Based on a sample of 300 psychiatric patients the items of the Standard Progressive Matrices test are analyzed in terms of classical and probabilistic methods, and a version shortened to 30 items is developed. This new version of the test is then standardized from a new sample of 1,200 patients. A table of selected percentiles is computed. Validation with respect to rough classification of intelligence is proved by comparison with results of the WIP .
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http://dx.doi.org/10.1159/000284003 | DOI Listing |
BMC Infect Dis
January 2025
Diagnostic Systems Division, United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland, 21702, United States of America.
Background: Point of need diagnostics provide efficient testing capability for remote or austere locations, decreasing the time to answer by minimizing travel or sample transport requirements. Loop-mediated isothermal amplification (LAMP) is an appealing technology for point-of-need diagnostics due to its rapid analysis time and minimal instrumentation requirements.
Methods: Here, we designed and optimized nine LAMP assays that are sensitive and specific to targeted bacterial select agents including Bacillus anthracis, Francisella tularensis, Yersinia pestis, and Brucella spp.
J Affect Disord
January 2025
School of Psychological Sciences, Tel Aviv University, Tel-Aviv, Israel. Electronic address:
Background: Increased attention allocation to negative-valenced information and decreased attention allocation to positive-valenced information have been implicated in the etiology and maintenance of depression. The Matrix task, a free-viewing eye-tracking attention assessment task, has shown corroborating results, coupled with adequate reliability. Yet, replication efforts are still needed.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
BMC Bioinformatics
January 2025
Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital of Capital Medical University, Beijing, 101100, China.
Background: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experimental techniques for identifying SM-miRNA associations highlight the necessity for efficient computational methodologies in this field.
Results: In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations.
J Affect Disord
January 2025
School of Medicine and Health, Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany; School of Medicine and Health, TUM-NIC Neuroimaging Center, Technical University of Munich, Munich, Germany.
Aim: This study investigates the effects of transcranial direct current stimulation (tDCS) on brain network connectivity in individuals with obsessive-compulsive disorder (OCD).
Methods: In a randomized, double-blind, sham-controlled experimental design anodal tDCS (vs. sham) was applied in a total of 43 right-handed patients with OCD, targeting the right pre-supplementary motor area (pre-SMA).
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