The network theory of psychological disorders posits that systems of symptoms cause, or are associated with, the expression of other symptoms. Substantial literature on symptom networks has been published to date, although no systematic review has been conducted exclusively on symptom networks of schizophrenia, schizoaffective disorder, and schizophreniform (people diagnosed with schizophrenia; PDS). This study aims to compare statistics of the symptom network publications on PDS in the last 21 years and identify congruences and discrepancies in the literature. More specifically, we will focus on centrality statistics. Thirty-two studies met the inclusion criteria. The results suggest that cognition, and social, and occupational functioning are central to the network of symptoms. Positive symptoms, particularly delusions were central among participants in many studies that did not include cognitive assessment. Nodes representing cognition were most central in those studies that did. Nodes representing negative symptoms were not as central as items measuring positive symptoms. Some studies that included measures of mood and affect found items or subscales measuring depression were central nodes in the networks. Cognition, and social, and occupational functioning appear to be core symptoms of schizophrenia as they are more central in the networks, compared to variables assessing positive symptoms. This seems consistent despite heterogeneity in the design of the studies.
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http://dx.doi.org/10.1017/S003329172300363X | DOI Listing |
Clin Lymphoma Myeloma Leuk
November 2024
Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Background: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; however, given the limited availability of chest CT during a pandemic, the assessment of more readily available imaging, such as chest radiographs, augmented by artificial intelligence may substitute for the detection of the features of coronavirus disease 2019 (COVID-19) pneumonia.
Methods: We trained a deep convolutional neural network to detect SARS-CoV-2 pneumonia using publicly available chest radiography imaging data including 8,851 normal, 6,045 pneumonia, and 200 COVID-19 pneumonia radiographs.
ISA Trans
December 2024
Faculty of Mechanical and Civil Engineering, Department of Automatic Control, Robotics and Fluid Technique, University of Kragujevac, Kraljevo 36000, Serbia. Electronic address:
When the fault diagnosis datasets contains noise disturbances, small samples, compound faults, and mixed conditions, the feature extraction capability of the neural network will face significant challenges. This paper proposes an end-to-end multi-scale residual network with parallel attention mechanism to address the above complex problems. Firstly, the adaptive mixing pooling method is employed to facilitate the model's ability to retain effective feature information present within the timing signal.
View Article and Find Full Text PDFMicrob Pathog
December 2024
Department of Urology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China. Electronic address:
The presence of the Hepatitis B virus (HBV) is considered as a valuable risk factor of hepatocellular carcinoma (HCC). To more deeply comprehend the molecular mechanism and transcriptome of HBV-induced HCC, we utilized tandem mass tagging (TMT)-based quantitative proteomics analysis and whole-transcriptome sequencing to analyze three sets of matched HepG2 hepatoma cells and HBV-positive HepAD38 cells. The differentially expressed (DE) proteins (1596), mRNAs (5263), miRNAs (581), lncRNAs (2672) and circRNAs (222) were subjected to differential expression and enrichment analyses in order to thoroughly assess the gene-regulatory circuits of HBV-induced HCC.
View Article and Find Full Text PDFClin Nutr ESPEN
December 2024
Child Psychopathology Unit, Scientific Institute, IRCSS Eugenio Medea, Bosisio Parini LC, Italy.
Background & Aims: Considerable interest has been recently given to the potential role of the gut-brain axis (GBA) -a two-way communication network between the gut microbiota and the central nervous system- in the pathogenesis of attention-deficit hyperactivity disorder (ADHD), suggesting the potential usefulness of probiotic and synbiotic supplementations. In light of the limited available evidence, synbiotic efficacy in ADHD children not taking medications should be clarified. This study aimed to investigate the efficacy of a synbiotic dietary supplementation on fatty acids levels as well as on microbiota composition, behaviour, cognition, and brain function in children with ADHD.
View Article and Find Full Text PDFBrain Res Bull
December 2024
College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, No.79 Yingze West Street, Taiyuan City, Shanxi Province, China. Electronic address:
Brain functional hypernetworks that can characterize the complex and multivariate interactions among multiple brain regions have been widely used in the diagnosis and prediction of brain diseases. However, there are few studies on the structure and dynamics of brain functional hypernetworks. Such studies can help to explore how the important functional features of brain functional hypernetworks characterize the working and pathological mechanisms of the human brain.
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