Hippocampal neural stem cell (NSC) proliferation is known to decline with age, which is closely linked to learning and memory impairments. In the current study, we found that the expression level of miR-181a-5p was decreased in the hippocampal NSCs of aged mice and that exogenous overexpression of miR-181a-5p promoted NSC proliferation without affecting NSC differentiation into neurons and astrocytes. The mechanistic study revealed that phosphatase and tensin homolog (PTEN), a negative regulator of the AKT signaling pathway, was the target of miR-181a-5p and knockdown of PTEN could rescue the impairment of NSC proliferation caused by low miR-181a-5p levels. Moreover, overexpression of miR-181a-5p in the dentate gyrus enhanced the proliferation of NSCs and ameliorated learning and memory impairments in aged mice. Taken together, our findings indicated that miR-181a-5p played a functional role in NSC proliferation and aging-related, hippocampus-dependent learning and memory impairments.
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http://dx.doi.org/10.1111/acel.13794 | DOI Listing |
PLoS One
January 2025
School of Electronic Information Engineering, Inner Mongolia University, Hohhot, Inner Mongolia, China.
Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN's local feature extraction capability and LSTM's advantage in handling sequential data to optimize sensing accuracy and efficiency.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Information Systems and Cybersecurity, University of Bisha, Bisha, KSA.
Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge.
View Article and Find Full Text PDFNetwork
January 2025
Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM).
View Article and Find Full Text PDFMetab Brain Dis
January 2025
School of Natural Product Studies, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
Alzheimer's disease is a complex neurodegenerative disease characterized by progressive decline in cognitive function and behaviour. Ginger is the rhizome of the plant Zingiber officinale Roscoe, has been an important ingredient of many Ayurveda formulations to treat neurological disorders. The present study aims to estimate the variation of 6-gingerol content in nine different ginger samples collected from Manipur, India, investigate the neuroprotective potential of the most potent ginger sample against scopolamine-induced cognitively impaired mice, and validate the therapeutic claim by molecular docking analysis.
View Article and Find Full Text PDFRadiol Artif Intell
January 2025
From the Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15,81377 Munich, Germany (T.W., J.D., M.I.); Department of Statistics, LMU Munich, Munich, Germany (T.W., D.R.); and Munich Center for Machine Learning, Munich, Germany (T.W., J.D., D.R., M.I.).
Purpose To investigate whether the computational effort of 3D CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, Tucker decomposition was applied to the convolutional kernels of the TotalSegmentator model, an nnU-Net model trained on a comprehensive CT dataset for automatic segmentation of 117 anatomic structures. The proposed approach reduced the floating-point operations (FLOPs) and memory required during inference, offering an adjustable trade-off between computational efficiency and segmentation quality.
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