Hepatitis C Virus Infection Epidemiology among People Who Inject Drugs in Europe: A Systematic Review of Data for Scaling Up Treatment and Prevention

Background: People who inject drugs (PWID) are a key population affected by hepatitis C virus (HCV). Treatment options are improving and may enhance prevention; however access for PWID may be poor. The availability in the literature of information on seven main topic areas (incidence, chronicity, genotypes, HIV co-infection, diagnosis and treatment uptake, and burden of disease) to guide HCV treatment and prevention scale-up for PWID in the 27 countries of the European Union is systematically reviewed. Methods and Findings: We searched MEDLINE, EMBASE and Cochrane Library for publications between 1 January 2000 and 31 December 2012, with a search strategy of general keywords regarding viral hepatitis, substance abuse and geographic scope, as well as topic-specific keywords. Additional articles were found through structured email consultations with a large European expert network. Data availability was highly variable and important limitations existed in comparability and representativeness. Nine of 27 countries had data on HCV incidence among PWID, which was often high (2.7-66/100 person-years, median 13, Interquartile range (IQR) 8.7–28). Most common HCV genotypes were G1 and G3; however, G4 may be increasing, while the proportion of traditionally ‘difficult to treat’ genotypes (G1+G4) showed large variation (median 53, IQR 43–62). Twelve countries reported on HCV chronicity (median 72, IQR 64–81) and 22 on HIV prevalence in HCV-infected PWID (median 3.9%, IQR 0.2–28). Undiagnosed infection, assessed in five countries, was high (median 49%, IQR 38–64), while of those diagnosed, the proportion entering treatment was low (median 9.5%, IQR 3.5–15). Burden of disease, where assessed, was high and will rise in the next decade. Conclusion: Key data on HCV epidemiology, care and disease burden among PWID in Europe are sparse but suggest many undiagnosed infections and poor treatment uptake. Stronger efforts are needed to improve data availability to guide an increase in HCV treatment among PWID

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987

Low frequency phase synchronisation analysis of MEG recordings from children with ADHD and controls using single channel ICA

It has been suggested that the human brain is intrinsically organised into dynamic, anti-correlated functional networks. This paper presents a study on the so-called default mode network - which is active when the brain is apparently at rest - and on brain activity related to a given task. This work involves the analysis of low frequency magnetoencephalographic recordings of children with Attention Deficit Hyperactivity Disorder (ADHD) and controls performing both attentional as well as perceptual tasks. Single channel independent component analysis is used to isolate low frequency brain signals within the data in the presence of higher frequency brain activity and artifacts. Phase synchrony analysis is then carried out between the components of channels of interest to quantify any interaction between distant brain regions within the default-mode network. Preliminary results show variations in the phase locking between ADHD and controls, and indicate a corresponding change in phase synchrony between the corresponding brain regions at periods of rest and when tasks are being performed

On spatio-temporal component selection in space-time independent component analysis: an application to ictal EEG

This paper assesses the use of independent component analysis (ICA) as applied to epileptic scalp electroencephalographic (EEG) recordings. In particular we address the newly introduced spatio-temporal ICA algorithm (ST-ICA), which uses both spatial and temporal information derived from multi-channel biomedical signal recordings to inform (or update) the standard ICA algorithm. ICA is a technique well suited to extracting underlying sources from multi-channel EEG recordings - for ictal EEG recordings, the goal is to both de-noise the EEG recordings (i.e. remove artifacts) as well as isolate and extract epileptic processes. As part of any ICA application, there is an interim stage whereby relevant components (or processes) need to be identified - either objectively or subjectively (usually the latter). In previous work with ST-ICA we used spectral information alone to identify the underlying processes subspaces extracted by the ST-ICA. Here we assess the joint use of spatial as well as spectral information for this purpose. We test this on ictal EEG segments where it can be seen that different underlying processes possess characteristic signatures in both modalities which can be utilized for the clustering (or process selection) stage

Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals

Background:It has been acknowledged that the frequency spectrum of measured electromagnetic (EM) brain signals shows a decrease in power with increasing frequency. This spectral behaviour may lead to difficulty in distinguishing event-related peaks from ongoing brain activity in the electro- and magnetoencephalographic (EEG and MEG) signal spectra. This can become an issue especially in the analysis of low frequency oscillations (LFOs) - below 0.5 Hz - which are currently being observed in signal recordings linked with specific pathologies such as epileptic seizures or attention deficit hyperactivity disorder (ADHD), in sleep studies, etc. Methods:In this work we propose a simple method that can be used to compensate for this 1/f trend hence achieving spectral whitening. This method involves filtering the raw measured EM signal through a differentiator prior to further data analysis. Results:Applying the proposed method to various exemplary datasets including very low frequency EEG recordings, epileptic seizure recordings, MEG data and evoked response data showed that this compensating procedure provides a flat spectral base onto which event related peaks can be clearly observed.Conclusions:Findings suggest that the proposed filter is a useful tool for the analysis of physiological data especially in revealing very low frequency peaks which may otherwise be obscured by the 1/f spectral activity inherent in EEG/MEG recordings.<br/

Extracting event-related field components through space-time ICA:A study of MEG recording from children with ADHD and controls

Event-related studies have provided indirect evidence that Attention Deficit Hyperactivity Disorder (ADHD) children have abnormalities in signal detection and discrimination, and in information processing. Moreover, studies suggest that there exist very low frequency fluctuations modulating underlying neuronal events. This paper presents an event-related fields (ERF) study involving the analysis of magnetoencephalographic (MEG) recordings of children with ADHD and controls during selective attention and perceptual, stimuli-based tasks. A specific form of blind-source separation — space-time independent component analysis (ST-ICA) — is used to isolate the early M100 responses within the data, which are indicative of selective attention deficits. The properties of the extracted responses, namely the amplitude and latency, as well as the power spectral densities of their inter-trial variations are then analyzed. Preliminary results demonstrate the ability of ST-ICA to extract relevant components from multi-dimensional, noisy, ERF data, and reveal differences in the amplitude and latency variations of the M100 responses of the two groups

Using localised weather files to assess overheating in naturally ventilated offices within London’s urban heat island

Urban environments typically experience increased average air temperatures compared to surrounding rural areas – a phenomenon referred to as the Urban Heat Island (UHI). The impact of the UHI on comfort in naturally ventilated buildings is the main focus of this article. The overheating risk in urban buildings is likely to be exacerbated in the future as a result of the combined effect of the UHI and climate change. In the design of such buildings in London, the usual current practice is to view the use of one generic weather file as being adequate to represent external temperatures. However, the work reported here demonstrates that there is a considerable difference between the overheating performance of a standard building at different sites within London. This implies, for example, that a building may wrongly pass or fail criteria used to demonstrate compliance with building regulations as a result of an inappropriate generic weather file being used. The work thus has important policy implications

Solubilisation of drugs in worm-like micelles of block copolymers of ethylene oxide and 1,2-butylene oxide in aqueous solution

Ethylene oxide and 1,2-butylene oxide were sequentially polymerised to form the diblock copolymer E13B10 (E=oxyethylene, B=oxybutylene, subscripts denote number-average block lengths in repeat units). Dynamic and static light scattering over the temperature range 10-30 degrees C demonstrated a transition from compact (spheroidal) micelles to larger, more elongated (worm-like) micelles with temperature increase above a critical onset temperature of about 20 degrees C. Determination of the solubilisation capacity for griseofulvin, carbamazepine and spironolactone of dilute micellar solutions of this copolymer, together with those of E11B8 and E17B12 block copolymers (which also show the sphere-to-worm transition), allowed investigation of the influence on solubilisation characteristics of hydrophobic block length and temperature. The extent of solubilisation at 25 degrees C of the poorly water-soluble drug spironolactone increased linearly with increase of hydrophobic block length, attributable to a concomitant increase in the proportion of worm-like micelles in solution