9 research outputs found

    Alpha7 cholinergic-agonist prevents systemic inflammation and improves survival during resuscitation

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    Severe haemorrhage is a common cause of death despite the recent advances in critical care. Conventional resuscitation fluids are designed to re-establish tissue perfusion, but they fail to prevent inflammatory responses during resuscitation. Our previous studies indicated that the vagus nerve can modulate systemic inflammation via the alpha7 nicotinic acetylcholine receptor (alpha7nAchR). Here, we report that the alpha7nAChR-agonist, GTS, restrains systemic inflammation and improves survival during resuscitation. Resuscitation with GTS rescued all the animals from lethal haemorrhage in a concentration-dependent manner. Unlike conventional resuscitation fluids, GTS inhibited the production of characteristic inflammatory and cardiodepressant factors including tumour necrosis factor (TNF) and high mobility group B protein-1 (HMGB1). Resuscitation with GTS was particularly effective in restraining systemic TNF responses and inhibiting its production in the spleen. At the molecular level, GTS inhibited p65RelA but not RelB NF-kappaB during resuscitation. Unlike non-specific nicotinic agonists, GTS inhibited serum protein TNF levels in both normal and splenectomized, haemorrhagic animals. Resuscitation with GTS inhibited poly(ADP-ribose) polymerase and systemic HMGB1 levels. Our studies suggest that GTS provides significant advantages as compared with non-specific nicotinic agonists, and it could be a promising anti-inflammatory supplement to improve survival during resuscitatio

    Extension and refinement of the predictive value of different classes of markers in ADNI: Four-year follow-up data

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    BACKGROUND: This study examined the predictive value of different classes of markers in the progression from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD) over an extended 4 year follow-up in ADNI. METHODS: MCI patients assessed on clinical, cognitive, MRI, PET-FDG, and CSF markers at baseline, and followed on a yearly basis for four years to ascertain progression to AD. Logistic regression models were fitted in clusters including demographics, APOE genotype, cognitive markers, and biomarkers (morphometric, PET-FDG, CSF Abeta and tau). RESULTS: The predictive model at four years revealed that two cognitive measures, an episodic memory measure and a clock drawing screening test, were the best predictors of conversion (AUC= 0.78). CONCLUSIONS: This model of prediction is consistent to the previous model at two years, thus highlighting the importance of cognitive measures in progression from MCI to AD. Cognitive markers were more robust predictors than biomarkers
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