Investigating cerebral oedema using poroelasticity

Cerebral oedema can be classified as the tangible swelling produced by expansion of the interstitial fluid volume. Hydrocephalus can be succinctly described as the abnormal accumulation of cerebrospinal fluid (CSF) within the brain which ultimately leads to oedema within specific sites of parenchymal tissue. Using hydrocephalus as a test bed, one is able to account for the necessary mechanisms involved in the interaction between oedema formation and cerebral fluid production, transport and drainage. The current state of knowledge about integrative cerebral dynamics and transport phenomena indicates that poroelastic theory may provide a suitable framework to better understand various diseases. In this work, Multiple-Network Poroelastic Theory (MPET) is used to develop a novel spatio-temporal model of fluid regulation and tissue displacement within the various scales of the cerebral environment. The model is applied through two formats, a one-dimensional finite difference – Computational Fluid Dynamics (CFD) coupling framework, as well as a two-dimensional Finite Element Method (FEM) formulation. These are used to investigate the role of endoscopic fourth ventriculostomy in alleviating oedema formation due to fourth ventricle outlet obstruction (1D coupled model) in addition to observing the capability of the FEM template in capturing important characteristics allied to oedema formation, like for instance in the periventricular region (2D model)

Poroelastic Modelling of CSF circulation via the incorporation of experimentally derived microscale water transport properties

We outline how multicompartmental poroelasticity is applied to the study of dementia. We utilize a 3D version of our poroelastic code to investigate the effects within parenchymal tissue. This system is coupled with multiple pipelines within the VPH-DARE@IT project which account for patient/subject-specific boundary conditions in the arterial compartment, in addition to both an image segmentation-mesh and integrated cardiovascular system model pipeline respectively. This consolidated template allows for the extraction of boundary conditions to run CFD simulations for the ventricles. Finally, we outline some experimental results that will help inform the MPET system

Investigating Dementia via a multicompartmental poroelastic model of parenchymal tissue

In this paper, a workflow within the VPH-DARE@IT Clinical Research Platform is presented. This is used to model the biomechanical behaviour of perfused brain tissue. This workflow features a 3D multicompartmental poroelastic framework, patient-specific brain anatomy representations and continuous waveforms of internal carotid and vertebral arteries, which are used as a means of personalizing the boundary conditions that feed the arterial compartment of the in-house poroelastic solver. Results are shown comparing CSF/ISF clearance and accumulation in two males of similar age, both are non-smokers, however one is more active and is diagnosed with MCI and experiences less sleep

Healthcare costs associated with progressive diabetic retinopathy among National Health Insurance enrollees in Taiwan, 2000-2004

<p>Abstract</p> <p>Background</p> <p>Diabetic retinopathy is one of the most common microvascular complications of diabetes and one of the major causes of adult visual impairment in national surveys in Taiwan. This study aimed to identify the healthcare costs of Taiwan's National Health Insurance program on behalf of diabetic patients with stable or progressive retinopathy.</p> <p>Methods</p> <p>A retrospective cohort study was conducted with 4,988 medication-using diabetic retinopathy subjects ≥ 40 years of age under National Health Insurance Program coverage between 2000 and 2004. Study cohort subjects were recorded as having diabetic retinopathy according to ICD-9-CM codes. States of diabetic retinopathy were strategically divided into stable and progressive categories according to subjects' conditions at follow-up in 2004. Expenditures were calculated and compared for the years 2000 and 2004.</p> <p>Results</p> <p>During the 4-year follow-up (2000 through 2004), 4,116 subjects (82.5%) of 4,988 diabetic subjects were in the stable category, and 872 (17.5%) were in the progressive category. Average costs of those in the normal category increased by US $48 from US$1921 in 2000 to US $1969 in 2004 (p = 0.594), whereas costs for those progressing from normal to non-proliferative diabetic retinopathy (NPDR) or proliferative diabetic retinopathy (PDR) increased by US$1760, from US $1566 in 2000 to US$3326 in 2004 (p < 0.001). The PDR category had the highest average costs at US $3632 in 2000. The NPDR-to-PDR category experienced the greatest increase in costs at US$3482, from US $2723 in 2000 to US$6204 in 2004 (p = 0.042), and the greatest percentage of increase at 2.3% (2.2% when adjusted by comparing to normal category).</p> <p>Conclusions</p> <p>This large-scale longitudinal study provides evidence that increased healthcare costs are associated with progressive diabetic retinopathy among diabetic NHI enrollees in Taiwan.</p

