Indications for implant removal after fracture healing: a review of the literature

Introduction: The aim of this review was to collect and summarize published data on the indications for implant removal after fracture healing, since these are not well defined and guidelines hardly exist. Methods: A literature search was performed. Results: Though there are several presumed benefits of implant removal, such as functional improvement and pain relief, the surgical procedure can be very challenging and may lead to complications or even worsening of the complaints. Research has focused on the safety of metal implants (e.g., risk of corrosion, allergy, and carcinogenesis). For these reasons, implants have been removed routinely for decades. Along with the introduction of titanium alloy implants, the need for implant removal became a subject of debate in view of potential (dis)advantages since, in general, implants made of titanium alloys are more difficult to remove. Currently, the main indications for removal from both the upper and lower extremity are mostly 'relative' and patient-driven, such as pain, prominent material, or simply the request for removal. True medical indications like infection or intra-articular material are minor reasons. Conclusion: This review illustrates the great variety of view points in the literature, with large differences in opinions and practices about the indications for implant removal after fracture healing. Since some studies have described asymptomatic patients developing complaints after removal, the general advice nowadays is to remove implants after fracture healing only in symptomatic patients and after a proper informed consent. Well-designed prospective studies on this subject are urgently needed in order to form guidelines based on scientific evidence

Strategic research agenda for biomedical imaging

This Strategic Research Agenda identifies current challenges and needs in healthcare, illustrates how biomedical imaging and derived data can help to address these, and aims to stimulate dedicated research funding efforts. Medicine is currently moving towards a more tailored, patient-centric approach by providing personalised solutions for the individual patient. Innovation in biomedical imaging plays a key role in this process as it addresses the current needs for individualised prevention, treatment, therapy response monitoring, and image-guided surgery. The use of non-invasive biomarkers facilitates better therapy prediction and monitoring, leading to improved patient outcomes. Innovative diagnostic imaging technologies provide information about disease characteristics which, coupled with biological, genetic and -omics data, will contribute to an individualised diagnosis and therapy approach. In the emerging field of theranostics, imaging tools together with therapeutic agents enable the selection of best treatments and allow tailored therapeutic interventions. For prenatal monitoring, the use of innovative imaging technologies can ensure an early detection of malfunctions or disease. The application of biomedical imaging for diagnosis and management of lifestyle-induced diseases will help to avoid disease development through lifestyle changes. Artificial intelligence and machine learning in imaging will facilitate the improvement of image interpretation and lead to better disease prediction and therapy planning. As biomedical imaging technologies and analysis of existing imaging data provide solutions to current challenges and needs in healthcare, appropriate funding for dedicated research is needed to implement the innovative approaches for the wellbeing of citizens and patients

Urinary Tract Infection and Progression to Pyelonephritis: Group B <i>Streptococcus</i> versus <i>E. coli</i>

Objective Group B Streptococcus (GBS) colonization of the lower urinary tract in pregnancy is associated with severe infections such as chorioamnionitis, endometritis, and pyelonephritis. The objective of this study was to compare rates of progression to pyelonephritis between GBS and Escherichia coli lower urinary tract infections (LUTIs), as well as compare infectious and obstetric morbidity secondary to these pathogens

Biomechanical in vitro assessment of fixed angle plating using a new concept of locking for the treatment of osteoporotic proximal humerus fractures

Locked plating attempts to improve mechanical stability via better anchorage of the screws in the bone. In 22 paired osteoporotic humeri an AO/ASIF 11-B 1 fracture was created. Locked and conventional plating using the same device of the latest generation was performed. Torsional loading around three axes (x = varus/valgus, y = flexion/extension, z = axial rotation) with an increasing moment (2, 3.5, 5 and 7.5 N·m) was applied. Interfragmentary motion within the locked group was lower for all three axes with higher cumulative survival rates (p < 0.05). The typical mode of failure was loss of fixation in the humeral head occurring earlier in the conventional group. The locking mechanism investigated provides more ultimate strength in an osteoporotic proximal humerus fracture model. Correlation with BMD suggests that this device may especially be suitable for use in osteoporotic bone

CHAIMELEON project: creation of a pan-European repository of health imaging data for the development of AI-powered cancer management tools

The CHAIMELEON project aims to set up a pan-European repository of health imaging data to be openly reused in AI experimentation for cancer management. This EU-funded project involves some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the currently four most prevalent types of cancer worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data for historic and newly diagnosed patients. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D Centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology