Simulation using novel equipment designed to explain spirometric abnormalities in respiratory disease enhances learning in higher cognitive domains

Simulation of disorders of respiratory mechanics shown by spirometry provides insight into the pathophysiology of disease but some clinically important disorders have not been simulated and none have been formally evaluated for education. We have designed simple mechanical devices which, along with existing simulators, enable all the main dysfunctions which have diagnostic value in spirometry to be simulated and clearly explained with visual and haptic feedback. We modelled the airways as Starling resistors by a clearly visible mechanical action to simulate intra- and extra-thoracic obstruction. A narrow tube was used to simulate fixed large airway obstruction and inelastic bands to simulate restriction. We hypothesized that using simulators whose action explains disease promotes learning especially in higher domain educational objectives. The main features of obstruction and restriction were correctly simulated. Simulation of variable extra-thoracic obstruction caused blunting and plateauing of inspiratory flow, and simulation of intra-thoracic obstruction caused limitation of expiratory flow with marked dynamic compression. Multiple choice tests were created with questions allocated to lower (remember and understand) or higher cognitive domains (apply, analyse and evaluate). In a cross-over design, overall mean scores increased after 1½ h simulation spirometry (43-68 %, effect size 1.06, P < 0.0001). In higher cognitive domains the mean score was lower before and increased further than lower domains (Δ 30 vs 20 %, higher vs lower effect size 0.22, P < 0.05). In conclusion, the devices successfully simulate various patterns of obstruction and restriction. Using these devices medical students achieved marked enhancement of learning especially in higher cognitive domains.

Untargeted Metabolomic Analysis of Human Plasma Indicates Differentially Affected Polyamine and L-Arginine Metabolism in Mild Cognitive Impairment Subjects Converting to Alzheimer’s Disease

This study combined high resolution mass spectrometry (HRMS), advanced chemometrics and pathway enrichment analysis to analyse the blood metabolome of patients attending the memory clinic: cases of mild cognitive impairment (MCI; n = 16), cases of MCI who upon subsequent follow-up developed Alzheimer's disease (MCI_AD; n = 19), and healthy age-matched controls (Ctrl; n = 37). Plasma was extracted in acetonitrile and applied to an Acquity UPLC HILIC (1.7μm x 2.1 x 100 mm) column coupled to a Xevo G2 QTof mass spectrometer using a previously optimised method. Data comprising 6751 spectral features were used to build an OPLS-DA statistical model capable of accurately distinguishing Ctrl, MCI and MCI_AD. The model accurately distinguished (R2 = 99.1%; Q2 = 97%) those MCI patients who later went on to develop AD. S-plots were used to shortlist ions of interest which were responsible for explaining the maximum amount of variation between patient groups. Metabolite database searching and pathway enrichment analysis indicated disturbances in 22 biochemical pathways, and excitingly it discovered two interlinked areas of metabolism (polyamine metabolism and L-Arginine metabolism) were differentially disrupted in this well-defined clinical cohort. The optimised untargeted HRMS methods described herein not only demonstrate that it is possible to distinguish these pathologies in human blood but also that MCI patients 'at risk' from AD could be predicted up to 2 years earlier than conventional clinical diagnosis. Blood-based metabolite profiling of plasma from memory clinic patients is a novel and feasible approach in improving MCI and AD diagnosis and, refining clinical trials through better patient stratification.

Plasma Clusterin Levels and the rs11136000 Genotype in Individuals with Mild Cognitive Impairment and Alzheimer's Disease

Aim: Substantial evidence links atherosclerosis and Alzheimer's disease (AD). Apolipoproteins, such as apolipoprotein E, have a causal relationship with both diseases. The rs11136000 SNP within the CLU gene, which encodes clusterin (apolipoprotein J), is also associated with increased AD risk. The aim of this study was to investigate the relationship between plasma clusterin and the rs11136000 genotype in mild cognitive impairment (MCI) and AD.

Methods: Plasma and DNA samples were collected from control, MCI and AD subjects (n=142, 111, 154, respectively). Plasma clusterin was determined by ELISA and DNA samples were genotyped for rs11136000 by TaqMan assay.

Results: Plasma clusterin levels were higher in MCI and AD subjects vs. controls (222.3 +/- 61.3 and 193.6 +/- 58.2 vs. 178.6 +/- 52.3 mu g/ml, respectively; p

Conclusion: This study examined control, MCI and AD subjects, identifying for the first time that plasma clusterin levels were influenced, not only by the presence of AD, but also the transitional stage of MCI, while rs11136000 genotype only influenced plasma clusterin levels in the control group. The increase in plasma clusterin in MCI and AD subjects may occur in response to the disease process and would be predicted to increase binding capacity for amyloid-beta peptides in plasma, enhancing their removal from the brain.

Location-Awareness for Radios and Networks: Part I

The papers in this special issue focus on the topic of location awareness for radio and networks. Localization-awareness using radio signals stands to revolutionize the fields of navigation and communication engineering. It can be utilized to great effect in the next generation of cellular networks, mining applications, health-care monitoring, transportation and intelligent highways, multi-robot applications, first responders operations, military applications, factory automation, building and environmental controls, cognitive wireless networks, commercial and social network applications, and smart spaces. A multitude of technologies can be used in location-aware radios and networks, including GNSS, RFID, cellular, UWB, WLAN, Bluetooth, cooperative localization, indoor GPS, device-free localization, IR, Radar, and UHF. The performances of these technologies are measured by their accuracy, precision, complexity, robustness, scalability, and cost. Given the many application scenarios across different disciplines, there is a clear need for a broad, up-to-date and cogent treatment of radio-based location awareness. This special issue aims to provide a comprehensive overview of the state-of-the-art in technology, regulation, and theory. It also presents a holistic view of research challenges and opportunities in the emerging areas of localization.