It is recognized that the study of the disaster medical response (DMR) is a relatively new field. To date, there is no evidence-based literature that clearly defines the best medical response principles, concepts, structures and processes in a disaster setting. Much of what is known about the DMR results from descriptive studies and expert opinion. No experimental studies regarding the effects of DMR interventions on the health outcomes of disaster survivors have been carried out. Traditional analytic methods cannot fully capture the flow of disaster victims through a complex disaster medical response system (DMRS). Computer modelling and simulation enable to study and test operational assumptions in a virtual but controlled experimental environment. The SIMEDIS (Simulation for the assessment and optimization of medical disaster management) simulation model consists of 3 interacting components: the victim creation model, the victim monitoring model where the health state of each victim is monitored and adapted to the evolving clinical conditions of the victims, and the medical response model, where the victims interact with the environment and the resources at the disposal of the healthcare responders. Since the main aim of the DMR is to minimize as much as possible the mortality and morbidity of the survivors, we designed a victim-centred model in which the casualties pass through the different components and processes of a DMRS. The specificity of the SIMEDIS simulation model is the fact that the victim entities evolve in parallel through both the victim monitoring model and the medical response model. The interaction between both models is ensured through a time or medical intervention trigger. At each service point, a triage is performed together with a decision on the disposition of the victims regarding treatment and/or evacuation based on a priority code assigned to the victim and on the availability of resources at the service point. The aim of the case study is to implement the SIMEDIS model to the DMRS of an international airport and to test the medical response plan to an airplane crash simulation at the airport. In order to identify good response options, the model then was used to study the effect of a number of interventional factors on the performance of the DMRS. Our study reflects the potential of SIMEDIS to model complex systems, to test different aspects of DMR, and to be used as a tool in experimental research that might make a substantial contribution to provide the evidence base for the effectiveness and efficiency of disaster medical management.Electronic supplementary materialThe online version of this article (doi:10.1007/s10916-016-0633-z) contains supplementary material, which is available to authorized users.

There is an unmet clinical need for adequate biomarkers to aid risk stratification and management of prostate cancer (PCa) patients. Even within the high-risk PCa category, not all patients will invariably have a poor prognosis, and improved stratification of this heterogeneous group is needed. In this context, components of the hedgehog (Hh) pathway may have promise as biomarkers, because the available evidence suggests increased Hh pathway activity may confer a poorer outcome in advanced and castrate-resistant PCa. In this study, potential associations between Hh pathway protein expression and clinicopathological factors, including time to biochemical recurrence (BCR), were investigated using a tissue microarray constructed from benign and malignant prostate samples from 75 predominantly high-risk PCa patients who underwent radical prostatectomy. Hh signaling activity was found to differ between benign and malignant prostate tissue, with a greater amount of active Hh signaling present in malignant than benign prostate epithelium. High expression of Patched 1 in malignant prostate epithelium was found to be an independent predictor of BCR in high-risk PCa patients. Glioma-associated oncogene 1 may potentially represent a clinically useful biomarker of an aggressive tumor phenotype. Evaluation of Hh signaling activity in PCa patients may be useful for risk stratification, and epithelial Patched 1 expression, in particular, may be a prognostic marker for BCR in high-risk PCa patients.

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Identifying and detecting disinformation is a major challenge. Twitter provides datasets of disinformation campaigns through their information operations report. We compare the results of community detection using a classical network representation with a maximum entropy network model. We conclude that the latter method is useful to identify the most significant interactions in the disinformation network over multiple datasets. We also apply the method to a disinformation dataset related to COVID-19, which allows us to assess the repeatability of studies on disinformation datasets.

BackgroundProstate cancer (PCa) is a heterogeneous disease with a variable natural history, genetics, and treatment outcome. The Hedgehog (Hh) signaling pathway is increasingly recognized as being potentially important for the development and progression of PCa. In this retrospective study, we compared the activation status of the Hh signaling pathway between benign and tumor tissue, and evaluated the clinical significance of Hh signaling in PCa.MethodsIn this tissue microarray (TMA) study, the protein expression of several Hh signaling components and Hh target proteins, along with microvessel density, were compared between benign (n = 64) and malignant (n = 170) prostate tissue, and correlated with PCa clinicopathological characteristics and biochemical recurrence (BCR).ResultsThe Hh signaling pathway appeared to be more active in PCa than in benign prostate tissue, as demonstrated by lower expression of the negative regulators PTCH1 and GLI3 in the tumor tissue compared to benign. In addition, high epithelial GLI2 expression correlated with higher pathological Gleason score. Overall, higher epithelial GLI3 expression in the tumor was shown to be an independent marker of a favorable prognosis.ConclusionHh signaling activation might reflect aggressive tumoral behavior, since high epithelial GLI2 expression positively correlates with a higher pathological Gleason score. Moreover, higher epithelial GLI3 expression is an independent marker of a more favorable prognosis.Electronic supplementary materialThe online version of this article (10.1186/s12885-017-3619-4) contains supplementary material, which is available to authorized users.