Understanding fatal and non-fatal drug overdose risk factors : overdose risk questionnaire pilot study—validation

Data collection and analysis was supported by the Health Informatics Centre, Farr Institute, the University of Dundee. In addition, NHS Fife R&D, NHS Education for Scotland, and the Medical School of the University of St Andrews supported the project.Background: Drug overdoses (fatal and non-fatal) are among the leading causes of death in population with substance use disorders. The aim of the current study was to identify risk factors for fatal and non-fatal drug overdose for predominantly opioid-dependent treatment–seeking population. Methods: Data were collected from 640 adult patients using a self-reported 25-item Overdose Risk (OdRi) questionnaire pertaining to drug use and identified related domains. The exploratory factor analysis (EFA) was primarily used to improve the interpretability of this questionnaire. Two sets of EFA were conducted; in the first set of analysis, all items were included, while in the second set, items related to the experience of overdose were removed. Logistic regression was used for the assessment of latent factors’ association with both fatal and non-fatal overdoses. Results: EFA suggested a three-factor solution accounting for 75 and 97% of the variance for items treated in the first and second sets of analysis, respectively. Factor 1 was common for both sets of EFA analysis, containing six items (Cronbach’s α = 0.70) focusing around “illicit drug use and lack of treatment.” In the first set of analysis, Factors 2 (Cronbach’s α = 0.60) and 3 (Cronbach’s α = 0.34) were focusing around “mental health and emotional trauma” and “chronic drug use and frequent overdose” domains, respectively. The increase of Factor 2 was found to be a risk factor for fatal drug overdose (adjusted coefficient = 1.94, p = 0.038). In the second set of analysis, Factors 2 (Cronbach’s α = 0.65) and 3 (Cronbach’s α = 0.59) as well as Factor 1 were found to be risk factors for non-fatal drug overdose ever occurring. Only Factors 1 and 3 were positively associated with non-fatal overdose (one in a past year). Conclusion: The OdRi tool developed here could be helpful for clinical studies for the overdose risk assessment. However, integrating validated tools for mental health can probably help refining the accuracy of latent variables and the questionnaire’s consistency. Mental health and life stress appear as important predictors of both fatal and non-fatal overdoses.Publisher PDFPeer reviewe

Association between chronic psychoactive substances use and systemic inflammation : a systematic review and meta-analysis

This systematic review and meta-analysis assess the change in inflammation biomarkers level among chronic psychoactive substance users. To meet the required inclusion criteria, all studies had to describe human participants with an age ≥18y., experiencing chronic psychostimulant (nicotine, amphetamine, cocaine), sedative (benzodiazepine, opioids) and/or cannabinoid use. The comparison group was defined as healthy participants. Studies where included if they reported at least one of the pro/inflammatory biomarkers. Study bias was examined by Funnel plots and heterogeneity by computing the I2 statistics. Only 21 eligible studies were selected based on 26216 study participants. A small and significant effect size of 0.18mg/L (95% CI:0.10-0.27) was detected in favor of chronic smokers (z=4.33;P<0.0001). There was evidence of publication bias for studies measuring IL-6 and IL-10 association with cocaine and IL-6 in association with cannabis. In summary, except for chronic tobacco users, there was no evidence of association between other chronic substances abuse and inflammatory levels. More studies are needed to inform policy and decision makers about the utility of anti-inflammatory based targeted intervention programs.PostprintPeer reviewe

AlexSys: a knowledge-based expert system for multiple sequence alignment construction and analysis

Multiple sequence alignment (MSA) is a cornerstone of modern molecular biology and represents a unique means of investigating the patterns of conservation and diversity in complex biological systems. Many different algorithms have been developed to construct MSAs, but previous studies have shown that no single aligner consistently outperforms the rest. This has led to the development of a number of ‘meta-methods’ that systematically run several aligners and merge the output into one single solution. Although these methods generally produce more accurate alignments, they are inefficient because all the aligners need to be run first and the choice of the best solution is made a posteriori. Here, we describe the development of a new expert system, AlexSys, for the multiple alignment of protein sequences. AlexSys incorporates an intelligent inference engine to automatically select an appropriate aligner a priori, depending only on the nature of the input sequences. The inference engine was trained on a large set of reference multiple alignments, using a novel machine learning approach. Applying AlexSys to a test set of 178 alignments, we show that the expert system represents a good compromise between alignment quality and running time, making it suitable for high throughput projects. AlexSys is freely available from http://alnitak.u-strasbg.fr/∼aniba/alexsys

Feasibility of cardiac MR thermometry at 0.55 T

Radiofrequency catheter ablation is an established treatment strategy for ventricular tachycardia, but remains associated with a low success rate. MR guidance of ventricular tachycardia shows promises to improve the success rate of these procedures, especially due to its potential to provide real-time information on lesion formation using cardiac MR thermometry. Modern low field MRI scanners (&lt;1 T) are of major interest for MR-guided ablations as the potential benefits include lower costs, increased patient access and device compatibility through reduced device-induced imaging artefacts and safety constraints. However, the feasibility of cardiac MR thermometry at low field remains unknown. In this study, we demonstrate the feasibility of cardiac MR thermometry at 0.55 T and characterized its in vivo stability (i.e., precision) using state-of-the-art techniques based on the proton resonance frequency shift method. Nine healthy volunteers were scanned using a cardiac MR thermometry protocol based on single-shot EPI imaging (3 slices in the left ventricle, 150 dynamics, TE = 41 ms). The reconstruction pipeline included image registration to align all the images, multi-baseline approach (look-up-table length = 30) to correct for respiration-induced phase variations, and temporal filtering to reduce noise in temperature maps. The stability of thermometry was defined as the pixel-wise standard deviation of temperature changes over time. Cardiac MR thermometry was successfully acquired in all subjects and the stability averaged across all subjects was 1.8 ± 1.0°C. Without multi-baseline correction, the overall stability was 2.8 ± 1.6°C. In conclusion, cardiac MR thermometry is feasible at 0.55 T and further studies on MR-guided catheter ablations at low field are warranted

Adequacy of Iodine Status and Associations with Gut Health : A Prospective Cohort Study among Infants in 8 Low- and Middle-Income Countries

Peer reviewe

Manifold-driven Grouping of Skeletal Muscle Fibers

In this report, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. To this end, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. The obtained metrics between fiber tracts encompasses both diffusion and localization information. As far as clustering is concerned, we use two methods. The first approach is based on diffusion maps and k-means clustering in the spectral embedding space. The second approach uses a linear programming formulation of prototype-based clustering. This formulation allows for classification over manifolds without the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. The experimental validation of the proposed framework is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects