Amyloid-beta peptide is central to the pathology of Alzheimer's disease, because it is neurotoxic--directly by inducing oxidant stress, and indirectly by activating microglia. A specific cell-surface acceptor site that could focus its effects on target cells has been postulated but not identified. Here we present evidence that the 'receptor for advanced glycation end products' (RAGE) is such a receptor, and that it mediates effects of the peptide on neurons and microglia. Increased expressing of RAGE in Alzheimer's disease brain indicates that it is relevant to the pathogenesis of neuronal dysfunction and death.

Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.

Background: Post-stroke cognitive impairment (PSCI) occurs in approximately half of ischemic stroke survivors. Infarct location is a potential determinant of PSCI, but a comprehensive map of strategic infarct locations is lacking. In this large-scale multicenter lesion-symptom mapping study, we aimed to identify infarct locations most strongly predictive of PSCI, and use this information to develop a prediction model. Methods:We harmonized individual patient data from twelve cohorts through the Meta-VCI-Map consortium. Patients with acute symptomatic infarcts on CT/MRI and cognitive assessment <1 year poststroke were eligible. PSCI was defined as impairment in ≥1 cognitive domains on neuropsychological assessment or impairment on the Montreal Cognitive Assessment. Voxel-based lesion-symptom mapping (VLSM) was used to calculate voxel-wise odds ratios for PSCI. For the prediction model, a "location impact score" on a five-point scale was derived from the VLSM results. Combined internal-external validation was performed using leave-one-cohort-out cross-validation for all twelve cohorts. Findings:In our combined sample of 2950 patients (age 67±12 years, 39% female), 44% had PSCI. We achieved almost complete lesion coverage of the brain in our analyses (87%). Infarcts in the left frontotemporal lobes, left thalamus, and right parietal lobe were strongly associated with PSCI (False Discovery Rate corrected q<0•01; voxel-wise odds ratios >20). These strategic regions were mapped onto a three-dimensional brain template to visualize PSCI risk per brain region. The location impact score showed good correspondence between predicted and observed risk across cohorts after adjusting for cohortspecific PSCI occurrence. Interpretation:This study provides the first comprehensive map of strategic infarct locations associated with risk of PSCI. A location impact score was derived from this map that robustly predicted PSCI across cohorts and can be applied by clinicians to identify individual patients at risk of PSCI.

Lesion location is an important determinant for post-stroke cognitive impairment. Although several 'strategic' brain regions have previously been identified, a comprehensive map of strategic brain regions for post-stroke cognitive impairment is lacking due to limitations in sample size and methodology. We aimed to determine strategic brain regions for post-stroke cognitive impairment by applying multivariate lesion-symptom mapping in a large cohort of 410 acute ischemic stroke patients. Montreal Cognitive Assessment at three to six months after stroke was used to assess global cognitive functioning and cognitive domains (memory, language, attention, executive and visuospatial function). The relation between infarct location and cognition was assessed in multivariate analyses at the voxel-level and the level of regions of interest using support vector regression. These two assumption-free analyses consistently identified the left angular gyrus, left basal ganglia structures and the white matter around the left basal ganglia as strategic structures for global cognitive impairment after stroke. A strategic network involving several overlapping and domain-specific cortical and subcortical structures was identified for each of the cognitive domains. Future studies should aim to develop even more comprehensive infarct location-based models for post-stroke cognitive impairment through multicenter studies including thousands of patients.

Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can efficiently utilize 3-D contextual information and automatically learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very deep 3-D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of information and gradients throughout the network. We train our model with Dice objective function to combat the severe class imbalance problem in data. A DWI data set containing 242 subjects (90 for training, 62 for validation, and 90 for testing) with various types of acute ischemic stroke was constructed to evaluate our method. Our model achieved high performance on various metrics (Dice similarity coefficient: 79.13%, lesionwise precision: 92.67%, and lesionwise F1 score: 89.25%), outperforming the other state-of-the-art CNN methods by a large margin. We also evaluated the model on ISLES2015-SSIS data set and achieved very competitive performance, which further demonstrated its generalization capacity. The proposed method is fast and accurate, demonstrating a good potential in clinical routines.

