Tumor cell-derived Timp-1 is necessary for maintaining metastasis-promoting Met-signaling via inhibition of Adam-10.

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A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia

Background: This paper presents a new agent-based model (ABM) for investigating T. b. rhodesiense human African trypanosomiasis (rHAT) disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies. Methods: The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The method offers a complementary approach to traditional compartmentalised modelling techniques, permitting incorporation of fine scale demographic data such as ethnicity, age and gender into the simulation. Results: Through identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be readily generated by other techniques. On average there were 1.99 (S.E. 0.245) human infections and 1.83 (S.E. 0.183) cattle infections per 6 month period. The model output identified that the approximate incidence rate (per 1000 person-years) was lower amongst cattle owning households (0.079, S.E. 0.017), than those without cattle (0.134, S.E. 0.017). Immigrant tribes (e.g. Bemba I.R. = 0.353, S.E.0.155) and school-age children (e.g. 5–10 year old I.R. = 0.239, S.E. 0.041) were the most at-risk for acquiring infection. These findings have the potential to aid the targeting of future mitigation strategies. Conclusion: ABMs provide an alternative way of thinking about HAT and NTDs more generally, offering a solution to the investigation of local-scale questions, and which generate results that can be easily disseminated to those affected. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale. © 2016 Alderton et al

Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems:An Example from Eastern Province, Zambia

In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra’s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread

Tissue inhibitor of metalloproteinases (TIMP)-1 creates a premetastatic niche in the liver through SDF-1/CXCR4-dependent neutrophil recruitment in mice.

UNLABELLED: Due to its ability to inhibit prometastatic matrix metalloproteinases, tissue inhibitor of metalloproteinases (TIMP)-1 has been thought to suppress tumor metastasis. However, elevated systemic levels of TIMP-1 correlate with poor prognosis in cancer patients, suggesting a metastasis-stimulating role of TIMP-1. In colorectal cancer patients, tumor as well as plasma TIMP-1 levels were correlated with synchronous liver metastasis or distant metastasis-associated disease relapse. In mice, high systemic TIMP-1 levels increased the liver susceptibility towards metastasis by triggering the formation of a premetastatic niche. This promoted hepatic metastasis independent of origin or intrinsic metastatic potential of tumor cells. High systemic TIMP-1 led to increased hepatic SDF-1 levels, which in turn promoted recruitment of neutrophils to the liver. Both inhibition of SDF-1-mediated neutrophil recruitment and systemic depletion of neutrophils reduced TIMP-1-induced increased liver susceptibility towards metastasis. This indicates a crucial functional role of neutrophils in the TIMP-1-induced premetastatic niche. CONCLUSION: Our results identify TIMP-1 as an essential promoter of hepatic premetastatic niche formation. (Hepatology 2015;61:238-248)

Local Disease–Ecosystem–Livelihood Dynamics: Reflections from Comparative Case Studies in Africa

This article explores the implications for human health of local interactions between disease, ecosystems and livelihoods. Five interdisciplinary case studies addressed zoonotic diseases in African settings: Rift Valley fever (RVF) in Kenya, human African trypanosomiasis in Zambia and Zimbabwe, Lassa fever in Sierra Leone and henipaviruses in Ghana. Each explored how ecological changes and human–ecosystem interactions affect pathogen dynamics and hence the likelihood of zoonotic spillover and transmission, and how socially differentiated peoples’ interactions with ecosystems and animals affect their exposure to disease. Cross-case analysis highlights how these dynamics vary by ecosystem type, across a range from humid forest to semi-arid savannah; the significance of interacting temporal and spatial scales; and the importance of mosaic and patch dynamics. Ecosystem interactions and services central to different people's livelihoods and well-being include pastoralism and agro-pastoralism, commercial and subsistence crop farming, hunting, collecting food, fuelwood and medicines, and cultural practices. There are synergies, but also tensions and trade-offs, between ecosystem changes that benefit livelihoods and affect disease. Understanding these can inform ‘One Health’ approaches towards managing ecosystems in ways that reduce disease risks and burdens. This article is part of the themed issue ‘One Health for a changing world: zoonoses, ecosystems and human well-being’