Climate Change Promotes the Emergence of Serious Disease Outbreaks of Filarioid Nematodes

Filarioid parasites represent major health hazards with important medical, veterinary, and economic implications, and considerable potential to affect the everyday lives of tens of millions of people globally (World Health Organization, 2007). Scenarios for climate change vary latitudinally and regionally and involve direct and indirect linkages for increasing temperature and the dissemination, amplification, and invasiveness of vector-borne parasites. High latitude regions are especially influenced by global climate change and thus may be prone to altered associations and dynamics for complex host-pathogen assemblages and emergence of disease with cascading effects on ecosystem structure. Although the potential for substantial ecological perturbation has been identified, few empirical observations have emanated from systems across the Holarctic. Coincidental with decades of warming, and anomalies of high temperature and humidity in the sub-Arctic region of Fennoscandia, the mosquito-borne filarioid nematode Setaria tundra is now associated with emerging epidemic disease resulting in substantial morbidity and mortality for reindeer and moose. We describe a host-parasite system that involves reindeer, arthropods, and nematodes, which may contribute as a factor to ongoing declines documented for this ungulate species across northern ecosystems. We demonstrate that mean summer temperatures exceeding 14°C drive the emergence of disease due to S. tundra. An association between climate and emergence of filarioid parasites is a challenge to ecosystem services with direct effects on public health, sustainability of free-ranging and domestic ungulates, and ultimately food security for subsistence cultures at high latitudes

Predicting outcomes in radiation oncology-multifactorial decision support systems

With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical decision-support systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated decision-support systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner