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      Young blood-mediated cerebromicrovascular rejuvenation through heterochronic parabiosis: enhancing blood-brain barrier integrity and capillarization in the aged mouse brain

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Is Open Access

            Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019

            Background Given the projected trends in population ageing and population growth, the number of people with dementia is expected to increase. In addition, strong evidence has emerged supporting the importance of potentially modifiable risk factors for dementia. Characterising the distribution and magnitude of anticipated growth is crucial for public health planning and resource prioritisation. This study aimed to improve on previous forecasts of dementia prevalence by producing country-level estimates and incorporating information on selected risk factors. Methods We forecasted the prevalence of dementia attributable to the three dementia risk factors included in the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 (high body-mass index, high fasting plasma glucose, and smoking) from 2019 to 2050, using relative risks and forecasted risk factor prevalence to predict GBD risk-attributable prevalence in 2050 globally and by world region and country. Using linear regression models with education included as an additional predictor, we then forecasted the prevalence of dementia not attributable to GBD risks. To assess the relative contribution of future trends in GBD risk factors, education, population growth, and population ageing, we did a decomposition analysis. Findings We estimated that the number of people with dementia would increase from 57·4 (95% uncertainty interval 50·4–65·1) million cases globally in 2019 to 152·8 (130·8–175·9) million cases in 2050. Despite large increases in the projected number of people living with dementia, age-standardised both-sex prevalence remained stable between 2019 and 2050 (global percentage change of 0·1% [–7·5 to 10·8]). We estimated that there were more women with dementia than men with dementia globally in 2019 (female-to-male ratio of 1·69 [1·64–1·73]), and we expect this pattern to continue to 2050 (female-to-male ratio of 1·67 [1·52–1·85]). There was geographical heterogeneity in the projected increases across countries and regions, with the smallest percentage changes in the number of projected dementia cases in high-income Asia Pacific (53% [41–67]) and western Europe (74% [58–90]), and the largest in north Africa and the Middle East (367% [329–403]) and eastern sub-Saharan Africa (357% [323–395]). Projected increases in cases could largely be attributed to population growth and population ageing, although their relative importance varied by world region, with population growth contributing most to the increases in sub-Saharan Africa and population ageing contributing most to the increases in east Asia. Interpretation Growth in the number of individuals living with dementia underscores the need for public health planning efforts and policy to address the needs of this group. Country-level estimates can be used to inform national planning efforts and decisions. Multifaceted approaches, including scaling up interventions to address modifiable risk factors and investing in research on biological mechanisms, will be key in addressing the expected increases in the number of individuals affected by dementia. Funding Bill & Melinda Gates Foundation and Gates Ventures.
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              Blood–brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders

              The blood-brain barrier (BBB) is a continuous endothelial membrane within brain microvessels that has sealed cell-to-cell contacts and is sheathed by mural vascular cells and perivascular astrocyte end-feet. The BBB protects neurons from factors present in the systemic circulation and maintains the highly regulated CNS internal milieu, which is required for proper synaptic and neuronal functioning. BBB disruption allows influx into the brain of neurotoxic blood-derived debris, cells and microbial pathogens and is associated with inflammatory and immune responses, which can initiate multiple pathways of neurodegeneration. This Review discusses neuroimaging studies in the living human brain and post-mortem tissue as well as biomarker studies demonstrating BBB breakdown in Alzheimer disease, Parkinson disease, Huntington disease, amyotrophic lateral sclerosis, multiple sclerosis, HIV-1-associated dementia and chronic traumatic encephalopathy. The pathogenic mechanisms by which BBB breakdown leads to neuronal injury, synaptic dysfunction, loss of neuronal connectivity and neurodegeneration are described. The importance of a healthy BBB for therapeutic drug delivery and the adverse effects of disease-initiated, pathological BBB breakdown in relation to brain delivery of neuropharmaceuticals are briefly discussed. Finally, future directions, gaps in the field and opportunities to control the course of neurological diseases by targeting the BBB are presented.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                GeroScience
                GeroScience
                Springer Science and Business Media LLC
                2509-2723
                May 10 2024
                Article
                10.1007/s11357-024-01154-8
                38914916
                5f74afff-8c7b-4c3d-ad81-992f7857e561
                © 2024

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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