Endogenous depression represents a severe mental health condition projected to become one of the worldwide leading causes of years lived with disability. The currently available clinical and non-clinical interventions designed to alleviate endogenous depression-associated symptoms encounter a series of inconveniences, from the lack of intervention effectiveness and medication adherence to unpleasant side effects. In addition, depressive individuals tend to be more frequent users of primary care units, which markedly affects the overall treatment costs. In parallel with the growing incidence of endogenous depression, researchers in sleep science have discovered multiple links between rapid eye movement (REM) sleep patterns and endogenous depression. Recent findings suggest that prolonged periods of REM sleep are associated with different psychiatric disorders, including endogenous depression. In addition, a growing body of experimental work confidently describes REM sleep deprivation (REM-D) as the underlying mechanism of most pharmaceutical antidepressants, proving its utility as either an independent or adjuvant approach to alleviating the symptoms of endogenous depression. In this regard, REM-D is currently being explored for its potential value as a sleep intervention-based method for improving the clinical management of endogenous depression. Therefore, this narrative review represents a comprehensive inventory of the currently available evidence supporting the potential use of REM-D as a reliable, non-pharmaceutical approach for treating endogenous depression, or as an adjuvant practice that could improve the effectiveness of currently used medication.

On 24 February 2022, the Russian Federation invaded Ukraine, starting a military conflict that soon turned into a full-scale war. The Romanians have been actively involved in helping their neighbors, causing the accumulation of emotional and mental pressure upon the Romanian population exposed to such a close military conflict. This cross-sectional study assessed, through an online survey (1586 adult Romanian residents), the primary coping mechanisms, quality of life and anxiety levels in response to the psychological trauma associated with the Russo—Ukrainian war. Based on the results, focusing on and venting emotions along with behavioral disengagement were the coping strategies that had the strongest negative impact on anxiety and well-being. On the other hand, positive reinterpretation and growth were associated with less anxiety, a higher degree of overall health and better quality of life. To the best of our knowledge, this study is the first of its kind to be performed among the Romanian population so far. Thus, we equip mental health practitioners with the tools (real-life evidence data) that will allow them to establish a more meaningful doctor—patient relationship, maximizing therapy results.

Infections can be triggered by a wide range of pathogens. However, there are few strains of bacteria that cause illness, but some are quite life-threatening. Likewise, viral infections are responsible for many human diseases, usually characterized by high contagiousness. Hence, as bacterial and viral infections can both cause similar symptoms, it can be difficult to determine the exact cause of a specific infection, and this limitation is critical. However, recent scientific advances have geared us up with the proper tools required for better diagnoses. Recent discoveries have confirmed the involvement of non-coding RNAs (ncRNAs) in regulating the pathogenesis of certain bacterial or viral infections. Moreover, the presence of reactive oxygen species (ROS) is also known as a common infection trait that can be used to achieve a more complete description of such pathogen-driven conditions. Thus, this opens further research opportunities, allowing scientists to explore infection-associated genetic patterns and develop better diagnosis and treatment methods. Therefore, the aim of this review is to summarize the current knowledge of the implication of ncRNAs and ROS in bacterial and viral infections, with great emphasis on their symmetry but, also, on their main differences.

This study investigates the correlation between REM sleep patterns, as measured by the Apple Watch, and depressive symptoms in an undiagnosed population. Employing the Apple Watch for data collection, REM sleep duration and frequency were monitored over a specified period. Concurrently, participants’ depressive symptoms were evaluated using standardized questionnaires. The analysis, primarily using Spearman’s correlation, revealed noteworthy findings. A significant correlation was observed between an increased REM sleep proportion and higher depressive symptom scores, with a correlation coefficient of 0.702, suggesting a robust relationship. These results highlight the potential of using wearable technology, such as the Apple Watch, in early detection and intervention for depressive symptoms, suggesting that alterations in REM sleep could serve as preliminary indicators of depressive tendencies. This approach offers a non-invasive and accessible means to monitor and potentially preempt the progression of depressive disorders. This study’s implications extend to the broader context of mental health, emphasizing the importance of sleep assessment in routine health evaluations, particularly for individuals exhibiting early signs of depressive symptoms.

Background: Rapid-eye-movement (REM) sleep disinhibition—shorter REM latency and a larger nightly REM fraction—is a well-described laboratory correlate of major depression. Whether the same pattern can be captured efficiently with consumer wearables in everyday settings remains unclear. We therefore quantified REM latency and proportion of REM sleep out of total sleep duration (labeled “REM sleep coefficient”) from Apple Watch recordings and examined their association with depressive symptoms. Methods: 191 adults wore an Apple Watch for 15 consecutive nights while a custom iOS app streamed raw accelerometry and heart-rate data. Sleep stages were scored with a neural-network model previously validated against polysomnography. REM latency and REM sleep coefficient were averaged per participant. Depressive severity was assessed twice with the Beck Depression Inventory and averaged. Descriptive statistics, normality tests, Spearman correlations, and ordinary-least-squares regressions were performed. Results: Mean ± SD values were BDI 13.52 ± 6.79, REM sleep coefficient 24.05 ± 6.52, and REM latency 103.63 ± 15.44 min. REM latency correlated negatively with BDI (Spearman ρ = −0.673, p < 0.001), whereas REM sleep coefficient correlated positively (ρ = 0.678, p < 0.001). Combined in a bivariate model, the two REM metrics explained 62% of variance in depressive severity. Conclusions: Wearable-derived REM latency and REM proportion jointly capture a large share of depressive-symptom variability, indicating their potential utility as accessible digital biomarkers. Larger longitudinal and interventional studies are needed to determine whether modifying REM architecture can alter the course of depression.

Auditory verbal hallucinations are one of the most common symptoms in schizophrenia, contributing substantially to distress, functional impairment, and relapse risk. Existing therapeutic approaches, including pharmacological treatment and structured psychotherapies such as cognitive behavioral therapy for psychosis and AVATAR therapy, show promising clinical outcomes. However, scalability remains limited due to resource intensity and reliance on specialized personnel. Against this background, we describe the rationale, prototype architecture, and prospective validation roadmap for GOBLIN—a digital platform prototype combining personalized voice conversion, webcam-based eye-tracking, and large-language-model-assisted clinical documentation and decision support. The platform is intended to support clinician-supervised therapeutic workflows and between-visit monitoring following prospective validation. GOBLIN is intended to investigate whether an integrated digital workflow could address key limitations of current care models in schizophrenia and psychotic disorders, including fragmented follow-up, limited responsiveness to rapid symptom changes, and lack of scalable intervention tools. Our conceptual prototype is the first step toward leveraging the potential of digital, AI-driven platforms to complement existing treatment modalities and improve long-term management of psychosis-related symptoms. The system’s modules are intended for clinician-supervised research workflows and remain investigational. This manuscript does not report completed clinical validation, therapeutic efficacy, diagnostic performance, relapse-prediction performance, or validated safety outcomes. Such capabilities remain to be evaluated prospectively through staged technical, usability, safety, and clinical studies before clinical implementation can be considered.