The role of microRNAs in depression

Advances in biosensors for major depressive disorder diagnostic biomarkers

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

MiRNA research-The potential for understanding the multiple facets of anorexia nervosa

Anorexia nervosa (AN) has a multifaceted and complex pathology, yet major gaps remain in our understanding of factors involved in AN pathology. MicroRNAs (miRNAs) play a regulatory role in translating genes into proteins and help understand and treat diseases. An extensive literature review on miRNAs with AN and comorbidities has uncovered a significant lack in miRNA research. To demonstrate the importance of understanding miRNA deregulation, we surveyed the literature on depression and obesity providing examples of relevant miRNAs. For AN, no miRNA sequencing or array studies have been found, unlike other psychiatric disorders. For depression and obesity, screenings and mechanistic studies were conducted, leading to clinical studies to improve understanding of their regulatory influences. MiRNAs are promising targets for studying AN due to their role as signaling molecules, involvement in psychiatric-metabolic axes, and potential as biomarkers. These characteristics offer valuable insights into the disease's etiology and potential new treatment options. The first miRNA-based treatment for rare metabolic disorders has been approved by the FDA and it is expected that these advancements will increase in the next decade. MiRNA research in AN is essential to examine its role in the development, manifestation, and progression of the disease. PUBLIC SIGNIFICANCE: The current understanding of the development and treatment of AN is insufficient. miRNAs are short regulatory sequences that influence the translation of genes into proteins. They are the subject of research in various diseases, including both metabolic and psychiatric disorders. Studying miRNAs in AN may elucidate their causal and regulatory role, uncover potential biomarkers, and allow for future targeted treatments.

Astroglial Dysfunctions in Mood Disorders and Rodent Stress Models: Consequences on Behavior and Potential as Treatment Target

Astrocyte dysfunctions have been consistently observed in patients affected with depression and other psychiatric illnesses. Although over the years our understanding of these changes, their origin, and their consequences on behavior and neuronal function has deepened, many aspects of the role of astroglial dysfunction in major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) remain unknown. In this review, we summarize the known astroglial dysfunctions associated with MDD and PTSD, highlight the impact of chronic stress on specific astroglial functions, and how astroglial dysfunctions are implicated in the expression of depressive- and anxiety-like behaviors, focusing on behavioral consequences of astroglial manipulation on emotion-related and fear-learning behaviors. We also offer a glance at potential astroglial functions that can be targeted for potential antidepressant treatment.

Converged avenues: depression and Alzheimer's disease- shared pathophysiology and novel therapeutics

Depression, a highly prevalent disorder affecting over 280 million people worldwide, is comorbid with many neurological disorders, particularly Alzheimer's disease (AD). Depression and AD share overlapping pathophysiology, and the search for accountable biological substrates made it an essential and intriguing field of research. The paper outlines the neurobiological pathways coinciding with depression and AD, including neurotrophin signalling, the hypothalamic-pituitary-adrenal axis (HPA), cellular apoptosis, neuroinflammation, and other aetiological factors. Understanding overlapping pathways is crucial in identifying common pathophysiological substrates that can be targeted for effective management of disease state. Antidepressants, particularly monoaminergic drugs (first-line therapy), are shown to have modest or no clinical benefits. Regardless of the ineffectiveness of conventional antidepressants, these drugs remain the mainstay for treating depressive symptoms in AD. To overcome the ineffectiveness of traditional pharmacological agents in treating comorbid conditions, a novel therapeutic class has been discussed in the paper. This includes neurotransmitter modulators, glutamatergic system modulators, mitochondrial modulators, antioxidant agents, HPA axis targeted therapy, inflammatory system targeted therapy, neurogenesis targeted therapy, repurposed anti-diabetic agents, and others. The primary clinical challenge is the development of therapeutic agents and the effective diagnosis of the comorbid condition for which no specific diagnosable scale is present. Hence, introducing Artificial Intelligence (AI) into the healthcare system is revolutionary. AI implemented with interdisciplinary strategies (neuroimaging, EEG, molecular biomarkers) bound to have accurate clinical interpretation of symptoms. Moreover, AI has the potential to forecast neurodegenerative and psychiatric illness much in advance before visible/observable clinical symptoms get precipitated.