AI Article Synopsis
Article Abstract
Major depression is one of the most disabling disorders, yet the pathogenesis of this mental disorder is poorly understood. The techniques of proteomics provide us with powerful tools, while the animal models of depression enable research that cannot be performed on humans due to practical difficulties or ethical reasons. In this review, we summarize the characteristics of some most commonly applied rat models of depression, and explore what could be done with novel proteomic approaches to offer an insight to the pathogenesis of major depression, biomarker establishment and drug development.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1586/14789450.5.2.315 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Using Virtual Reality to Promote Self-Identity Reconstruction as the Main Focus of Therapy.
J Clin Psychol
January 2025
Department of Clinical Psychology and Psychobiology, The Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain.
Based on the repertory grid technique, we developed Explore Your Meanings (EYME), a digital platform that helps patients explore identity values and internal conflicts using virtual reality (VR). EYME was part of a research project treating depression in young adults, including 10 weekly, 1-h sessions aimed at changing personal constructs-cognitive schemas that shape how individuals interpret reality. We present the case of Mary, a 21-year-old woman diagnosed with persistent major depressive disorder and social phobia.
View Article and Find Full Text PDFMachine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.
Neuroradiology
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.
Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.
Prevalence of depression and association to socioeconomic position. Results from the Lolland-Falster Health Study.
Nord J Psychiatry
January 2025
Mental Health Services, Psychiatry East, Copenhagen University Hospital, Region Zealand, Denmark.
Purpose: To describe the prevalence of self-reported depression in a socioeconomically deprived area in Denmark and the association to socioeconomic position (SEP) defined by education, occupation, and being in financial strain.
Method: 13,955 adults completing the Major Depression Inventory (MDI) in the Lolland-Falster Health Study questionnaire were included.Multivariate logistic regression on symptoms of depression based on MDI sum score and ICD-10 scores, associated to education, occupation, and financial strain - unadjusted and adjusted for sex and age group.
Are neurasthenia and depression the same disease entity? An electroencephalography study.
BMC Psychiatry
January 2025
Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
Background: The neurasthenia-depression controversy has lasted for several decades. It is challenging to solve the argument by symptoms alone for syndrome-based disease classification. Our aim was to identify objective electroencephalography (EEG) measures that can differentiate neurasthenia from major depressive disorder (MDD).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!