Modelling dynamic changes in blood flow and volume in the cerebral vasculature.

The cerebral microvasculature plays a key role in the transport of blood and the delivery of nutrients to the cells that perform brain function. Although recent advances in experimental imaging techniques mean that its structure and function can be interrogated to very small length scales, allowing individual vessels to be mapped to a fraction of 1 µm, these techniques currently remain confined to animal models. In-vivo human data can only be obtained at a much coarser length scale, of order 1 mm, meaning that mathematical models of the microvasculature play a key role in interpreting flow and metabolism data. However, there are close to 10,000 vessels even within a single voxel of size 1 mm3. Given the number of vessels present within a typical voxel and the complexity of the governing equations for flow and volume changes, it is computationally challenging to solve these in full, particularly when considering dynamic changes, such as those found in response to neural activation. We thus consider here the governing equations and some of the simplifications that have been proposed in order more rigorously to justify in what generations of blood vessels these approximations are valid. We show that two approximations (neglecting the advection term and assuming a quasi-steady state solution for blood volume) can be applied throughout the cerebral vasculature and that two further approximations (a simple first order differential relationship between inlet and outlet flows and inlet and outlet pressures, and matching of static pressure at nodes) can be applied in vessels smaller than approximately 1 mm in diameter. We then show how these results can be applied in solving flow fields within cerebral vascular networks providing a simplified yet rigorous approach to solving dynamic flow fields and compare the results to those obtained with alternative approaches. We thus provide a framework to model cerebral blood flow and volume within the cerebral vasculature that can be used, particularly at sub human imaging length scales, to provide greater insight into the behaviour of blood flow and volume in the cerebral vasculature.

Comparing different analysis methods for quantifying the MRI amide proton transfer (APT) effect in hyperacute stroke patients.

Amide proton transfer (APT) imaging is a pH mapping method based on the chemical exchange saturation transfer phenomenon that has potential for penumbra identification following stroke. The majority of the literature thus far has focused on generating pH-weighted contrast using magnetization transfer ratio asymmetry analysis instead of quantitative pH mapping. In this study, the widely used asymmetry analysis and a model-based analysis were both assessed on APT data collected from healthy subjects (n = 2) and hyperacute stroke patients (n = 6, median imaging time after onset = 2 hours 59 minutes). It was found that the model-based approach was able to quantify the APT effect with the lowest variation in grey and white matter (≤ 13.8 %) and the smallest average contrast between these two tissue types (3.48 %) in the healthy volunteers. The model-based approach also performed quantitatively better than the other measures in the hyperacute stroke patient APT data, where the quantified APT effect in the infarct core was consistently lower than in the contralateral normal appearing tissue for all the patients recruited, with the group average of the quantified APT effect being 1.5 ± 0.3 % (infarct core) and 1.9 ± 0.4 % (contralateral). Based on the fitted parameters from the model-based analysis and a previously published pH and amide proton exchange rate relationship, quantitative pH maps for hyperacute stroke patients were generated, for the first time, using APT imaging.

Phase-rectified signal averaging for intrapartum electronic fetal heart rate monitoring is related to acidaemia at birth.

OBJECTIVE: Recent studies suggest that phase-rectified signal averaging (PRSA), measured in antepartum fetal heart rate (FHR) traces, may sensitively indicate fetal status; however, its value has not been assessed during labour. We determined whether PRSA relates to acidaemia in labour, and compare its performance to short-term variation (STV), a related computerised FHR feature. DESIGN: Historical cohort. SETTING: Large UK teaching hospital. POPULATION: All 7568 Oxford deliveries that met the study criteria from April 1993 to February 2008. METHODS: We analysed the last 30 minutes of the FHR and associated outcomes of infants. We used computerised analysis to calculate PRSA decelerative capacity (DC(PRSA)), and its ability to predict umbilical arterial blood pH ≤ 7.05 using receiver operator characteristic (ROC) curves and event rate estimates (EveREst). We compared DC(PRSA) with STV calculated on the same traces. MAIN OUTCOME MEASURE: Umbilical arterial blood pH ≤ 7.05. RESULTS: We found that PRSA could be measured in all cases. DC(PRSA) predicted acidaemia significantly better than STV: the area under the ROC curve was 0.665 (95% CI 0.632-0.699) for DC(PRSA), and 0.606 (0.573-0.639) for STV (P = 0.007). EveREst plots showed that in the worst fifth centile of cases, the incidence of low pH was 17.75% for DC(PRSA) but 11.00% for STV (P < 0.001). DC(PRSA) was not highly correlated with STV. CONCLUSIONS: DC(PRSA) of the FHR can be measured in labour, and appears to predict acidaemia more accurately than STV. Further prospective evaluation is warranted to assess whether this could be clinically useful. The weak correlation between DC(PRSA) and STV suggests that they could be combined in multivariate FHR analyses.

