Despite preliminary evidence that individuals with borderline personality disorder (BPD) demonstrate deficits in learning from corrective feedback, no studies have examined the influence of emotional state on these learning deficits in BPD. This laboratory study examined the influence of negative emotions on learning among participants with BPD (n = 17), compared with clinical (past-year mood/anxiety disorder; n = 20) and healthy (n = 23) controls. Participants completed a reinforcement learning task before and after a negative emotion induction. The learning task involved presenting pairs of stimuli with probabilistic feedback in the training phase, and subsequently assessing accuracy for choosing previously rewarded stimuli or avoiding previously punished stimuli. ANOVAs and ANCOVAs revealed no significant between-group differences in overall learning accuracy. However, there was an effect of group in the ANCOVA for postemotion induction high-conflict punishment learning accuracy, with the BPD group showing greater decrements in learning accuracy than controls following the negative emotion induction.
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http://dx.doi.org/10.1521/pedi_2017_31_299 | DOI Listing |
Acta Radiol
January 2025
Department of Medical Imaging, Dalin Tzu-Chi Hospital, Chiayi, Taiwan.
Background: The wide variability in thresholds on computed tomography (CT) perfusion parametric maps has led to controversy in the stroke imaging community about the most accurate measurement of core infarction.
Purpose: To investigate the feasibility of using U-Net to perform infarct core segmentation in CT perfusion imaging.
Material And Methods: CT perfusion parametric maps were the input of U-Net, while the ground truth segmentation was determined based on diffusion-weighted imaging (DWI).
Stat Methods Med Res
January 2025
CITMAga and Department of Statistics and Operations Research, Universidade de Vigo, Vigo, Galicia, Spain.
The study of the predictive ability of a marker is mainly based on the accuracy measures provided by the so-called confusion matrix. Besides, the area under the receiver operating characteristic curve has become a popular index for summarizing the overall accuracy of a marker. However, the nature of the relationship between the marker and the outcome, and the role that potential confounders play in this relationship could be fundamental in order to extrapolate the observed results.
View Article and Find Full Text PDFClin Implant Dent Relat Res
February 2025
SEMRUK Technology Inc., Cumhuriyet Teknokent, Sivas, Turkiye.
Objectives: This study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.
Materials And Methods: A retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized.
Bioinform Adv
November 2024
Institute of Biochemistry and Molecular Medicine, University of Bern, Bern 3012, Switzerland.
Summary: Protein structure prediction aims to infer a protein's three-dimensional (3D) structure from its amino acid sequence. Protein structure is pivotal for elucidating protein functions, interactions, and driving biotechnological innovation. The deep learning model AlphaFold2, has revolutionized this field by leveraging phylogenetic information from multiple sequence alignments (MSAs) to achieve remarkable accuracy in protein structure prediction.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, SE-182 88 Stockholm, Sweden.
Aims: A simplified version of the history, electrocardiogram, age, risk factors, troponin (HEART) score, excluding troponin, has been proposed to rule-out major adverse cardiac events (MACEs). Computerized history taking (CHT) provides a systematic and automated method to obtain information necessary to calculate the HEAR score. We aimed to evaluate the efficacy and diagnostic accuracy of CHT in calculating the HEAR score for predicting MACE.
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