The brain is composed of a dense and ramified vascular network of arteries, veins and capillaries of various sizes. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of reduced blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models. To this end, we have adapted our functional ultrasound imaging platform, previously designed for recording large scale activity, to enable rapid and reproducible acquisition, segmentation and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical - m3 spatial resolution. Unlike most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it requires neither exogenous contrast agents nor long post-processing time. Therefore, we performed a cortex-wide reconstruction of the vasculature and its quantitative analysis, including i) classification of descending arteries versus ascending veins in more than 1500 vessels/animal and ii) rapid estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which allows rapid visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to better understand cerebrovascular pathologies such as stroke, vascular cognitive impairment and brain tumors, and is highly scalable for the clinic.
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http://dx.doi.org/10.1007/s12021-024-09706-1 | DOI Listing |
Stroke
January 2025
Department of Clinical Neuroscience and Therapeutics, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan (M.T., T.N., S.A., H.M.).
Background: Synthetic magnetic resonance imaging (MRI) is an innovative MRI technology that enables the acquisition of multiple quantitative values, including T1 and T2 values, proton density, and myelin volume, in a single scan. Although the usefulness of myelin measurement with synthetic MRI has been reported for assessing several diseases, investigations in patients with stroke have not been reported. We aimed to explore the utility of myelin quantification using synthetic MRI in predicting outcomes in patients with acute ischemic stroke.
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January 2025
IBiTech - BioMMedA Group, Ghent University, Corneel Heymanslaan 10, Entrance 98, 9000 Gent, Belgium.
Molecular oxygen (O) is essential for life, and continuous effort has been made to understand its pathways in cellular respiration with all-atom (AA) molecular dynamics (MD) simulations of, e.g., membrane permeation or binding to proteins.
View Article and Find Full Text PDFHeliyon
January 2025
Coordination Center for Research in Social Sciences, Faculty of Economics and Business, University of Debrecen, Böszörményi út 138., 4032, Debrecen, Hungary.
In recent months, the European Union has experienced inflation that has not been seen for decades. Inflation and inflation expectations are crucial in economic and purchasing behaviour, as they influence consumption. Hungary had the highest inflation among the Member States of the European Union.
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December 2024
Plastic Surgery, Shri Guru Ram Rai Institute of Medical & Health Sciences, Dehradun, IND.
Pregnancy issues such as gestational hypertension, preeclampsia, and gestational diabetes mellitus (GDM) are significant contributors to long-term cardiovascular diseases (CVDs) in women. Recent research has proved the impact of exercise on improving cardiovascular outcomes, particularly in women with pregnancy-related disorders. This review explores the outcomes of various exercise interventions on cardiovascular health in pregnant women.
View Article and Find Full Text PDFVis Intell
December 2024
Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zürich, Switzerland.
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.
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