Positron Emission Tomography-Computed Tomography and Magnetic Resonance Imaging Assessments in a Mouse Model of Implant-Related Bone and Joint Staphylococcus aureus Infection.

Osteomyelitis is an infection of the bone, associated with an inflammatory process. Imaging plays an important role in establishing the diagnosis and the most appropriate patient management. However, data are lacking regarding the use of preclinical molecular imaging techniques to assess osteomyelitis progression in experimental models. This study aimed to compare structural and molecular imaging to assess disease progression in a mouse model of implant-related bone and joint infections caused by Staphylococcus aureus. In SWISS mice, the right femur was implanted with a resorbable filament impregnated with S. aureus (infected group, n = 10) or sterile culture medium (uninfected group, n = 6). Eight animals (5 infected, 3 uninfected) were analyzed with magnetic resonance imaging (MRI) at 1, 2, and 3 weeks postintervention, and 8 mice were analyzed with [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET)-computed tomography (CT) at 48 h and at 1, 2, and 3 weeks postintervention. In infected animals, CT showed bone lesion progression, mainly in the distal epiphysis, although some uninfected animals presented evident bone sequestra at 3 weeks. MRI showed a lesion in the articular area that persisted for 3 weeks in infected animals. This lesion was smaller and less evident in the uninfected group. At 48 h postintervention, FDG-PET showed higher joint uptake in the infected group than in the uninfected group (P = 0.025). Over time, the difference between groups increased. These results indicate that FDG-PET imaging was much more sensitive than MRI and CT for differentiating between infection and inflammation at early stages. FDG-PET clearly distinguished between infection and postsurgical bone healing (in uninfected animals) from 48 h to 3 weeks after implantation. IMPORTANCE Our results encourage future investigations on the utility of the model for testing different therapeutic procedures for osteomyelitis.

Evaluation of Clostridium difficile Infection with PET/CT Imaging in a Mouse Model.

PURPOSE: Existing clinical or microbiological scores are not sensitive enough to obtain prompt identification of patients at risk of complicated Clostridium difficile infection (CDI). Our aim was to use a CDI animal model to evaluate 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]FDG-PET) as a marker of severe course of infection. PROCEDURES: CDI was induced with cefoperazone for 10 days followed by clindamycin 1 day before C. difficile inoculation. Mice were divided into wild type (n = 6), antibiotic without infection (AC n = 4), h001-infected (n = 5, ribotype 001), and h027-infected (n = 5, ribotype 027). Two days after inoculation, [18F]FDG-PET was acquired. Weight, general animal condition, and survival were monitored daily for 9 days. RESULTS: h001 group showed symptoms for 4 days with 0 % mortality and a similar colon uptake than control animals (h001 0.52 ± 0.20, WT 0.42 ± 0.07, and AC 0.36 ± 0.06). The h027 group showed symptoms up to 7 days, with 66.7 % of mortality 4 days after infection, and significantly higher colon uptake (0.93 ± 0.38, p < 0.05). Clinical score was associated to colon and cecum uptake (rho = 0.78, p = 0.0001) (rho = 0.73, p = 0.0003). CONCLUSION: High toxin producer ribotype 027 induced more severe CDI infections, correlating with higher colon and cecum [18F]FDG uptake. Colon uptake may purportedly serve as early predictor of CDI severity.

ITEMAS ontology for healthcare technology innovation.

BACKGROUND: The Platform for Innovation in Medical and Health Technologies (ITEMAS) is a network of 66 healthcare centres focused on fostering innovation in medical and health technologies as an essential tool for increasing the sustainability of the Spanish healthcare system. The present research is focused on defining a formal representation that details the most relevant concepts associated with the creation and adoption of innovative medical technology in the Spanish healthcare system. METHODS: The methodology applied is based on the methontology process, including peer-review identification and selection of concepts from the ITEMAS innovation indicators and innovation management system standards. This stage was followed by an iterative validation process. Concepts were then conceptualised, formalised and implemented in an ontology. RESULTS: The ontology defined describes how relationships between employees, organisations, projects and ideas can be applied to generate results that are transferrable to the market, general public and scientific forums. Overall, we identified 136 concepts, 138 object properties and 30 properties in a five-level hierarchy. The ontology was tested and validated as an appropriate framework for calculating the ITEMAS innovation indicators. CONCLUSIONS: The consensus concepts were expressed in the form of an ontology to be used as a single communication format between the members of the ITEMAS network. Healthcare centres can compare their innovation results and obtain a better understanding of their innovation context based on the reasoning techniques of artificial intelligence. As a result, they can benefit from advanced analytical capabilities to define the most appropriate innovation policies for each centre based on the common experience of the large number of healthcare centres involved. The results can be used to create a map of agents and knowledge to show capabilities, projects and services provided by each of the participating centres. The ontology could also be applied as an instrument to match needs with existing projects and capabilities from the community of organisations working in healthcare technology innovation.