Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Major depressive disorder (MDD) is a multidimensional psychiatric disorder that is estimated to affect around 350 million people worldwide. Generating valid and effective animal models of depression is critical and has been challenging for neuroscience researchers. For preclinical studies, models based on stress exposure, such as unpredictable chronic mild stress (uCMS), are amongst the most reliable and used, despite presenting concerns related to the standardization of protocols and time consumption for operators. To overcome these issues, we developed an automated system to expose rodents to a standard uCMS protocol. Here, we compared manual (uCMS) and automated (auCMS) stress-exposure protocols. The data shows that the impact of the uCMS exposure by both methods was similar in terms of behavioral (cognition, mood, and anxiety) and physiological (cell proliferation and endocrine variations) measurements. Given the advantages of time and standardization, this automated method represents a step forward in this field of preclinical research.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913774 | PMC |
| http://dx.doi.org/10.3390/cells12030381 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Automated Literature Screening for Hepatocellular Carcinoma Treatment Through Integration of 3 Large Language Models: Methodological Study.
JMIR Med Inform
September 2025
Department of Hepatobiliary and Vascular Surgery, First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Background: Primary liver cancer, particularly hepatocellular carcinoma (HCC), poses significant clinical challenges due to late-stage diagnosis, tumor heterogeneity, and rapidly evolving therapeutic strategies. While systematic reviews and meta-analyses are essential for updating clinical guidelines, their labor-intensive nature limits timely evidence synthesis.
Objective: This study proposes an automated literature screening workflow powered by large language models (LLMs) to accelerate evidence synthesis for HCC treatment guidelines.
Need Analysis of Clinician-Oriented Integrated Precision Oncology Decision Support Tools: Qualitative Descriptive Study.
JMIR Hum Factors
September 2025
College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
Background: The rapid advancement of next-generation sequencing has significantly expanded the landscape of precision medicine. However, health care professionals face increasing challenges in keeping pace with the growing body of oncological knowledge and integrating it effectively into clinical workflows. Precision oncology decision support (PODS) tools aim to assist clinicians in navigating this complexity, yet their current functionalities only partially address clinical needs.
View Article and Find Full Text PDFRETRACTED: Toncheva et al. Pathogenic Variants Associated with Rare Monogenic Diseases Established in Ancient Neanderthal and Denisovan Genome-Wide Data. 2023, , 727.
Genes (Basel)
August 2025
Medical Faculty, Department of Medical Genetics, Medical University of Sofia, Sofia 1000, Bulgaria.
The journal retracts the article "Pathogenic Variants Associated with Rare Monogenic Diseases Established in Ancient Neanderthal and Denisovan Genome-Wide Data" [...
View Article and Find Full Text PDFAI-Driven quality assurance in mammography: Enhancing quality control efficiency through automated phantom image evaluation in South Korea.
PLoS One
September 2025
Korea University College of Medicine, Seoul, Republic of Korea.
Purpose: To develop and validate a deep learning-based model for automated evaluation of mammography phantom images, with the goal of improving inter-radiologist agreement and enhancing the efficiency of quality control within South Korea's national accreditation system.
Materials And Methods: A total of 5,917 mammography phantom images were collected from the Korea Institute for Accreditation of Medical Imaging (KIAMI). After preprocessing, 5,813 images (98.
Automated segmentation of retinal vessel using HarDNet fully convolutional networks.
PLoS One
September 2025
School of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Computer-aided diagnostic (CAD) systems for color fundus images play a critical role in the early detection of fundus diseases, including diabetes, hypertension, and cerebrovascular disorders. Although deep learning has substantially advanced automatic segmentation techniques in this field, several challenges persist, such as limited labeled datasets, significant structural variations in blood vessels, and persistent dataset discrepancies, which continue to hinder progress. These challenges lead to inconsistent segmentation performance, particularly for small vessels and branch regions.
View Article and Find Full Text PDF