| 1. |
- Pozo-Rodrigálvarez, Andrea, et al.
(författare)
-
C3a Receptor Signaling Inhibits Neurodegeneration Induced by Neonatal Hypoxic-Ischemic Brain Injury
- 2021
-
Ingår i: Frontiers in Immunology. - : Frontiers Media SA. - 1664-3224. ; 12
-
Tidskriftsartikel (refereegranskat)abstract
- Hypoxic-ischemic neonatal encephalopathy due to perinatal asphyxia is the leading cause of brain injury in newborns. Clinical data suggest that brain inflammation induced by perinatal insults can persist for years. We previously showed that signaling through the receptor for complement peptide C3a (C3aR) protects against cognitive impairment induced by experimental perinatal asphyxia. To investigate the long-term neuropathological effects of hypoxic-ischemic injury to the developing brain and the role of C3aR signaling therein, we subjected wildtype mice, C3aR deficient mice, and mice expressing biologically active C3a in the CNS to mild hypoxic-ischemic brain injury on postnatal day 9. We found that such injury triggers neurodegeneration and pronounced reactive gliosis in the ipsilesional hippocampus both of which persist long into adulthood. Transgenic expression of C3a in reactive astrocytes reduced hippocampal neurodegeneration and reactive gliosis. In contrast, neurodegeneration and microglial cell density increased in mice lacking C3aR. Intranasal administration of C3a for 3 days starting 1 h after induction of hypoxia-ischemia reduced neurodegeneration and reactive gliosis in the hippocampus of wildtype mice. We conclude that neonatal hypoxic-ischemic brain injury leads to long-lasting neurodegeneration. This neurodegeneration is substantially reduced by treatment with C3aR agonists, conceivably through modulation of reactive gliosis. Copyright © 2021 Pozo-Rodrigálvarez, Li, Stokowska, Wu, Dehm, Sourkova, Steinbusch, Mallard, Hagberg, Pekny and Pekna.
|
|
| 2. |
- Stokowska, Anna, et al.
(författare)
-
Complement C3a treatment accelerates recovery after stroke via modulation of astrocyte reactivity and cortical connectivity
- 2023
-
Ingår i: Journal of Clinical Investigation. - : American Society for Clinical Investigation. - 0021-9738 .- 1558-8238. ; 133:10
-
Tidskriftsartikel (refereegranskat)abstract
- Despite advances in acute care, ischemic stroke remains a major cause of long-term disability. Approaches targeting both neuronal and glial responses are needed to enhance recovery and improve long-term outcome. The complement C3a receptor (C3aR) is a regulator of inflammation with roles in neurodevelopment, neural plasticity, and neurodegeneration. Using mice lacking C3aR (C3aR-/-) and mice overexpressing C3a in the brain, we uncovered 2 opposing effects of C3aR signaling on functional recovery after ischemic stroke: inhibition in the acute phase and facilitation in the later phase. Peri-infarct astrocyte reactivity was increased and density of microglia reduced in C3aR-/- mice; C3a overexpression led to the opposite effects. Pharmacological treatment of wild-type mice with intranasal C3a starting 7 days after stroke accelerated recovery of motor function and attenuated astrocyte reactivity without enhancing microgliosis. C3a treatment stimulated global white matter reorganization, increased peri-infarct structural connectivity, and upregulated Igf1 and Thbs4 in the peri-infarct cortex. Thus, C3a treatment from day 7 after stroke exerts positive effects on astrocytes and neuronal connectivity while avoiding the deleterious consequences of C3aR signaling during the acute phase. Intranasal administration of C3aR agonists within a convenient time window holds translational promise to improve outcome after ischemic stroke.
|
|
| 3. |
- Wan, Miao, et al.
(författare)
-
A Random Indexing Approach for Web User Clustering and Web Prefetching
- 2012
-
Ingår i: New Frontiers in Applied Data Mining. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642283192 - 9783642283208 ; , s. 40-52
-
Konferensbidrag (refereegranskat)abstract
- In this paper we present a novel technique to capture Web users’ behaviour based on their interest-oriented actions. In our approach we utilise the vector space model Random Indexing to identify the latent factors or hidden relationships among Web users’ navigational behaviour. Random Indexing is an incremental vector space technique that allows for continuous Web usage mining. User requests are modelled by Random Indexing for individual users’ navigational pattern clustering and common user profile creation. Clustering Web users’ access patterns may capture common user interests and, in turn, build user profiles for advanced Web applications, such as Web caching and prefetching. We present results from the Web user clustering approach through experiments on a real Web log file with promising results. We also apply our data to a prefetching task and compare that with previous approaches. The results show that Random Indexing provides more accurate prefetchings.
|
|
| 4. |
- Wan, Miao, et al.
(författare)
-
Web user clustering and Web prefetching using Random Indexing with weight functions
- 2012
-
Ingår i: Knowledge and Information Systems. - : Springer Verlag (Germany). - 0219-1377 .- 0219-3116. ; 33:1, s. 89-115
-
Tidskriftsartikel (refereegranskat)abstract
- Users of a Web site usually perform their interest-oriented actions by clicking or visiting Web pages, which are traced in access log files. Clustering Web user access patterns may capture common user interests to a Web site, and in turn, build user profiles for advanced Web applications, such as Web caching and prefetching. The conventional Web usage mining techniques for clustering Web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users navigational behaviour. In this paper, we propose an approach based on a vector space model, called Random Indexing, to discover such intrinsic characteristics of Web users activities. The underlying factors are then utilised for clustering individual user navigational patterns and creating common user profiles. The clustering results will be used to predict and prefetch Web requests for grouped users. We demonstrate the usability and superiority of the proposed Web user clustering approach through experiments on a real Web log file. The clustering and prefetching tasks are evaluated by comparison with previous studies demonstrating better clustering performance and higher prefetching accuracy.
|
|