Genetic testing in inherited polyposis syndromes - how and why?

There have been recent advances in genetic testing enabling accurate diagnosis of polyposis syndromes by identifying causative gene mutations, which is essential in the management of individuals with polyposis syndrome and predictive genetic testing of their extended families. There are some similarities in clinical presentation of various polyposis syndromes, which may pose a challenge to diagnosis. In this review, we discuss the clinical presentation of the main polyposis syndromes and the process of genetic testing, including the latest advancement and future of genetic testing. We aim to reiterate the importance of genetic testing in the management of polyposis syndromes, potential pitfalls associated with genetic testing and recommendations for healthcare professionals involved with the care of polyposis patients.

Optimal sampling schedule for chemical exchange saturation transfer.

The sampling schedule for chemical exchange saturation transfer imaging is normally uniformly distributed across the saturation frequency offsets. When this kind of evenly distributed sampling schedule is used to quantify the chemical exchange saturation transfer effect using model-based analysis, some of the collected data are minimally informative to the parameters of interest. For example, changes in labile proton exchange rate and concentration mainly affect the magnetization near the resonance frequency of the labile pool. In this study, an optimal sampling schedule was designed for a more accurate quantification of amine proton exchange rate and concentration, and water center frequency shift based on an algorithm previously applied to magnetization transfer and arterial spin labeling. The resulting optimal sampling schedule samples repeatedly around the resonance frequency of the amine pool and also near to the water resonance to maximize the information present within the data for quantitative model-based analysis. Simulation and experimental results on tissue-like phantoms showed that greater accuracy and precision (>30% and >46%, respectively, for some cases) were achieved in the parameters of interest when using optimal sampling schedule compared with evenly distributed sampling schedule. Hence, the proposed optimal sampling schedule could replace evenly distributed sampling schedule in chemical exchange saturation transfer imaging to improve the quantification of the chemical exchange saturation transfer effect and parameter estimation.

MRI-based parameter inference for cerebral perfusion modelling in health and ischaemic stroke.

Cerebral perfusion modelling is a promising tool to predict the impact of acute ischaemic stroke treatments on the spatial distribution of cerebral blood flow (CBF) in the human brain. To estimate treatment efficacy based on CBF, perfusion simulations need to become suitable for group-level investigations and thus account for physiological variability between individuals. However, computational perfusion modelling to date has been restricted to a few patient-specific cases. This study set out to establish automated parameter inference for perfusion modelling based on neuroimaging data and thus enable CBF simulations of groups. Magnetic resonance imaging (MRI) data from 75 healthy senior adults were utilised. Brain geometries were computed from healthy reference subjects' T1-weighted MRI. Haemodynamic model parameters were determined from spatial CBF maps measured by arterial spin labelling (ASL) perfusion MRI. Thereafter, perfusion simulations were conducted in 75 healthy cases followed by 150 acute ischaemic stroke cases representing an occlusion and CBF cessation in the left and right middle cerebral arteries. The anatomical fitness of the brain geometries was evaluated by comparing the simulated grey (GM) and white matter (WM) volumes to measurements in healthy reference subjects. Strong positive correlations were found in both tissue types (GM: Pearson's r 0.74, P<0.001; WM: Pearson's r 0.84, P<0.001). Haemodynamic parameter tuning was verified by comparing the total volumetric blood flow rate to the brain in healthy reference subjects and simulations (Pearson's r 0.89, P<0.001). In acute ischaemic stroke cases, the simulated infarct volume using a perfusion-based estimate was 197±25 ml. Computational predictions were in agreement with anatomical and haemodynamic values from the literature concerning T1-weighted, T2-weighted, and phase-contrast MRI measurements in healthy scenarios and acute ischaemic stroke cases. The acute stroke simulations did not capture small infarcts (left tail of the distribution), which could be explained by neglected compensatory mechanisms, e.g. collaterals. The proposed parameter inference method provides a foundation for group-level CBF simulations and for in silico clinical stroke trials which could assist in medical device and drug development.

Vasomotion does inhibit mass exchange between axisymmetric blood vessels and tissue.

