Nano-Bio-Technology and Sensing Chips: New Systems for Detection in Personalized Therapies and Cell Biology

Further advances in molecular medicine and cell biology also require new electrochemical systems to detect disease biomarkers and therapeutic compounds. Microelectronic technology offers powerful circuits and systems to develop innovative and miniaturized biochips for sensing at the molecular level. However, microelectronic biochips proposed in the literature often do not show the right specificity, sensitivity, and reliability required by biomedical applications. Nanotechnology offers new materials and solutions to improve the surface properties of sensing probes. The aim of the present paper is to review the most recent progress in Nano-Bio-Technology in the area of the development of new electrochemical systems for molecular detection in personalized therapy and cell culture monitoring

Unique parameter identification for cardiac diagnosis in critical care using minimal data sets.

Lumped parameter approaches for modelling the cardiovascular system typically have many parameters of which a significant percentage are often not identifiable from limited data sets. Hence, significant parts of the model are required to be simulated with little overall effect on the accuracy of data fitting, as well as dramatically increasing the complexity of parameter identification. This separates sub-structures of more complex cardiovascular system models to create uniquely identifiable simplified models that are one to one with the measurements. In addition, a new concept of parameter identification is presented where the changes in the parameters are treated as an actuation force into a feed back control system, and the reference output is taken to be steady state values of measured volume and pressure. The major advantage of the method is that when it converges, it must be at the global minimum so that the solution that best fits the data is always found. By utilizing continuous information from the arterial/pulmonary pressure waveforms and the end-diastolic time, it is shown that potentially, the ventricle volume is not required in the data set, which was a requirement in earlier published work. The simplified models can also act as a bridge to identifying more sophisticated cardiac models, by providing an initial set of patient specific parameters that can reveal trends and interactions in the data over time. The goal is to apply the simplified models to retrospective data on groups of patients to help characterize population trends or un-modelled dynamics within known bounds. These trends can assist in improved prediction of patient responses to cardiac disturbance and therapy intervention with potentially smaller and less invasive data sets. In this way a more complex model that takes into account individual patient variation can be developed, and applied to the improvement of cardiovascular management in critical care

Model-based identification and diagnosis of a porcine model of induced endotoxic shock with hemofiltration

A previously validated cardiovascular system (CVS) model and parameter identification method for cardiac and circulatory disease states are extended and further validated in a porcine model (N = 6) of induced endotoxic shock with hemofiltration. Errors for the identified model are within 10% when the model is re-simulated and compared to the clinical data. All identified parameter trends over time in the experiments match clinically expected changes both individually and over the cohort. This work represents a further clinical validation of these model-based cardiovascular diagnosis and therapy guidance methods for use with monitoring endotoxic disease states

Primary Non-Function of the Liver Allograft.

Severe graft dysfunction as opposed to the expected immediate graft function following liver transplantation is a major complication, and the clinical manifestations of such that lead to either immediate re-transplantation or patient death are the catastrophic end of the spectrum. Primary non-function (PNF) has declined in incidence over the years, yet the impact on patient and healthcare teams, and the burden on the organ pool in case of the need for re-transplant should not be underestimated. There is no universal test to define the diagnosis of PNF and current criteria are based on various biochemical parameters surrogate of liver function, and moreover a disparity remains within different healthcare systems on selecting candidates eligible for urgent re-transplantation. The impact on PNF from traditionally accepted risk factors has changed somewhat, mainly driven by the rising demand for organs, combined with the concerted approach by clinicians on the in-depth understanding of PNF, optimal graft recipient selection, mitigation of clinical environment a marginal graft being reperfused and post-operative management. Regardless of the mode, available data suggests machine perfusion strategies perhaps help reduce the incidence further but does not completely avert the risk of PNF. The mainstay of management relies on identifying severe allograft dysfunction at a very early stage and aggressive management whilst excluding other identifiable causes that mimic severe organ dysfunction. This approach may help salvage some grafts by preventing total graft failure, and also maintaining a patient in an optimal physiological state if re-transplantation is considered the ultimate patient salvage strategy.Supplemental Visual Abstract; http://links.lww.com/TP/C143

Patient specific identification of the cardiac driver function in a cardiovascular system model.

The cardiac muscle activation or driver function, is a major determinant of cardiovascular dynamics, and is often approximated by the ratio of the left ventricle pressure to the left ventricle volume. In an intensive care unit, the left ventricle pressure is usually never measured, and the left ventricle volume is only measured occasionally by echocardiography, so is not available real-time. This paper develops a method for identifying the driver function based on correlates with geometrical features in the aortic pressure waveform. The method is included in an overall cardiovascular modelling approach, and is clinically validated on a porcine model of pulmonary embolism. For validation a comparison is done between the optimized parameters for a baseline model, which uses the direct measurements of the left ventricle pressure and volume, and the optimized parameters from the approximated driver function. The parameters do not significantly change between the two approaches thus showing that the patient specific approach to identifying the driver function is valid, and has potential clinically