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- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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- Bevan, RJ, et al.
(författare)
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Retinal ganglion cell degeneration correlates with hippocampal spine loss in experimental Alzheimer's disease
- 2020
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Ingår i: Acta neuropathologica communications. - : Springer Science and Business Media LLC. - 2051-5960. ; 8:1, s. 216-
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Tidskriftsartikel (refereegranskat)abstract
- Neuronal dendritic and synaptic pruning are early features of neurodegenerative diseases, including Alzheimer’s disease. In addition to brain pathology, amyloid plaque deposition, microglial activation, and cell loss occur in the retinas of human patients and animal models of Alzheimer’s disease. Retinal ganglion cells, the output neurons of the retina, are vulnerable to damage in neurodegenerative diseases and are a potential opportunity for non-invasive clinical diagnosis and monitoring of Alzheimer’s progression. However, the extent of retinal involvement in Alzheimer’s models and how well this reflects brain pathology is unclear. Here we have quantified changes in retinal ganglion cells dendritic structure and hippocampal dendritic spines in three well-studied Alzheimer’s mouse models, Tg2576, 3xTg-AD and APPNL-G-F. Dendritic complexity of DiOlistically labelled retinal ganglion cells from retinal explants was reduced in all three models in an age-, gender-, and receptive field-dependent manner. DiOlistically labelled hippocampal slices showed spine loss in CA1 apical dendrites in all three Alzheimer’s models, mirroring the early stages of neurodegeneration as seen in the retina. Morphological classification showed that loss of thin spines predominated in all. The demonstration that retinal ganglion cells dendritic field reduction occurs in parallel with hippocampal dendritic spine loss in all three Alzheimer’s models provide compelling support for the use of retinal neurodegeneration. As retinal dendritic changes are within the optical range of current clinical imaging systems (for example optical coherence tomography), our study makes a case for imaging the retina as a non-invasive way to diagnose disease and monitor progression in Alzheimer’s disease.
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- Goodfellow, IG, et al.
(författare)
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Inhibition of coxsackie B virus infection by soluble forms of its receptors: Binding affinities, altered particle formation, and competition with cellular receptors
- 2005
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Ingår i: Journal of Virology. - 1098-5514. ; 79:18, s. 12016-12024
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Tidskriftsartikel (refereegranskat)abstract
- We previously reported that soluble decay-accelerating factor (DAF) and coxsackievirus-adenovirus receptor (CAR) blocked coxsackievirus 133 (CVB3) myocarditis in mice, but only soluble CAR blocked CVB3-mediated pancreatitis. Here, we report that the in vitro mechanisms of viral inhibition by these soluble receptors also differ. Soluble DAF inhibited virus infection through the formation of reversible complexes with CVB3, while binding of soluble CAR to CVB induced the formation of altered (A) particles with a resultant irreversible loss of infectivity. A-particle formation was characterized by loss of VP4 from the virions and required incubation of CVB3-CAR complexes at 37 degrees C. Dimeric soluble DAF (DAF-Fc) was found to be 125-fold-more effective at inhibiting CVB3 than monomeric DAF, which corresponded to a 100-fold increase in binding affinity as determined by surface plasmon resonance analysis. Soluble CAR and soluble dimeric CAR (CAR-Fc) bound to CVB3 with 5,000- and 10,000-fold-higher affinities than the equivalent forms of DAF. While DAF-Fc was 125-fold-more effective at inhibiting virus than monomeric DAF, complement regulation by DAF-Fc was decreased 4 fold. Therefore, while the virus binding was a cooperative event, complement regulation was hindered by the molecular orientation of DAF-Fc, indicating that the regions responsible for complement regulation and virus binding do not completely overlap. Relative contributions of CVB binding affinity, receptor binding footprint on the virus capsid, and induction of capsid conformation alterations for the ability of cellular DAF and CAR to act as receptors are discussed.
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- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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