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Filename: controllers/Detail.php
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Function: _error_handler
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Filename: controllers/Detail.php
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Background: The symptoms of obsessive-compulsive disorder (OCD) are highly heterogeneous and it is unclear what is the optimal way to conceptualize this heterogeneity. This study aimed to establish a comprehensive symptom structure model of OCD across the lifespan using factor and network analytic techniques.
Methods: A large multinational cohort of well-characterized children, adolescents, and adults diagnosed with OCD ( = 1366) participated in the study. All completed the Dimensional Yale-Brown Obsessive-Compulsive Scale, which contains an expanded checklist of 87 distinct OCD symptoms. Exploratory and confirmatory factor analysis were used to outline empirically supported symptom dimensions, and interconnections among the resulting dimensions were established using network analysis. Associations between dimensions and sociodemographic and clinical variables were explored using structural equation modeling (SEM).
Results: Thirteen first-order symptom dimensions emerged that could be parsimoniously reduced to eight broad dimensions, which were valid across the lifespan: Disturbing Thoughts, Incompleteness, Contamination, Hoarding, Transformation, Body Focus, Superstition, and Loss/Separation. A general OCD factor could be included in the final factor model without a significant decline in model fit according to most fit indices. Network analysis showed that Incompleteness and Disturbing Thoughts were most central (i.e. had most unique interconnections with other dimensions). SEM showed that the eight broad dimensions were differentially related to sociodemographic and clinical variables.
Conclusions: Future research will need to establish if this expanded hierarchical and multidimensional model can help improve our understanding of the etiology, neurobiology and treatment of OCD.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9693708 | PMC |
http://dx.doi.org/10.1017/S0033291720005437 | DOI Listing |
J Cachexia Sarcopenia Muscle
February 2025
Sprott Centre for Stem Cell Research, Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, Canada.
Background: Duchenne muscular dystrophy (DMD) is a devastating disease characterized by progressive muscle wasting that leads to diminished lifespan. In addition to the inherent weakness of dystrophin-deficient muscle, the dysfunction of resident muscle stem cells (MuSC) significantly contributes to disease progression.
Methods: Using the mdx mouse model of DMD, we performed an in-depth characterization of disease progression and MuSC function in dystrophin-deficient skeletal muscle using immunohistology, isometric force measurements, transcriptomic analysis and transplantation assays.
J Cachexia Sarcopenia Muscle
February 2025
School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China.
J Cachexia Sarcopenia Muscle
February 2025
Clinical Surgery, University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, Scotland, UK.
Background: Single-slice computed tomography (CT) body composition has been studied extensively for prognostication in patients with cancer. New software packages can also provide multi-slice volumetric measurements, but the clinical utility of these remains under explored. This study aimed to evaluate the agreement between single- and multi-slice body composition analyses in patients with oesophagogastric cancer and to explore the association between these measures and overall survival.
View Article and Find Full Text PDFInt J Dev Biol
December 2024
Laboratory of Plasticity and Differentiation of Neural Crest Cells, Federal University of Santa Catarina, Florianopolis, Santa Catarina, Brazil.
The neural crest (NC) is an embryonic cell population with high migratory capacity. It contributes to forming several organs and tissues, such as the craniofacial skeleton and the peripheral nervous system of vertebrates. Both pre-migratory and post-migratory NC cells are plastic, adopting multiple differentiation paths by responding to different inductive environmental signals.
View Article and Find Full Text PDFAnn Ital Chir
December 2024
Department of Colorectal Surgery, Hubei Provincial Hospital of Traditional Chinese Medicine Affiliated to Hubei University of Chinese Medicine, 430071 Wuhan, Hubei, China.
Aim: Anorectal diseases, often requiring surgical intervention and careful post-operative wound management, pose substantial challenges in healthcare. This study presents a novel application of artificial intelligence, specifically machine learning, aimed at improving the classification and analysis of post-surgical wound images. By doing so, it seeks to enhance patient outcomes through personalized and optimized wound care strategies.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!