World society of emergency surgery (WSES) guidelines for management of skin and soft tissue infections

Skin and soft tissue infections (SSTIs) encompass a variety of pathological conditions ranging from simple superficial infections to severe necrotizing soft tissue infections. Necrotizing soft tissue infections (NSTIs) are potentially life-threatening infections of any layer of the soft tissue compartment associated with widespread necrosis and systemic toxicity. Successful management of NSTIs involves prompt recognition, timely surgical debridement or drainage, resuscitation and appropriate antibiotic therapy. A worldwide international panel of experts developed evidence-based guidelines for management of soft tissue infections. The multifaceted nature of these infections has led to a collaboration among surgeons, intensive care and infectious diseases specialists, who have shared these guidelines, implementing clinical practice recommendations

World Society of Emergency Surgery (WSES) guidelines for management of skin and soft tissue infections

Peer reviewe

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology