Journal of clinical monitoring and computing
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J Clin Monit Comput · Aug 2013
Analysis of a mathematical model of apoptosis: individual differences and malfunction in programmed cell death.
Apoptosis is an important area of research because of its role in keeping a mature multicellular organism's number of cells constant, hence, ensuring that the organism does not have cell accumulation that may transform into cancer with additional hallmarks. Firstly, we have carried out sensitivity analysis on an existing mathematical mitochondria-dependent apoptosis model to find out which parameters have a role in causing monostable cell survival, which may, in turn, lead to malfunction in apoptosis. We have then generated three base parameter sets that represent healthy cells. ⋯ For this treatment, the amount of proteasome inhibitor needed for caspase-3 activation may be different for hypothetical cells with a different pro- or anti-apoptotic protein defect. It is also found that caspase-3 can be activated by Bcl-2 inhibitor treatment only in those hypothetical malfunctioning cells with Bax deficiency but not in others. These results are in line with the view that molecular heterogeneity in individuals may be an important factor in determining the individuals' positive or negative responses to treatments.
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J Clin Monit Comput · Aug 2013
ReviewPotential of surface acoustic wave biosensors for early sepsis diagnosis.
Early diagnosis of sepsis is a difficult problem for intensivists and new biomarkers for early diagnosis have been difficult to come by. Here we discuss the potential of adapting a technology from the electronics industry, surface acoustic wave (SAW) sensors, for diagnosis of multiple markers of sepsis in real time, using non-invasive assays of exhaled breath condensate. The principles and advantages of the SAW technology are reviewed as well as a proposed plan for adapting this flexible technology to early sepsis detection.
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J Clin Monit Comput · Aug 2013
Comparative StudyBaroreflex sensitivity variations in response to propofol anesthesia: comparison between normotensive and hypertensive patients.
The aim of this paper is to compare baroreflex sensitivity (BRS) following anesthesia induction via propofol to pre-induction baseline values through a systematic and mathematically robust analysis. Several mathematical methods for BRS quantification were applied to pre-operative and intra-operative data collected from patients undergoing major surgery, in order to track the trend in BRS variations following anesthesia induction, as well as following the onset of mechanical ventilation. Finally, a comparison of BRS trends in chronic hypertensive patients (CH) with respect to non hypertensive (NH) patients was performed. 10 NH and 7 CH patients undergoing major surgery with American Society of Anesthesiologists classification score 2.5 ± 0.5 and 2.6 ± 0.5 respectively, were enrolled in the study. ⋯ A larger decrease in ABP was observed in CH patients when compared to NH patients, whereas HR remained unaltered and BRS was found to be lower than in the NH group at baseline, with no significant changes in the following epochs when compared to baseline. To our knowledge, this is the first study in which the autonomic response to propofol induction in CH and NH patients was compared. The analysis of BRS through a mathematically rigorous procedure in the perioperative period could result in the availability of additional information to guide therapy and anesthesia in uncontrolled hypertensive patients, which are prone to a higher rate of hypotension events occurring during general anesthesia induction.
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Extensive use of high frequency imaging in medical applications permit the estimation of velocity fields which corresponds to motion of landmarks in the imaging field. The focus of this work is on the development of a robust local optical flow algorithm for velocity field estimation in medical applications. Local polynomial fits to the medical image intensity-maps are used to generate convolution operators to estimate the spatial gradients. ⋯ Tikhonov regularization is exploited to synthesize a well posed optimization problem and to penalize large displacements. The proposed algorithm is tested and validated on benchmark datasets for deformable image registration. The ten datasets include large and small deformations, and illustrate that the proposed algorithm outperforms or is competitive with other algorithms tested on this dataset, when using mean and variance of the displacement error as performance metrics.