Prior research has identified a number of areas of cognitive deficit among children with sickle cell disease (SCD), including decrements in memory span and working memory. The present study examined short-term memory span and working memory performance among children with SCD (n = 25) and demographically matched comparison children (n = 25) using digit span, spatial span, and the self-ordered pointing test. Children with SCD showed difficulties only for digit span-backward. Additional cognitive ability measures administered indicated auditory processing was an area of deficit related to digit span-backward performance. The study suggests that modality specific deficits are one factor in short-term memory span for children with SCD. The cause of this deficit is unclear, but may involve both central and peripheral components of auditory processing.
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http://dx.doi.org/10.1016/j.acn.2005.06.008 | DOI Listing |
NPJ Sci Food
December 2024
Department of Bioscience and Biotechnology, Faculty of Agriculture, Graduate School of Kyushu University, Fukuoka, Japan.
In a series of studies on blood-brain barrier transportable peptides, a soybean dipeptide, Tyr-Pro, penetrated the mouse brain parenchyma after oral intake and improved short and long memory impairment in acute Alzheimer's model mice. Here, we aimed to clarify the anti-dementia effects of this peptide administered to SAMP8 mice prior to dementia onset. At the end of the 25-week protocol in 16-week-old SAMP8 mice, Tyr-Pro (10 mg/kg/day) significantly improved the reduced spatial learning ability compared with that in the control and amino acid (Tyr + Pro) groups as indicated by the results of Morris water maze tests conducted for five consecutive days.
View Article and Find Full Text PDFNat 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 PDFBrief 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 PDFAdv 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 PDFBMC Med Res Methodol
December 2024
School of Public Health, Xuzhou Medical University, No. 209 Tongshan Road, Xuzhou, Jiangsu, 221004, China.
Background: The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied. It may be inaccurate to apply predictive models from a broad region directly to a small area. This paper builds a prediction approach for small size COVID-19 time series in a city.
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