Background: Both stress and prematurity can induce hyperglycemia in the neonatal intensive care unit, which, in turn, is associated with worsened outcomes. Endogenous glucose production (EGP) is the formation of glucose by the body from substrates and contributes to blood glucose (BG) levels. Due to the inherent fragility of the extremely low birth weight (ELBW) neonates, true fasting EGP cannot be explicitly determined, introducing uncertainty into glycemic models that rely on quantifying glucose sources. Stochastic targeting, or STAR, is one such glycemic control framework. Methods: A literature review was carried out to gather metabolic and EGP values on preterm infants with a gestational age (GA) <32 weeks and a birth weight (BW) <2 kg. The data were analyzed for EGP trends with BW, GA, BG, plasma insulin, and glucose infusion (GI) rates. Trends were modeled and compared with a literature-derived range of population constant EGP models using clinically validated virtual trials on retrospective clinical data. Results: No clear relationship was found for EGP and BW, GA, or plasma insulin. Some evidence of suppression of EGP with increasing GI or BG was seen. Virtual trial results showed that population-constant EGP models fit clinical data best and gave tighter control performance to a target band in virtual trials. Conclusions: Variation in EGP cannot easily be quantified, and EGP is sufficiently modeled as a population constant in the neonatal intensive care insulin-nutrition-glucose model. Analysis of the clinical data and fitting error suggests that ELBW hyperglycemic preterm neonates have unsuppressed EGP in the higher range than that seen in literature.

The coupled evolution of an eroding cylinder immersed in a fluid within the subcritical Reynolds range is explored with scale resolving simulations. Erosion of the cylinder is driven by fluid shear stress. Kármán vortex shedding features in the wake and these oscillations occur on a significantly smaller time scale compared to the slowly eroding cylinder boundary. Temporal and spatial averaging across the cylinder span allows mean wall statistics such as wall shear to be evaluated; with geometry evolving in 2-D and the flow field simulated in 3-D. The cylinder develops into a rounded triangular body with uniform wall shear stress which is in agreement with existing theory and experiments. We introduce a node shuffle algorithm to reposition nodes around the cylinder boundary with a uniform distribution such that the mesh quality is preserved under high boundary deformation. A cylinder is then modelled within an infinite array of other cylinders by simulating a repeating unit cell and their profile evolution is studied. A similar terminal form is discovered for large cylinder spacings with consistent flow conditions and an intermediate profile was found with a closely packed lattice before reaching the common terminal form.

This study presents a new rheometry technique which requires a free surface velocity field as an input. By minimising the difference between observed and simulated data, we show here that it is possible to estimate the three parameters of an assumed Ellis rheological law. The dam-break problem is considered here with molasses as the working fluid. The free surface velocity is evaluated by seeding the free surface with buoyant particles and using particle tracking velocimetry. The parameter identification is successfully tested with "synthetic" data produced by the numerical model. The parameter identification algorithm is shown to be robust even when significant noise is added to the synthetic dataset. For true experimental data, the reconstructed flow curve is within 25% of the actual one, demonstrating the potential of the method for circumstances where standard rheometry does not apply.

Abstract:Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is assessed to evaluate its efficacy as a diagnostic indicator. Statistical analyses investigate the SOFA score distributions in the days leading up to patient mortality and patient discharge. It is found that the SOFA score is not an effective predictor of patient mortality, but it is a useful tool for prediction of patient discharge from the Intensive Care Unit (ICU). The distribution of overall SOFA score was observed not to change notably in the days leading up to patient death. However, the SOFA score distribution was observed to have a trend towards lower SOFA scores in the days leading up to patient discharge. Finally, assessment of the individual components of the overall SOFA score indicated that the coagulation and cardiovascular scores showed the highest correlation to mortality and are therefore the most useful individual groups to be used as diagnostic indicators.

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