Sickle cell disease (SCD) is associated with increased risk of neurocognitive deficits. However, whether functioning changes following nonmyeloablative hematopoietic stem cell transplant (HSCT) remains unclear. This study aimed to examine changes in neuropsychological functioning pre- to post-transplant among patients with SCD and compare patients and siblings. Adults with SCD ( = 47; M = 31.8 ± 8.9) and their sibling stem cell donors ( = 22; M = 30.5± 9.2) enrolled on a nonmyeloablative HCST protocol completed cognitive and patient-reported outcome assessments at baseline and 12 months post-transplant. Path analyses were used to assess associations between pre-transplant variables and sibling/patient group status and post-transplant function. Mean patient cognitive scores were average at both timepoints. Patient processing speed and somatic complaints improved from baseline to follow-up. Baseline performance predicted follow-up performance across cognitive variables; patient/sibling status predicted follow-up performance on some processing speed measures. Results suggest that patients with SCD demonstrate slower processing speed than siblings. Processing speed increased pre- to post-HSCT among patients and siblings, and on some measures patients demonstrated greater improvement. Thus, HSCT may improve processing speed in patients, although further confirmation is needed. Findings provide promising evidence that neurocognitive functioning remains stable without detrimental effects from pre- to 12-months post nonmyeloablative HSCT in individuals with SCD.
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http://dx.doi.org/10.1080/09602011.2023.2238948 | DOI Listing |
J Gerontol B Psychol Sci Soc Sci
January 2025
Linguistics and English as a Second Language, Faculty of Arts, University of Groningen, Groningen, the Netherlands.
Objectives: The complex life experience of speaking two or more languages has been suggested to preserve cognition in older adulthood. This study aimed to investigate this further by examining the relationship between multilingual experience variables and cognitive functioning in a large cohort of older adults in the diversely multilingual north of the Netherlands.
Method: 11,332 older individuals participating in the Lifelines Cohort Study completed a language experience questionnaire.
Braz J Psychiatry
January 2025
Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Department and Institute of Psychiatry, University of São Paulo, São Paulo, SP, Brazil.
Objective: Post-stroke depression (PSD) affects approximately 40% of stroke survivors, with cognitive deficits being frequently observed. Transcranial Direct Current Stimulation (tDCS) has shown promise in improving cognitive performance in stroke patients. We explored the effects of tDCS on cognitive performance in PSD.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification.
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January 2025
Computer-Aided Design and Test (CADT) Research Group, McMaster University, Hamilton, ON L8S 4L8, Canada.
A parallelized field-programmable gate array (FPGA) architecture is proposed to realize an ultra-fast, compact, and low-cost dual-channel ultra-wideband (UWB) pulsed-radar system. This approach resolves the main shortcoming of current FPGA-based radars, namely their low processing throughput, which leads to a significant loss of data provided by the radar receiver. The architecture is integrated with an in-house UWB pulsed radar operating at a sampling rate of 20 gigasamples per second (GSa/s).
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January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
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