The molecular signatures of cells in the brain have been revealed in unprecedented detail, yet the ageing-associated genome-wide expression changes that may contribute to neurovascular dysfunction in neurodegenerative diseases remain elusive. Here, we report zonationdependent transcriptomic changes in aged mouse brain endothelial cells (ECs), which prominently implicate altered immune/cytokine signaling in ECs of all vascular segments, and functional changes impacting the blood-brain barrier (BBB) and glucose/energy metabolism especially in capillary ECs (capECs). An overrepresentation of Alzheimer disease (AD) GWAS genes is evident among the human orthologs of the differentially expressed genes of aged capECs, while comparative analysis revealed a subset of concordantly downregulated, functionally important genes in human AD brains. Treatment with exenatide, a glucagon-like peptide-1 receptor agonist, strongly reverses aged mouse brain EC transcriptomic changes and BBB leakage, with associated attenuation of microglial priming. We thus revealed transcriptomic alterations underlying brain EC ageing that are complex yet pharmacologically reversible.

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SUMMARYVascular dysfunction in patients with diabetes mellitus is related to advanced glycation end product (AGE) formation. We previously showed that AGEs produce an increase in vascular permeability and generated an oxidant stress after binding to the receptor (RAGE) present on endothelium. RAGE, a 35-kDa protein that belongs to the immunoglobulin superfamily, has been cloned from a rat lung cDNA library, and recombinant rat soluble RAGE (rR-RAGE) has been produced in insect cells. The sequence of RAGE is highly conserved between human and rat. We studied the biological effect of rR-RAGE and pharmacokinetics of 125 I-rR-RAGE after intravenous or intraperitoneal administration in normal and streptozotocin-induced diabetic rats. rR-RAGE prevented albumin or inulin transfer through a bovine aortic endothelial cell monolayer, restored the hyperpermeability observed in diabetic rats or induced in normal rats by diabetic rat red blood cells, and corrected the reactive oxygen intermediate production after intravenous or intraperitoneal administration. After intravenous injection of 125 I-rR-RAGE, the distribution half-life was longer ( p Յ 0.01) in diabetic (0.15 and 4.01 hr) than in normal (0.02 and 0.21 hr) rats, as was the case for the elimination half-lives (diabetic, 57.17 hr; normal, 26.02 hr; p Յ 0.01). Distribution volume was higher in diabetic than in normal rats (6.94 and 3.24 liter/kg, respectively; p ϭ 0.049). Our study showed that rR-RAGE was biologically active in vivo and slowly cleared, which suggests it could be considered as a potential therapy.Vascular dysfunction is one of the main complications in patients with diabetes mellitus. Several hypotheses have been proposed to explain vascular abnormalities linked to hyperglycemia, including sorbitol toxicity (1), diacylglycerol protein kinase activation (1), AGE formation (1, 2), and reactive oxygen species production (3). AGEs are the ultimate products of a nonenzymatic glycation of primary amino groups on proteins or lipids. These compounds are yellowbrown with characteristic fluorescence spectrum (1-3). The RAGE has been purified and cloned; it is a protein of 35 kDa that belongs to the immunoglobulin superfamily. It is composed of extracellular domains (one V-type domain followed by two C-type domains), a single transmembrane spanning domain, and a highly charged cytosolic tail (4). The RAGE is present on several cell types, including ECs, smooth muscle cells, monocytes/macrophages, cardiac myocytes, neural tissue, and hepatocytes (5).We recently showed that AGE engagement to a receptor present on ECs produces an oxidant stress and results in an increased vascular permeability (6, 7). We showed that sRAGE form could be used to prevent the consequences of the presence of AGE in the plasma and demonstrated that the newly described sRAGE prevents the effects of AGEs on EC functions and on the hyperpermeability observed in streptozotocin-induced diabetic rats or in normal rats that had been transfused with diabetic rat RBCs (7). sRAGE acts by binding t...