Creating realistic nerve agent victim profiles for computer simulation of medical CBRN disaster response.

In the last decades, Chemical, Biological, Radiological and Nuclear (CBRN) threats have become serious risks prompting countries to prioritize preparedness for such incidents. As CBRN scenarios are very difficult and expensive to recreate in real life, computer simulation is particularly suited for assessing the effectiveness of contingency plans and identifying areas of improvement. These computer simulation exercises require realistic and dynamic victim profiles, which are unavailable in a civilian context. In this paper we present a set of civilian nerve agent injury profiles consisting of clinical parameters and their evolution, as well as the methodology used to create them. These injury profiles are based on military injury profiles and adapted to the civilian population, using sarin for the purpose of illustration. They include commonly measured parameters in the prehospital setting. We demonstrate that information found in military sources can easily be adjusted for a civilian population using a few simple assumptions and validated methods. This methodology can easily be expanded to other chemical warfare agents as well as different ways of exposure. The resulting injury profiles are generic so they can also be used in tabletop and live simulation exercises. Modeling and simulation, if used correctly and in conjunction with empirical data gathered from lessons learned, can assist in providing the evidence practices for effective and efficient response decisions and interventions, considering the contextual factors of the affected area and the specific disaster scenario.

Maximum entropy networks for large scale social network node analysis.

Recently proposed computational techniques allow the application of various maximum entropy network models at a larger scale. We focus on disinformation campaigns and apply different maximum entropy network models on the collection of datasets from the Twitter information operations report. For each dataset, we obtain additional Twitter data required to build an interaction network. We consider different interaction networks which we compare to an appropriate null model. The null model is used to identify statistically significant interactions. We validate our method and evaluate to what extent it is suited to identify communities of members of a disinformation campaign in a non-supervised way. We find that this method is suitable for larger social networks and allows to identify statistically significant interactions between users. Extracting the statistically significant interaction leads to the prevalence of users involved in a disinformation campaign being higher. We found that the use of different network models can provide different perceptions of the data and can lead to the identification of different meaningful patterns. We also test the robustness of the methods to illustrate the impact of missing data. Here we observe that sampling the correct data is of great importance to reconstruct an entire disinformation operation.

Patched 1 Expression Correlates with Biochemical Relapse in High-Risk Prostate Cancer Patients.

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.

Tissue microarray analysis indicates hedgehog signaling as a potential prognostic factor in intermediate-risk prostate cancer.

BACKGROUND: Prostate 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. METHODS: In 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). RESULTS: The 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. CONCLUSION: Hh 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.

SIMEDIS: a Discrete-Event Simulation Model for Testing Responses to Mass Casualty Incidents.

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.