98%
921
2 minutes
20
The apolipoprotein E4 (APOE-ε4) allele is the strongest genetic risk factor for developing late-onset Alzheimer's disease, and may predispose individuals to Alzheimer's-related cognitive decline by affecting normal brain function early in life. To investigate the impact of human APOE alleles on cognitive performance in mice, we trained 3-mo-old APOE targeted replacement mice (E2, E3, and E4) in the Barnes maze to locate and enter a target hole along the perimeter of the maze. Long-term spatial memory was probed 24 h and 72 h after training. We found that young E4 mice exhibited significantly impaired spatial learning and memory in the Barnes maze compared to E3 mice. Deficits in spatial cognition were also present in a second independent cohort of E4 mice tested at 18 mo of age. In contrast, cognitive performance in the hidden platform water maze was not as strongly affected by APOE genotype. We also examined the dendritic morphology of neurons in the medial entorhinal cortex of 3-mo-old TR mice, neurons important to spatial learning functions. We found significantly shorter dendrites and lower spine densities in basal shaft dendrites of E4 mice compared to E3 mice, consistent with spatial learning and memory deficits in E4 animals. These findings suggest that human APOE-ε4 may affect cognitive function and neuronal morphology early in life.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3630489 | PMC |
http://dx.doi.org/10.1101/lm.030031.112 | DOI Listing |
Nucleic Acids Res
September 2025
Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland.
Spatial omics allow for the molecular characterization of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, give rise to very different data modalities. The characteristics of the two data types are well known in spatial statistics as point patterns and lattice data.
View Article and Find Full Text PDFJ Neurosci Methods
September 2025
Department of Computer Science and Engineering, IIT (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India. Electronic address:
Background: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.
New Methods: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification.
Biomed Phys Eng Express
September 2025
Southwest Jiaotong University School of Mechanical Engineering, No. 111, North Section 1, Second Ring Road, Jinniu District, Chengdu, Chengdu, Sichuan, 610031, CHINA.
Total hip arthroplasty (THA) is the standard surgical treatment for end-stage hip osteoarthritis, with its success dependent on precise preoperative planning, which, in turn, relies on accurate three-dimensional segmentation and reconstruction of the periarticular bone of the hip joint. However, patients with hip osteoarthritis often exhibit pathological characteristics, such as joint space narrowing, femoroacetabular impingement, osteophyte formation, and joint deformity. These changes present significant challenges for traditional manual or semi-automatic segmentation methods.
View Article and Find Full Text PDFNeural Netw
September 2025
Department of Computer Science and Engineering, Hanyang University ERICA, 15588, South Korea. Electronic address:
In 6G mobile communication systems, various AI-based network functions and applications have been standardized. Federated learning (FL) is adopted as the core learning architecture for 6G systems to avoid privacy leakage from mobile user data. However, in FL, users with non-independent and identically distributed (non-IID) datasets can deteriorate the performance of the global model because the convergence direction of the gradient for each dataset is different, thereby inducing a weight divergence problem.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Cyberspace Security (School of Cryptology), Hainan University, No. 58, Renmin Avenue, Haikou, 570228, Hainan, China. Electronic address:
The primary challenge of large-margin learning lies in designing classifiers with strong discriminative power. Although existing large margin methods have achieved success in various classification tasks, they often suffer from weak task generalization and imbalanced handling of easy and hard samples. In this paper, we propose a margin adaptive synthetic virtual Softmax loss (SV-Softmax), which dynamically generates virtual prototypes by synthesizing embedded features and their corresponding prototypes.
View Article and Find Full Text PDF