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Fish is a good source of protein; however, certain anthropogenic activities can contaminate their habitat with elevated heavy metals levels. In this study, copper, lead, mercury, cadmium, and arsenic in fish tissue, water and sediment were determined using PerkinElmer PINAAcle 900T Graphite AAS. Standard indices in human health risk assessment were used to estimate non-carcinogenic implications associated with consuming Clarias batrachus, Clarias gariepinus, Hemichromis fasciatus, Chrysichthys nigrodigitatus, and Macrobrachium rosenbergii from Densu River. Heavy metal concentration levels recorded in November 2017 were in the order of surface water < pelagic fish < benthic fishes < sediments. Cadmium and Lead levels in all investigated fish tissues exceeded FAO/WHO recommended standard. Pb, Cd, and Hg mean concentration levels in the water exceeded the WHO threshold level of 0.01, 0.003, and 0.001mg/kg, respectively. Concentration level of all sediment samples was below the USEPA set limit for analyzed heavy metals. From the correlation analysis, Hemichromis fasciatus was identified as an applicable bioindicator for assessing heavy metal pollution because it correlated with water and sediment significantly. Principal component analysis ascribed heavy metal pollution in Densu River to anthropogenic activities along the river. The interpretation of estimated daily intake computation showed that the content of individual heavy metals in the fishes is not likely to endanger the health of the consumers. However, the recorded hazard index for Clarias gariepinus, Clarias batracus, and Macrobrachium rosenbergii exceeded one (HI>1), an indication of a non-cancer risk to consumers.

Computer simulations provide virtual hands-on experience when actual hands-on experience is not possible. To use these simulations in medical science, they need to be able to predict the behavior of actual processes with actual patient-specific geometries. Many uncertainties enter in the process of developing these simulations, starting with creating the geometry. The actual patient-specific geometry is often complex and hard to process. Usually, simplifications to the geometry are introduced in exchange for faster results. However, when simplified, these simulations can no longer be considered patient-specific as they do not represent the actual patient they come from. The ultimate goal is to keep the geometries truly patient-specific without any simplification. However, even without simplifications, the patient-specific geometries are based on medical imaging modalities and consequent use of numerical algorithms to create and process the 3D surface. Multiple users are asked to process medical images of a complex geometry. Their resulting geometries are used to assess how the user’s choices determine the resulting dimensions of the 3D model. It is shown that the resulting geometry heavily depends on user’s choices.