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Objective: The cerebral mechanisms of traits associated with depersonalization-derealization disorder (DPRD) remain poorly understood.
Method: Happy and sad emotion expressions were presented to DPRD and non-referred control (NC) subjects in an implicit event-related functional magnetic resonance imaging (fMRI) design, and correlated with self report scales reflecting typical co-morbidities of DPRD: depression, dissociation, anxiety, somatization.
Results: Significant differences between the slopes of the two groups were observed for somatization in the right temporal operculum (happy) and ventral striatum, bilaterally (sad). Discriminative regions for symptoms of depression were the right pulvinar (happy) and left amygdala (sad). For dissociation, discriminative regions were the left mesial inferior temporal gyrus (happy) and left supramarginal gyrus (sad). For state anxiety, discriminative regions were the left inferior frontal gyrus (happy) and parahippocampal gyrus (sad). For trait anxiety, discriminative regions were the right caudate head (happy) and left superior temporal gyrus (sad). Discussion The ascertained brain regions are in line with previous findings for the respective traits. The findings suggest separate brain systems for each trait.
Conclusion: Our results do not justify any bias for a certain nosological category in DPRD.
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http://dx.doi.org/10.1017/S1092852913000588 | DOI Listing |
Photodiagnosis Photodyn Ther
September 2025
Department of Ophthalmology, People's Hospital of Feng Jie, Chongqing, 404600, China. Electronic address:
Objective: This study aims to develop a robust, multi-task deep learning framework that integrates vessel segmentation and radiomic analysis for the automated classification of four retinal conditions- diabetic retinopathy (DR), hypertensive retinopathy (HR), papilledema, and normal fundus-using fundus images.
Materials: AND.
Methods: A total of 2,165 patients from eight medical centers were enrolled.
Med
August 2025
Joint Academic Rheumatology Program, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece; Centre of New Biotechnologies and Precision Medicine (CNBPM), School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece. Electronic address: p
Background: Pathogenic responses against self and foreign antigens in systemic autoimmunity and infection, respectively, engage similar immunologic components, thus lacking distinguishing diagnostic biomarkers. Herein, we tested whether whole-blood transcriptome analysis discriminates autoimmune from infectious diseases.
Methods: We applied nested cross-validation methodology to tune and validate random forests, k-nearest neighbors, and support vector machines, using a new preprocessing method on 22 publicly available datasets, including 594 patients with a broad spectrum of systemic autoimmune diseases and 615 patients with diverse viral, bacterial, and parasitic infections.
Ann Plast Surg
September 2025
From the Department of Plastic Surgery, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India.
Background: Burns are associated with significant morbidity and mortality, with several factors determining mortality. Identifying reliable early predictors of mortality is crucial for guiding treatment decisions and improving outcomes. This study evaluates the prognostic significance of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in predicting mortality in patients with severe burns.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot 76100, Israel.
Humans and other organisms make decisions choosing between different options, with the aim of maximizing the reward and minimizing the cost. The main theoretical framework for modeling the decision-making process has been based on the highly successful drift-diffusion model, which is a simple tool for explaining many aspects of this process. However, recent observations challenge this model.
View Article and Find Full Text PDFJ Agric Food Chem
September 2025
University of Teramo, Department of Bioscience and Technology for Food, Agriculture and Environment, Via Renato Balzarini 1, Teramo 64100, Italy.
This study investigates the phenolic and fatty acid profiles of olives from four cultivars (Arbequina, Arbosana, Frantene, and Koroneiki), widely grown in the Mediterranean region and collected at different ripening stages in Italy. The aim was to assess the potential of olive chemical profiles as markers for cultivar classification using machine learning algorithms, including Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Results showed that phenolic profiling achieved significantly higher classification accuracy than fatty acids across all models.
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