Estimation of parameters in a structured SIR model

[EN] In this paper, an age-structured epidemiological process is considered. The disease model is based on a SIR model with unknown parameters. We addressed two important issues to analyzing the model and its parameters. One issue is concerned with the theoretical existence of unique solution, the identifiability problem. The second issue is how to estimate the parameters in the model. We propose an iterative algorithm to study the identifiability of the system and a method to estimate the parameters which are identifiable. A least squares approach based on a finite set of observations helps us to estimate the initial values of the parameters. Finally, we test the proposed algorithms.The authors would like to thank the referees and the editor for their comments and useful suggestions for improvement of the manuscript. This work has been partially supported by Spanish Grant MTM2013-43678-P.Cantó Colomina, B.; Coll, C.; Sánchez, E. (2017). Estimation of parameters in a structured SIR model. Advances in Difference Equations. 33:1-13. https://doi.org/10.1186/s13662-017-1078-5S11333Strogatz, S, Friedman, M, Mallinck-Rodt, AJ, McKay, S: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Perseus Books, Washington (1994)De La Sen, M, Quesada, A: Some equilibrium, stability, instability and oscillatory results for an extended discrete epidemic model with evolution memory. Adv. Differ. Equ. 2013, 234 (2013)Han, Q, Wang, Z: On extinction of infectious diseases for multi-group SIRS models with satured incidence rate. Adv. Differ. Equ. 2015, 333 (2015)Cantó, B, Coll, C, Sánchez, E: Structural identifiability of a model of dialysis. Math. Comput. Model. 50, 733-737 (2009)Cantó, B, Coll, C, Sánchez, E: Identifiability of a class of discretized linear partial differential algebraic equations. Math. Probl. Eng., 1-12 (2011)Craciun, G, Pantea, C: Identifiability of chemical reaction networks. J. Math. Chem. 44, 244-259 (2008)Malik, MB, Salman, M: State-space least mean square. Digit. Signal Process. 18, 334-345 (2008)Ding, F, Liu, PX, Liu, G: Multiinnovatiovation least-squares identification for system modeling. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 18(3), 767-778 (2010)Ben-Zvi, A, McLellan, PJ, McAuley, KB: Identifiability of linear time-invariant differential-algebraic systems, I. The generalized Markov parameter approach. Ind. Eng. Chem. Res. 42, 6607-6618 (2003)Boyadjiev, C, Dimitrova, E: An iterative method for model parameter identification. Comput. Chem. Eng. 29, 941-948 (2005)Ben-Zvi, A, McLellan, PJ, McAuley, KB: Identifiability of linear time-invariant differential-algebraic systems, 2. The differential-algebraic approach. Ind. Eng. Chem. Res. 43, 1251-1259 (2004)Dion, JM, Commault, C, van der Woude, J: Generic properties and control of linear structured systems: a survey. Automatica 39, 1125-1144 (2003)Chou, IC, Voit, EO: Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 219, 57-83 (2009)Schmitz, OJ: Ecology and Ecosystems Conservation. Island Press, Washington (2013

Disparities in appendicitis rupture rate among mentally ill patients

<p>Abstract</p> <p>Background</p> <p>Many studies have been carried out that focus on mental patients' access to care for their mental illness, but very few pay attention on these same patients' access to care for their physical diseases. Acute appendicitis is a common surgical emergency. Our population-based study was to test for any possible association between mental illness and perforated appendicitis. We hypothesized that there are significant disparities in access to timely surgical care between appendicitis patients with and without mental illness, and more specifically, between patients with schizophrenia and those with another major mental illness.</p> <p>Methods</p> <p>Using the National Health Insurance (NHI) hospital-discharge data, we compared the likelihood of perforated appendix among 97,589 adults aged 15 and over who were hospitalized for acute appendicitis in Taiwan between the years 1997 to 2001. Among all the patients admitted for appendicitis, the outcome measure was the odds of appendiceal rupture vs. appendicitis that did not result in a ruptured appendix.</p> <p>Results</p> <p>After adjusting for age, gender, ethnicity, socioeconomic status (SES) and hospital characteristics, the presence of schizophrenia was associated with a 2.83 times higher risk of having a ruptured appendix (odds ratio [OR], 2.83; 95% confidence interval [CI], 2.20–3.64). However, the presence of affective psychoses (OR, 1.15; 95% CI: 0.77–1.73) or other mental disorders (OR, 1.58; 95% CI: 0.89–2.81) was not a significant predictor for a ruptured appendix.</p> <p>Conclusion</p> <p>These findings suggest that given the fact that the NHI program reduces financial barriers to care for mentally ill patients, they are still at a disadvantage for obtaining timely treatment for their physical diseases. Of patients with a major mental illness, schizophrenic patients may be the most vulnerable ones for obtaining timely surgical care.</p

Automated smoother for the numerical decoupling of dynamics models

<p>Abstract</p> <p>Background</p> <p>Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure.</p> <p>Results</p> <p>In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from <it>in-vivo </it>NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations.</p> <p>Conclusion</p> <p>The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.</p