A5045795462- Sarcoma Diagnosis and Treatment
- Vascular Tumors and Angiosarcomas
- Cardiac tumors and thrombi
- Transplantation: Methods and Outcomes
- Organ and Tissue Transplantation Research
- Musculoskeletal synovial abnormalities and treatments
- Head and Neck Cancer Studies
- Orthopedic Surgery and Rehabilitation
- Wound Healing and Treatments
- Mast cells and histamine
- Head and Neck Surgical Oncology
- Organ Donation and Transplantation
- Orthopedic Infections and Treatments
- Injury Epidemiology and Prevention
- Reconstructive Surgery and Microvascular Techniques
- Burn Injury Management and Outcomes
Ruhr University Bochum
2018-2025
BG University Hospital Bergmannsheil Bochum
2018-2025
University Hospital Bonn
2020
Septic arthritis of the wrist is a rare but severe condition requiring urgent diagnosis and treatment to prevent joint destruction functional impairment. The objective this study was investigate prognostic parameters long-term outcomes. This retrospective prospective cohort included 44 patients treated for septic between 2008 2024. All underwent surgical arthrotomy due concomitant soft tissue involvement, with median follow-up 29 months. Clinical outcomes were assessed through total active...
Abstract Introduction Predicting burn mortality remains a critical aspect of care, guiding clinical decision making. Recent studies have highlighted the limitations traditional scores such as Baux or ABSI, and modified versions existing been introduced over time. Advances in care - including wound resuscitation protocols practices underscore need for updated prediction models that reflect modern treatment. In particular, potential new technologies, particularly machine learning, offers...
Background: Non-otherwise specified (NOS) sarcomas, a diverse and diagnostically challenging group of mesenchymal malignancies, pose significant clinical dilemmas due to their variable trajectories therapeutic responses. This study utilizes advanced machine learning techniques, namely classification regression trees Shapley additive explanation (SHAP) values, identify predictors survival, metastatic progression, recurrence within well-defined patient cohort, aiming improve risk...
Background: Sarculator and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms are freely available risk prediction scores for surgically treated patients with primary sarcomas. Due to the rarity of angiosarcomas, these have only been tested on small cohorts angiosarcoma patients. In neither original patient cohort upon which is based nor in subsequent studies was a distinction made between secondary as app intended be applied Therefore, objective our investigation assess whether...
Background Chronic rejection remains the Achilles heel in vascularized composite allotransplantation. Animal models to specifically study chronic allotransplantation do not exist so far. However, there are established rat solid organ transplantation such as allogeneic between strains Lewis and Fischer344. Thus, we initiated this investigate applicability of hindlimb these imitate identify potential markers. Methods Allogeneic were performed (recipient) Fischer344 (donor) rats with either...
Zusammenfassung Das Plattenepithelkarzinom ist das häufigste Malignom im Bereich der Mundhöhle, des Pharynx und Larynx. Auch Zeitalter modernster medikamentöser Behandlungsverfahren bleibt derzeit die radikale Resektion dieser Tumoren therapeutische Goldstandard. Der mit Operation verbundene Verlust anatomischer Strukturen verstärkt zwangsläufig durch den Tumor selbst hervorgerufenen Funktionsstörungen. Dabei wird Umfang funktionellen Defizite maßgeblich vom Resektionsausmaß bestimmt....
The mortality of severely burned patients can be predicted by multiple scores which have been created over the last decades. As treatment burn injuries and intensive care management improved immensely years, former prediction seem to losing accuracy in predicting survival. Therefore, various modifications existing established innovative introduced. In this study, we used data from German Burn Registry analyzed them regarding patient using different methods machine learning. We Classification...