A retrospective analysis of dental implant survival in HIV patients.

AIM: This 5 years retrospective cohort survival study compared failure rates of dental implants placed in HIV (+) and HIV (-) patients relative to several risk factors. MATERIALS AND METHODS: Between 2006 and 2015, 484 implants placed in HIV (+) patients and 805 implants placed in HIV (-) patients were assessed for survival. The effects of HIV were estimated using propensity weighting. The effects of age, smoking status, diabetes, restoration status, gender, implant type, placement site, hepatitis C status, baseline CD4 count and CD4%, post-placement average CD4%, nadir CD4%, nadir CD4 count and antiviral therapy were analysed. RESULTS: Implants placed in HIV (+) patients and HIV (-) patients had similar failure rates (HR = 1.4, p = 0.34). Increased failure rates were observed in HIV (+) patients with baseline CD4% ≤20 (HR = 2.72, p = 0.04), post-placement CD4% average ≤20% (HR = 2.71, p = 0.04), protease inhibitor administration (HR = 2.74, p = 0.04), smoking (HR = 2.61, p = 0.05) and anterior maxillary placement (HR = 5.82, p < 0.01). Hepatitis C coinfection, viral titre, baseline CD4 count, gender, implant type and restoration type were non-contributory. CONCLUSION: Implants placed in HIV (+) patients had similar survival rates as HIV (-) patients. Failure rates increase significantly when confounding risk factors are present in HIV (+) patients.

FDA's nozzle numerical simulation challenge: non-Newtonian fluid effects and blood damage.

Data from FDA's nozzle challenge-a study to assess the suitability of simulating fluid flow in an idealized medical device-is used to validate the simulations obtained from a numerical, finite-differences code. Various physiological indicators are computed and compared with experimental data from three different laboratories, getting a very good agreement. Special care is taken with the derivation of blood damage (hemolysis). The paper is focused on the laminar regime, in order to investigate non-Newtonian effects (non-constant fluid viscosity). The code can deal with these effects with just a small extra computational cost, improving Newtonian estimations up to a ten percent. The relevance of non-Newtonian effects for hemolysis parameters is discussed.

@neurIST: infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services.

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

A service-oriented grid infrastructure for biomedical data and compute services.

Service-oriented Grid technologies are increasingly utilized for the realization of future biomedical IT infrastructures since they offer unprecedented opportunities for the integration of advanced analysis and simulation applications as well as distributed heterogeneous data sources and information systems. The European Union's @neurIST project is developing a Grid-based IT infrastructure for the management of all processes linked to research, diagnosis, and treatment development for complex and multifactorial diseases encompassing data repositories, computational analysis services, and information systems handling multiscale, multimodal information at distributed sites. This paper provides an overview of the @neurIST Grid middleware and outlines the infrastructure offered for the provision of advanced compute and data services to support computationally demanding modeling and simulation tasks and to access heterogeneous distributed data sources through semantic integration.