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People with serious mental illness have challenged self-awareness, including momentary monitoring of performance. A core feature of this challenge is in the domain of using external information to guide behavior, an ability that is measured very well by certain problem-solving tasks such as the Wisconsin Card Sorting Test (WCST) . We used a modified WCST to examine correct sorts and accuracy decisions regarding the correctness of sort. Participants with schizophrenia (n = 99) or bipolar disorder (n = 76) sorted 64 cards and then made judgments regarding correctness of each sort prior to feedback. Time series analyses examined the course of correct sorts and correct accuracy decisions by examining the momentary correlation and lagged correlation on the next sort. People with schizophrenia had fewer correct sorts, fewer categories, and fewer correct accuracy decisions (all p<.001). Positive response biases were seen in both groups. After an incorrect sort or accuracy decision, the groups were equally likely to be incorrect on the next sort or accuracy decision. Following correct accuracy decisions, participants with bipolar disorder were significantly (p=.003) more likely to produce a correct sort or accuracy decision. These data are consistent with previous studies implicating failures to consider external feedback for decision making. Interventions aimed at increasing consideration of external information during decision making have been developed and interventions targeting use of feedback during cognitive test performance are in development.
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http://dx.doi.org/10.1016/j.psychres.2024.115831 | DOI Listing |
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.
Anim Microbiome
August 2025
Ruminant Nutrition and Feed Engineering Technology Research Center, College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, 210095, Jiangsu, China.
iScience
August 2025
Molecular Cellular and Developmental Biology (MCD), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, 31000 Toulouse, France.
During animal development, cells communicate to ensure tissue-wide synchronization of differentiation. While several mechanisms contributing to cell coordination have been described, whether additional mechanisms are at play should cells locally desynchronize remains unknown. Here, we investigate the responses to experimentally induced desynchronized cells during epidermis development.
View Article and Find Full Text PDFbioRxiv
August 2025
Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544.
Modern high-density neural recordings demand spike sorting algorithms that can handle diverse probe geometries and complex, neuron-specific drift, yet existing methods often rely on rigid geometric assumptions and one-dimensional drift models. Here, we introduce KIASORT (Knowledge-Integrated Automated Spike Sorting), a geometry-free approach for per-neuron drift tracking. KIASORT trains channel-specific classifiers in a hybrid linear-nonlinear embedding space, capturing waveform features often missed by conventional linear methods.
View Article and Find Full Text PDFInt J Stroke
August 2025
Center for Clinical Big Data and Statistics of the Second Affiliated Hospital Zhejiang University School of Medicine, School of Public Health Zhejiang University School of Medicine, China.
Background: Stroke is a leading cause of death and disability worldwide, with women facing unique risks due to a combination of well-established, under-recognized, and female-specific factors.
Aims: This prospective cohort study aimed to quantify the population attributable fractions (PAFs) of stroke with distinct risk factor profiles and to explore disparities across age strata.
Methods: Data were from 239,200 women recruited in the UK Biobank.