Aim: Amyloid-β (Aβ) accumulation, accelerated by traumatic brain injury (TBI), may play a crucial role in neurodegeneration in chronic-stage TBI. The injury type could influence Aβ dynamics because of TBI's complex, heterogeneous nature. We, therefore, investigated spatial patterns of amyloid deposition according to injury type after TBI using 5-(5-(2-(2-(2-[F]-fluoroethoxy)ethoxy)ethoxy)benzofuran-2-yl)--methylpyridin-2-amine (F-FPYBF-2) positron emission tomography (PET).
Methods: Altogether, 20 patients with chronic TBI [12 with focal injury, 8 with diffuse axonal injury (DAI)] underwent F-FPYBF-2 PET, structural magnetic resonance imaging (MRI), and neuropsychological examination. Additionally, 50 healthy controls underwent either F-FPYBF-2 PET (n=30) or structural MRI (n=20).
Results: Standardized uptake value ratio (SUVR) on PET images and regional brain volumes were measured in four cortical (frontal, parietal, occipital, temporal) and subcortical (combined caudate, putamen, pallidum, thalamus) regions. Patients with DAI showed significantly increased (compared with controls) SUVR in occipital and temporal cortices and decreased brain volume in occipital cortex (corrected p < 0.05). Although patients with focal injury showed decreased SUVR in all regions except occipital cortex, there were no significant differences (compared with controls) in the SUVR in any regions. There were no significant correlations between increased SUVR and neuropsychological impairments in patients with DAI.
Conclusion: Varying spatial patterns of amyloid deposition suggest amyloid pathology diversity depending on the injury type in chronic-TBI patients.
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http://dx.doi.org/10.2147/NDT.S268504 | DOI Listing |
Health Serv Res
January 2025
Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Objective: To examine the extent of segregation between hospitals for Medicare beneficiaries by race, ethnicity, and dual-eligible status over time.
Data Sources And Study Setting: We used Medicare inpatient hospital provider data for fee-for-service (FFS) beneficiaries, and the Dartmouth Atlas of Health Care from 2013 to 2021 nationwide, for hospital referral regions (HRRs), and for and hospital service areas (HSAs).
Study Design: We conducted time trend analysis with dissimilarity indices (DIs) for Black (DI-Black), Hispanic (DI-Hispanic), non-White (including Black, Hispanic, and other non-White) (DI-non-White), and dual-eligible (DI-Dual) beneficiaries.
Sensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
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January 2025
Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns.
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January 2025
NUS-ISS, National University of Singapore, Singapore 119615, Singapore.
Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches.
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January 2025
Department of Information Technology, Quaid e Awam University, Nawabshah 67450, Pakistan.
Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed.
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