The interface between Alzheimer's disease, normal aging, and related disorders

Since the earliest descriptions of Alzheimer's disease (AD), the presence of senile plaques (SP) and neurofibrillary tangles (NFT) have been regarded as the typical pathological hallmarks of the disease. Studies over the last twenty years, however, have reported a considerable degree of heterogeneity within the AD phenotype and as a consequence, an overlap between the pathological features of AD not only with normal aging, but also with disorders related to AD. This review discusses: 1) the degree of heterogeneity within AD, 2) the concept of an 'interface' between disorders, 3) the nature and degree of the interface between AD and normal aging, vascular dementia (VD), the tauopathies, synucleinopathies, and prion disease, and 4) whether the original status of AD should be retained or whether AD, normal aging, and the related disorders should be regarded as representing a 'continuum' of neuropathological change.

Development of an enantiomer selective microsampling assay for the quantification of ketorolac suitable for paediatric pharmacokinetic studies

Background: Ketorolac, a potent nonsteroidal anti-inflammatory drug used for pain control in children, exists as a racemate of inactive R (+) and active S (-) enantiomers. Aim: To develop a microsampling assay for the enantioselective analysis of ketorolac in children. Methods: Ketorolac enantiomers were extracted from 50 µl of plasma by liquid–liquid extraction and separated on a ChiralPak AD-RH. Detection was by a TSQ quantum triple quadrupole mass spectrometer with an electrospray ionisation source operating in a positive ion mode. Five children (age 13.8 (1.6) years, weight 52.7 (7.2) kg), were administered intravenous ketorolac 0.5 mg/kg (maximum 10 mg) and blood samples were taken at 0, 0.25, 0.5, 1, 2, 4, 6, 8 and 12 h post administration. CL, VD and t1/2 were calculated based on non-compartmental methods. Results: The standard curves for R (+) and S (-) ketorolac were linear in the range 0–2000 ng/ml. The LLOQs of the method were 0.15 ng on column and 0.31 ng on column for R (+) and S (-) ketorolac, respectively. The median (range) VD and CL of R (+) and S (-) ketorolac were 0.12 l/kg (0.07–0.17), 0.017 l/h/kg (0.12–0.29) and 0.17 (0.09–0.31) l/kg, 0.049 (0.02–0.1) l/h/kg, p = 0.043), respectively. The median (range) elimination half-life (t1/2) of the R (+) and S (-) ketorolac was 5.0 h (2.5–5.8) and 3.1 h (1.8–4.4), p = 0.043), respectively. Conclusion: The development of a simple, rapid and reliable ketorolac assay suitable for paediatric PK studies is reported. Copyright © 2013 John Wiley & Sons, Ltd.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Harnessing linked knowledge sources for topic classification in social media

Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets. Copyright 2013 ACM.