1.Hypotension with neurovascular changes and cognitive dysfunction: An epidemiological, pathobiological, and treatment review.
Yingzhe CHENG ; Lin LIN ; Peilin HUANG ; Jiejun ZHANG ; Yanping WANG ; Xiaodong PAN
Chinese Medical Journal 2025;138(4):405-418
Hypotension is a leading cause of age-related cognitive impairment. The available literature evidences that vascular factors are associated with dementia and that hypotension alters cerebral perfusion flow and can aggravate the neurodegeneration of Alzheimer's disease (AD). Despite the discovery of biomarkers and the recent progress made in neurovascular biology, epidemiology, and brain imaging, some key issues remain largely unresolved: the potential mechanisms underlying the neural deterioration observed in AD, the effect of cerebrovascular alterations on cognitive deficits, and the positive effects of hypotension treatment on cognition. Therefore, further well-designed studies are needed to unravel the potential association between hypotension and cognitive dysfunction and reveal the potential benefits of hypotension treatment for AD patients. Here, we review the current epidemiological, pathobiological, and treatment-related literature on neurovascular changes and hypotension-related cognitive dysfunction and highlight the unsettled but imminent issues that warrant future research endeavors.
Humans
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Hypotension/complications*
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Cognitive Dysfunction/etiology*
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Alzheimer Disease/epidemiology*
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Cerebrovascular Circulation/physiology*
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Cognition Disorders/etiology*
2.Brain White Matter Changes in Non-demented Individuals with Color Discrimination Deficits and Their Association with Cognitive Impairment: A NODDI Study.
Jiejun ZHANG ; Peilin HUANG ; Lin LIN ; Yingzhe CHENG ; Weipin WENG ; Jiahao ZHENG ; Yixin SUN ; Shaofan JIANG ; Xiaodong PAN
Neuroscience Bulletin 2025;41(8):1364-1376
Previous studies have found associations between color discrimination deficits and cognitive impairments besides aging. However, investigations into the microstructural pathology of brain white matter (WM) associated with these deficits remain limited. This study aimed to examine the microstructural characteristics of WM in the non-demented population with abnormal color discrimination, utilizing Neurite Orientation Dispersion and Density Imaging (NODDI), and to explore their correlations with cognitive functions and cognition-related plasma biomarkers. The tract-based spatial statistic analysis revealed significant differences in specific brain regions between the abnormal color discrimination group and the healthy controls, characterized by increased isotropic volume fraction and decreased neurite density index and orientation dispersion index. Further analysis of region-of-interest parameters revealed that the isotropic volume fraction in the bilateral anterior thalamic radiation, superior longitudinal fasciculus, cingulum, and forceps minor was significantly correlated with poorer performance on neuropsychological assessments and to varying degrees various cognition-related plasma biomarkers. These findings provide neuroimaging evidence that WM microstructural abnormalities in non-demented individuals with abnormal color discrimination are associated with cognitive dysfunction, potentially serving as early markers for cognitive decline.
Humans
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White Matter/pathology*
;
Male
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Female
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Cognitive Dysfunction/physiopathology*
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Middle Aged
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Aged
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Color Perception/physiology*
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Brain/pathology*
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Neuropsychological Tests
;
Diffusion Tensor Imaging
3.CAMU-Net:an improved model for retinal vessel segmentation based on Attention U-Net
Yunfei TANG ; Zhiping DAN ; Zhengtian HONG ; Yonglin CHEN ; Peilin CHENG ; Guo CHENG ; Fangting LIU
Chinese Journal of Medical Physics 2024;41(8):960-968
An improved U-Net model(channel attention module U-Net,CAMU-Net)is proposed to achieve precise segmentation of retinal vessels.CAMU-Net model enhances its understanding of regional features by employing residual enhancement convolution to extract important information from the regions,improves the global feature acquisition capability by introducing feature refinement module to promote feature extraction,realizes precise segmentation by adding channel attention module to capture image features accurately,and enhances its capability to perceive target boundaries and details through a multi-scale feature fusion structure.The ablation study on the DRIVE dataset validates the role of each module in retinal vessel segmentation.The comparison with other mainstream network models on DRIVE and STARE datasets verify that CAMU-Net model is superior to other models.

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