with 0.919, 0.868, and 0.949 AUC values for NE, DE, and HME, respectively. Conclusions On the basis of the theoretical basis of chronobiology, this study proposed a good model for future research by developing a mood prediction algorithm using machine learning by processing and reclassifying digital log data. In addition to academic value, it is expected that this study will be of practical help to improve the prognosis of patients with mood disorders by making it possible to apply actual clinical application owing to the rapid expansion of digital technology.

Summary The value of polymers is manifested in their vital use as building blocks in material and life sciences. Ribonucleic acid (RNA) is a polynucleic acid, but its polymeric nature in materials and technological applications is often overlooked due to an impression that RNA is seemingly unstable. Recent findings that certain modifications can make RNA resistant to RNase degradation while retaining its authentic folding property and biological function, and the discovery of ultra-thermostable RNA motifs have adequately addressed the concerns of RNA unstability. RNA can serve as a unique polymeric material to build varieties of nanostructures including nanoparticles, polygons, arrays, bundles, membrane, and microsponges that have potential applications in biomedical and material sciences. Since 2005, more than a thousand publications on RNA nanostructures have been published in diverse fields, indicating a remarkable increase of interest in the emerging field of RNA nanotechnology. In this review, we aim to: delineate the physical and chemical properties of polymers that can be applied to RNA; introduce the unique properties of RNA as a polymer; review the current methods for the construction of RNA nanostructures; describe its applications in material, biomedical and computer sciences; and, discuss the challenges and future prospects in this field.

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