MRI changes in psoriatic dactylitisextent of pathology, relationship to tenderness and correlation with clinical indices

Objectives. To quantify the extent of inflammation in psoriatic dactylitis and to examine the relationship between clinical and magnetic resonance imaging (MRI) data in both tender and non-tender dactylitis. Methods. Seventeen patients with psoriatic dactylitis underwent clinical assessment for 6 months after change of treatment, usually to methotrexate. Measures of dactylitis included the Leeds Dactylitis Index, the assessment tool used in the Infliximab in Psoriatic Arthritis Clinical Trial (IMPACT), a simple count of tender dactlylitic digits and a count of all dactylitic digits, both tender and non-tender. MRI scans of the affected hand or foot were performed before and after treatment using a 1.5T Siemens scanner pre- and post-contrast. Results. All patients improved clinically, as did their respective dactylitis scores and MRI images. The findings on MRI in both dactylitic and non-dactylitic digits were profound and widespread. The difference between tender and non-tender dactylitis was quantitative rather than qualitative. Synovitis and soft-tissue oedema were the most frequent abnormalities being present in 69 of tender dactylitic digits but bone oedema and flexor tenosynovitis were also frequently seen. Soft-tissue oedema was circumferential and enhancing and not limited to association with the flexor or extensor tendons. None of the clinical indices of dactylitis showed a close relationship to the extent of MRI abnormalities. Conclusions. MRI images demonstrate widespread abnormalities in digits of people with psoriatic arthritis. Tender dactylitic digits have more abnormalities than other digits but the relationship between clinical and MRI scores is not strong

Updated estimate of the duration of the meningo-encephalitic stage in gambiense human African trypanosomiasis

Background: The duration of the stages of HAT is an important factor in epidemiological studies and intervention planning. Previously, we published estimates of the duration of the haemo-lymphatic stage 1 and meningo-encephalitic stage 2 of the gambiense form of human African trypanosomiasis (HAT), in the absence of treatment. Here we revise the estimate of stage 2 duration, computed based on data from Uganda and South Sudan, by adjusting observed infection prevalence for incomplete case detection coverage and diagnostic inaccuracy. Findings: The revised best estimate for the mean duration of stage 2 is 252 days (95% CI 171–399), about half of our initial best estimate, giving a total mean duration of untreated gambiense HAT infection of approximately 2 years and 2 months. Conclusions: Our new estimate provides improved information on the transmission dynamics of this neglected tropical disease in Uganda and South Sudan. We stress that there remains considerable variability around the estimated mean values, and that one must be cautious in applying these results to other foci

Mode of action and choice of antimalarial drugs for intermittent preventive treatment in infants.

Intermittent preventive treatment in infants (IPTi) is an effective and safe malaria control strategy. However, it remains unclear what antimalarials should be used to replace sulfadoxine-pyrimethamine (SP) when and where SP is no longer an effective drug for IPTi. Work recently conducted in Tanzania, combined with the findings of previous studies, indicates that IPTi is essentially intermittent chemoprophylaxis; consequently, long-acting antimalarials that provide a long period of post-treatment prophylaxis will be the most effective alternative to SP. However, because of concerns about development of drug resistance, new combinations of long-acting drugs are urgently needed

Response to Buffet et al: Intermittent preventive anti-malarial treatment to children (IPTc): firebreak or fire trap?

Buffet and colleagues have reviewed some of the potential benefits and drawbacks of intermittent preventive antimalarial treatment in children (IPTc). They acknowledge its efficacy but raise concerns about the feasibility of implementing this intervention, its safety, its impact on drug resistance, the possibility that IPTc might impair the development of naturally acquired immunity and its role in a changing environment. We believe that we can address some, although not all, of their concerns. © 2008 Elsevier Ltd. All rights reserved

High Prevalence of Diabetes and Metabolic Syndrome Among Policemen

The prevalence of diabetes is rapidly rising all over the globe at an alarming rate.1 Over the past 30 years, the status of diabetes has changed from being considered a mild disorder of the elderly, to one of the major causes of morbidity and mortality affecting the youth and middle aged. It is important to note that the rise in prevalence is seen in all six inhabited continents of the globe.2 The major driver of the epidemic is the more common form of diabetes namely type 2 diabetes, which accounts more than 90% of all diabetic cases

A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization

Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviours with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterise several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localisation experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall