AI Article Synopsis
- The study aimed to investigate the impact of omega-3 supplementation (EPA and DHA) on behavior and cognitive abilities in children aged 6-12 with ADHD.
- During a 16-week double-blind trial involving 95 children, various cognitive functions and behaviors were assessed using standardized methods.
- Results showed that the omega-3 supplementation improved working memory but did not significantly affect other cognitive measures or behavioral ratings, with improvements linked to higher EPA and DHA levels and lower arachidonic acid levels.
Article Abstract
Objective: To determine whether supplementation with the long-chain omega-3 polyunsaturated fatty acids eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) affects behavioral symptoms and cognitive impairments in children 6-12 years of age diagnosed with attention-deficit/hyperactivity disorder (ADHD).
Study Design: The randomized, double-blind placebo-controlled 16 weeks trial was conducted with 95 children diagnosed with ADHD according to DSM-IV criteria. Behavior was assessed by parents, teachers and investigators using standardized rating scales and questionnaires. Further outcome variables were working memory, speed of information processing and various measures of attention. For a subgroup of 81 participants, erythrocyte membrane fatty acid composition was analyzed before and after the intervention.
Results: Supplementation with the omega-3 fatty acid mix increased EPA and DHA concentrations in erythrocyte membranes and improved working memory function, but had no effect on other cognitive measures and parent- and teacher-rated behavior in the study population. Improved working memory correlated significantly with increased EPA, DHA and decreased AA (arachidonic acid).
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.plefa.2014.04.004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Pattern memory cannot be completely and truly realized in deep neural networks.
Sci Rep
December 2024
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN's interpretability and recurrent erratic behavior remain incontrovertible facts.
View Article and Find Full Text PDFCerebellar-driven cortical dynamics can enable task acquisition, switching and consolidation.
Nat Commun
December 2024
Computational Neuroscience Unit, Intelligent Systems Labs, Faculty of Engineering, University of Bristol, Bristol, UK.
The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions.
View Article and Find Full Text PDFTrivalent recombinant protein vaccine induces cross-neutralization against XBB lineage and JN.1 subvariants: preclinical and phase 1 clinical trials.
Nat Commun
December 2024
Laboratory of Aging Research and Cancer Drug Target, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
The immune escape capacities of XBB variants necessitate the authorization of vaccines with these antigens. In this study, we produce three recombinant trimeric proteins from the RBD sequences of Delta, BA.5, and XBB.
View Article and Find Full Text PDFRNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.
Brief Bioinform
November 2024
In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan.
Accurate prediction of RNA modifications holds profound implications for elucidating RNA function and mechanism, with potential applications in drug development. Here, the RNA-ModX presents a highly precise predictive model designed to forecast post-transcriptional RNA modifications, complemented by a user-friendly web application tailored for seamless utilization by future researchers. To achieve exceptional accuracy, the RNA-ModX systematically explored a range of machine learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit, and Transformer-based architectures.
View Article and Find Full Text PDFDeep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.
Adv Sci (Weinh)
December 2024
Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong SAR, China.
The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole-cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi-class classification.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!