A graphical method for practical and informative identifiability analyses of physiological models: A case study of insulin kinetics and sensitivity

peer reviewedBackground: Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error. Methods: The method measures the distinction of the integral formulations of the parameter coefficients at the proposed sampling times. It can thus predict the susceptibility of the parameters to the effects of measurement error. The method is tested in-silico with Monte Carlo analyses of a number of insulin sensitivity test applications. Results: The method successfully captured the analogous nature of identifiability observed in Monte Carlo analyses of a number of cases including protocol alterations, parameter changes and differences in participant behaviour. However, due to the numerical nature of the analyses, prediction was not perfect in all cases. Conclusions: Thus although the current method has valuable and significant capabilities in terms of study or test protocol design, additional developments would further strengthen the predictive capability of the method. Finally, the method captures the experimental reality that sampling error and timing can negate assumed parameter identifiability and that identifiability is a continuous rather than discrete phenomenon

Parameter Identification Methods in a Model of the Cardiovascular System

To be clinically relevant, mathematical models have to be patient-specific, meaning that their parameters have to be identified from patient data. To achieve real time monitoring, it is important to select the best parameter identification method, in terms of speed, efficiency and reliability. This work presents a comparison of seven parameter identification methods applied to a lumped-parameter cardiovascular system model. The seven methods are tested using in silico and experimental reference data. To do so, precise formulae for initial parameter values first had to be developed. The test results indicate that the trust-region reflective method seems to be the best method for the present model. This method (and the proportional method) are able to perform parameter identification in two to three minutes, and will thus benefit cardiac and vascular monitoring applications

Immunochip analysis identifies multiple susceptibility loci for systemic sclerosis

In this study, 1,833 systemic sclerosis (SSc) cases and 3,466 controls were genotyped with the Immunochip array. Classical alleles, amino acid residues, and SNPs across the human leukocyte antigen (HLA) region were imputed and tested. These analyses resulted in a model composed of six polymorphic amino acid positions and seven SNPs that explained the observed significant associations in the region. In addition, a replication step comprising 4,017 SSc cases and 5,935 controls was carried out for several selected non-HLA variants, reaching a total of 5,850 cases and 9,401 controls of European ancestry. Following this strategy, we identified and validated three SSc risk loci, including DNASE1L3 at 3p14, the SCHIP1-IL12A locus at 3q25, and ATG5 at 6q21, as well as a suggested association of the TREH-DDX6 locus at 11q23. The associations of several previously reported SSc risk loci were validated and further refined, and the observed peak of association in PXK was related to DNASE1L3. Our study has increased the number of known genetic associations with SSc, provided further insight into the pleiotropic effects of shared autoimmune risk factors, and highlighted the power of dense mapping for detecting previously overlooked susceptibility loci

A Novel Experimental Approach for the Measurement of Vibration-Induced Changes in the Rheological Properties of Ex Vivo Ovine Brain Tissue

Increased incidence of traumatic brain injury (TBI) imposes a growing need to understand the pathology of brain trauma. A correlation between the incidence of multiple brain traumas and rates of behavioural and cognitive deficiencies has been identified amongst people that experienced multiple TBI events. Mechanically, repetitive TBIs may affect brain tissue in a similar way to cyclic loading. Hence, the potential susceptibility of brain tissue to mechanical fatigue is of interest. Although temporal changes in ovine brain tissue viscoelasticity and biological fatigue of other tissues such as tendons and arteries have been investigated, no methodology currently exists to cyclically load ex vivo brain tissue. A novel rheology-based approach found a consistent, initial stiffening response of the brain tissue before a notable softening when subjected to a subsequential cyclic rotational shear. History dependence of the mechanical properties of brain tissue indicates susceptibility to mechanical fatigue. Results from this investigation increase understanding of the fatigue properties of brain tissue and could be used to strengthen therapy and prevention of TBI, or computational models of repetitive head injuries

Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation

Compliant phantoms of the human aortic arch can mimic patient specific cardiovascular dysfunctions in vitro. Hence, phantoms may enable elucidation of haemodynamic disturbances caused by aortic dysfunction. This paper describes the fabrication of a thin-walled silicone phantom of the human ascending aorta and brachiocephalic artery. The model geometry was determined via a meta-analysis and modelled in SolidWorks before 3D printing. The solid model surface was smoothed and scanned with a 3D scanner. An offset outer mould was milled from Ebalta S-Model board. The final phantom indicated that ABS was a suitable material for the internal model, the Ebalta S-Model board yielded a rough external surface. Co-location of the moulds during silicone pour was insufficient to enable consistent wall thickness. The resulting phantom was free of air bubbles but did not have the desired wall thickness consistency