Non-Invasive Local Diagnosis of Surgical Infections

点击翻译按钮获取中文摘要

 DESCRIPTION (provided by applicant): Surgical site infections (SSI) have become a major public health concern. The presence of a foreign body (implant) significantly increases susceptibility to infection as pathologic microorganisms compete with local host cells to adhere to the surface of surgical implants. This is particularly relevant in orthopedic surgery where more than 2.5 million devices are implanted annually in the United States alone. Strategies to diagnose and treat SSI are critically important in combating this increasingly difficult public health issue. In this project we will implement a novel technology for non-invasively diagnosing acute post-operative infections. The diagnostic system is based on our simple, inexpensive, wireless, implantable sensor platform. The small sensors are integrated with implants requiring minimal or no alteration to the implant itself. To detect infections, we will incorporate an antige-sensitive hydrogel in our simple sensor system which is sensitive to antigens produced by Staphylococcus aureus (the most common microorganism in orthopedic infections). This non-invasive economical diagnostic tool which facilitates early diagnosis of SSI has the potential to "disrupt" current SSI treatment paradigms, reduce costs, improve outcomes, and reduce readmissions. Because of its simplicity, small size, and minimal cost, the proposed system is highly translational. Ultimately, the sensors have the potential to reduce the morbidity and mortality associated with surgical site infection. In this project, we will fabricate antigen-sensitive hydrogels and optimize their sensitivity to antigens associated with S. aureus to achieve our target properties. We will test the hydrogels in solutions containing purified antigens, then in culture. Using candidate hydrogels, we will fabricate wireless sensors which incorporate multiple formulations of the hydrogel and test them in solution and in culture. S. aureus-sensitive sensors will then be tested in vivo in an implant infection model. Sensor sensitivity and specificity will be determined.