Simultaneous pancreas and kidney transplantation as the standard surgical treatment for diabetes mellitus patients with end-stage renal disease

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A multi-functional PEGylated gold(iii) compound: potent anti-cancer properties and self-assembly into nanostructures for drug co-delivery

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Remote Data Retrieval for Bioinformatics Applications: An Agent Migration Approach

Some of the approaches have been developed to retrieve data automatically from one or multiple remote biological data sources. However, most of them require researchers to remain online and wait for returned results. The latter not only requires highly available network connection, but also may cause the network overload. Moreover, so far none of the existing approaches has been designed to address the following problems when retrieving the remote data in a mobile network environment: (1) the resources of mobile devices are limited; (2) network connection is relatively of low quality; and (3) mobile users are not always online. To address the aforementioned problems, we integrate an agent migration approach with a multi-agent system to overcome the high latency or limited bandwidth problem by moving their computations to the required resources or services. More importantly, the approach is fit for the mobile computing environments. Presented in this paper are also the system architecture, the migration strategy, as well as the security authentication of agent migration. As a demonstration, the remote data retrieval from GenBank was used to illustrate the feasibility of the proposed approach

Shoulder pain due to cervical radiculopathy: an underestimated long-term complication of herpes zoster virus reactivation?

Purpose To evaluate if herpes zoster virus (HZV) reactivation may be considered in the aetiology of cervical radiculopathy. Methods The study group was composed of 110 patients (52 M-58F;mean age ± SD:46.5 ± 6.12; range:40-73) with a clinical diagnosis of cervical radiculopathy. Patients with signs of chronic damage on neurophysiological studies were submitted to an X-ray and to an MRI of the cervical spine in order to clarify the cause of the cervical radiculopathy and were investigated for a possible reactivation of HZV; HZV reactivation was considered as “recent” or “antique” if it occurs within or after 24 months from the onset of symptoms, respectively. Data were submitted to statistics. Results Thirty-eight patients (34,5%,16 M-22F) had a history of HZV reactivation: four (2 M-2F) were “recent” and 34 (14 M-20F) were “antique”. In 68 of 110 participants (61,8%,30 M-38F), pathological signs on X-ray and/or MRI of the cervical spine appeared; in the remaining 42 (38,2%,22 M-20F) X-ray and MRI resulted as negative. Among patients with HZV reactivation, seven (18,4%) had a “positive” X-ray-MRI while in 31 (81,6%) the instrumental exams were considered as negative. The prevalence of “antique” HZV reactivations was statistically greater in the group of patients with no pathological signs on X-ray/MRI of the cervical spine with respect to the group with a pathological instrumental exam (p < 0.01). Conclusions It may be useful to investigate the presence of a positive history of HZV reactivation and to consider it as a long-term complication of a cervical root inflammation especially in patients in which X-ray and MRI of the cervical spine did not show pathological findings

Astrocytic Ion Dynamics: Implications for Potassium Buffering and Liquid Flow

We review modeling of astrocyte ion dynamics with a specific focus on the implications of so-called spatial potassium buffering, where excess potassium in the extracellular space (ECS) is transported away to prevent pathological neural spiking. The recently introduced Kirchoff-Nernst-Planck (KNP) scheme for modeling ion dynamics in astrocytes (and brain tissue in general) is outlined and used to study such spatial buffering. We next describe how the ion dynamics of astrocytes may regulate microscopic liquid flow by osmotic effects and how such microscopic flow can be linked to whole-brain macroscopic flow. We thus include the key elements in a putative multiscale theory with astrocytes linking neural activity on a microscopic scale to macroscopic fluid flow.Comment: 27 pages, 7 figure

Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine

In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised therapy of the individual. This article will provide an overview of the various putative tear fluid biomarkers that have been identified to date, ranging from ocular surface disease and retinopathies to cancer and multiple sclerosis. Putative tear fluid biomarkers of ocular disorders, as well as the more recent field of systemic disease biomarkers, will be shown

An examination of knowledge, attitudes and practices related to lead exposure in South Western Nigeria

BACKGROUND: Lead is a highly toxic and pervasive metal. Chronic exposure to low levels is responsible for significant health effects, particularly in children. Prevention remains the best option for reducing childhood lead exposure, however the knowledge, attitudes and practices to lead exposure in many developing countries is not known. Methods: We conducted four focus group discussions (FGD) to evaluate knowledge attitudes and practices to lead exposure in Nigeria. An FGD guide was developed from the literature and preliminary discussion with members of the public. Participants in the FGD were randomly selected from adults living in Ibadan, South Western Nigeria in 2004. RESULTS: We found that there was limited awareness of the sources of lead exposure in the domestic environment and participants had little knowledge of the health effects of chronic low-dose lead exposure. CONCLUSION: We conclude that the findings of this study should be used, in conjunction with others, to develop appropriate health education intervention for lead exposure in the domestic environment

Stable optical trapping and sensitive characterization of nanostructures using standing- wave Raman tweezers

Optical manipulation and label-free characterization of nanoscale structures open up new possibilities for assembly and control of nanodevices and biomolecules. Optical tweezers integrated with Raman spectroscopy allows analyzing a single trapped particle, but is generally less effective for individual nanoparticles. The main challenge is the weak gradient force on nanoparticles that is insufficient to overcome the destabilizing effect of scattering force and Brownian motion. Here, we present standing-wave Raman tweezers for stable trapping and sensitive characterization of single isolated nanostructures with a low laser power by combining a standing-wave optical trap with confocal Raman spectroscopy. This scheme has stronger intensity gradients and balanced scattering forces, and thus can be used to analyze many nanoparticles that cannot be measured with single-beam Raman tweezers, including individual single-walled carbon nanotubes (SWCNT), graphene flakes, biological particles, SERS-active metal nanoparticles, and high-refractive semiconductor nanoparticles. This would enable sorting and characterization of specific SWCNTs and other nanoparticles based on their increased Raman fingerprints

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date