Vasomotion, the name given to the physiological phenomenon whereby blood vessel walls exhibit rhythmic oscillations in diameter, is a complex process and very poorly understood. It has been proposed as a mechanism for protecting tissue when perfusion levels are reduced, since it has experimentally been shown to occur more frequently under such conditions. However, no quantitative evidence yet exists for whether the oscillation of the wall actually has any effect on mass transport to tissue. In our previous work, it was shown that the presence of non-linearities in the governing equation could result in a significant change in time-averaged mass transport to tissue: however, it was not possible, due to the limitations of the model, to determine whether time-averaged mass transport increased or decreased. This model is extended in this paper through coupling of the one-dimensional axisymmetric mass transport equations in tissue and blood to quantify the effects of vasomotion on mass transport to tissue. The results show that over a wide parameter range, surrounding those values calculated from experimental data, vasomotion does inhibit mass transport to tissue in a one-dimensional axisymmetric blood vessel by an amount that is predominantly dependent upon the amplitude of oscillation and that increases rapidly at larger oscillation amplitudes.

Simulation-based optimisation to quantify heterogeneity of specific ventilation and perfusion in the lung by the Inspired Sinewave Test.

The degree of specific ventilatory heterogeneity (spatial unevenness of ventilation) of the lung is a useful marker of early structural lung changes which has the potential to detect early-onset disease. The Inspired Sinewave Test (IST) is an established noninvasive 'gas-distribution' type of respiratory test capable of measuring the cardiopulmonary parameters. We developed a simulation-based optimisation for the IST, with a simulation of a realistic heterogeneous lung, namely a lognormal distribution of spatial ventilation and perfusion. We tested this method in datasets from 13 anaesthetised pigs (pre and post-lung injury) and 104 human subjects (32 healthy and 72 COPD subjects). The 72 COPD subjects were classified into four COPD phenotypes based on 'GOLD' classification. This method allowed IST to identify and quantify heterogeneity of both ventilation and perfusion, permitting diagnostic distinction between health and disease states. In healthy volunteers, we show a linear relationship between the ventilatory heterogeneity versus age ([Formula: see text]). In a mechanically ventilated pig, IST ventilatory heterogeneity in noninjured and injured lungs was significantly different (p < 0.0001). Additionally, measured indices could accurately identify patients with COPD (area under the receiver operating characteristic curve is 0.76, p < 0.0001). The IST also could distinguish different phenotypes of COPD with 73% agreement with spirometry.

On the Sensitivity Analysis of Porous Finite Element Models for Cerebral Perfusion Estimation.

Computational physiological models are promising tools to enhance the design of clinical trials and to assist in decision making. Organ-scale haemodynamic models are gaining popularity to evaluate perfusion in a virtual environment both in healthy and diseased patients. Recently, the principles of verification, validation, and uncertainty quantification of such physiological models have been laid down to ensure safe applications of engineering software in the medical device industry. The present study sets out to establish guidelines for the usage of a three-dimensional steady state porous cerebral perfusion model of the human brain following principles detailed in the verification and validation (V&V 40) standard of the American Society of Mechanical Engineers. The model relies on the finite element method and has been developed specifically to estimate how brain perfusion is altered in ischaemic stroke patients before, during, and after treatments. Simulations are compared with exact analytical solutions and a thorough sensitivity analysis is presented covering every numerical and physiological model parameter. The results suggest that such porous models can approximate blood pressure and perfusion distributions reliably even on a coarse grid with first order elements. On the other hand, higher order elements are essential to mitigate errors in volumetric blood flow rate estimation through cortical surface regions. Matching the volumetric flow rate corresponding to major cerebral arteries is identified as a validation milestone. It is found that inlet velocity boundary conditions are hard to obtain and that constant pressure inlet boundary conditions are feasible alternatives. A one-dimensional model is presented which can serve as a computationally inexpensive replacement of the three-dimensional brain model to ease parameter optimisation, sensitivity analyses and uncertainty quantification. The findings of the present study can be generalised to organ-scale porous perfusion models. The results increase the applicability of computational tools regarding treatment development for stroke and other cerebrovascular conditions.

A porous circulation model of the human brain for in silico clinical trials in ischaemic stroke.

The advancement of ischaemic stroke treatment relies on resource-intensive experiments and clinical trials. In order to improve ischaemic stroke treatments, such as thrombolysis and thrombectomy, we target the development of computational tools for in silico trials which can partially replace these animal and human experiments with fast simulations. This study proposes a model that will serve as part of a predictive unit within an in silico clinical trial estimating patient outcome as a function of treatment. In particular, the present work aims at the development and evaluation of an organ-scale microcirculation model of the human brain for perfusion prediction. The model relies on a three-compartment porous continuum approach. Firstly, a fast and robust method is established to compute the anisotropic permeability tensors representing arterioles and venules. Secondly, vessel encoded arterial spin labelling magnetic resonance imaging and clustering are employed to create an anatomically accurate mapping between the microcirculation and large arteries by identifying superficial perfusion territories. Thirdly, the parameter space of the problem is reduced by analysing the governing equations and experimental data. Fourthly, a parameter optimization is conducted. Finally, simulations are performed with the tuned model to obtain perfusion maps corresponding to an open and an occluded (ischaemic stroke) scenario. The perfusion map in the occluded vessel scenario shows promising qualitative agreement with computed tomography images of a patient with ischaemic stroke caused by large vessel occlusion. The results highlight that in the case of vessel occlusion (i) identifying perfusion territories is essential to capture the location and extent of underperfused regions and (ii) anisotropic permeability tensors are required to give quantitatively realistic estimation of perfusion change. In the future, the model will be thoroughly validated against experiments.

Mathematical modeling of thermal ablation.

Ablation techniques have become a widespread choice for the treatment of cancerous tumors for which surgical resection techniques have a poor prognosis. The minimally invasive nature and high success rate when performed by experienced clinicians mean that ablation is likely to remain a core technique. However, the success rate can drop off dramatically when less-experienced operators are involved, and it is particularly difficult to kill all of the tumor and only the tumor, given the dynamic nature of the processes that lead to cell death. Mathematical modeling of the response to ablation treatment has a long history. Since the seminal paper of Pennes in 1948, there have been numerous attempts to propose models that are both physiologically accurate and computationally inexpensive. All of these models are based on different principles and assumptions, which may make them suitable only for particular applications. This makes choosing a model very difficult because of the lack of understanding about what the limitations of different assumptions are likely to be and how this influences the necessary computational resources. Here we review the models available in the literature, illustrating how the different assumptions impact upon both their accuracy and computational expense.The primary intentions are to provide a critical scientific review and a practical guide for researchers wishing to use such models in clinical applications.

Hereditary mixed polyposis syndrome due to a BMPR1A mutation.

The conditions Juvenile Polyposis Syndrome (JPS) and Hereditary Mixed Polyposis Syndrome (HMPS) are associated with an increased risk of colorectal carcinoma. The genetic mechanisms which explain these conditions have until recently been poorly understood. Recent interest has focused on the transforming growth factor (TGF)-beta signalling pathway and, in particular, on mutations in the SMAD4 gene. However, not all cases of JPS and HMPS have mutations in SMAD4 and focus has now shifted to other components of the TGF-beta pathway to clarify the genetic mechanisms involved in these conditions. In this report, we describe the significance of a bone morphogenetic protein receptor type 1A gene mutation in an Irish family.

Predictive markers in breast cancer--the present.

Breast cancer is a heterogeneous disease and there is a continual drive to identify markers that will aid in predicting prognosis and response to therapy. To date, relatively few markers have established prognostic power. Oestrogen receptor (ER) is probably the most powerful predictive marker in breast cancer management, both in determining prognosis and in predicting response to hormone therapies. Progesterone receptor (PR) is also a widely used marker, although its value is less well established. HER-2 status has also become a routine prognostic and predictive factor in breast cancer. Given the importance of these biological markers in patient management, it is essential that assays are robust and quality controlled, and that interpretation is standardized. Furthermore, it is important to be aware of the limitations in their predictive power, and how this may be refined through addition of further biological markers. The aim of this review is to provide an overview of the established role of ER, PR and HER-2 in patient management, the current standards for assessing these markers, as well as highlighting the controversies that still surround their use and methods of assessment.

Multiple mitochondrial DNA deletions in monozygotic twins with OPMD.

BACKGROUND: Oculopharyngeal muscular dystrophy (OPMD) is caused by expansions of the poly (A) binding protein 2 (PABP2) gene. Previous histological analyses have revealed mitochondrial abnormalities in the muscles of OPMD patients but their significance remains uncertain. OBJECTIVE: We had the rare opportunity to study monozygotic twins with identical expansions of the PABP2 gene but with markedly different severities of OPMD. Both had histological features of mitochondrial myopathy. We determined whether mitochondrial DNA abnormalities underlay these changes. METHODS: Clinical information was obtained by history and examination. Muscle biopsies were obtained from each subject and genetic analysis was performed using long-range PCR and Southern blotting. RESULTS: We demonstrate, for the first time, the presence of mitochondrial DNA (mtDNA) deletions by Southern blotting in individuals with OPMD. This correlates with the presence of mitochondrial myopathy in both twins. Moreover, both twins had different mtDNA deletions, which might explain their phenotypic differences. CONCLUSION: We hypothesise that mitochondrial dysfunction may occur as a consequence of PABP2 gene mutations, and that this dysfunction may affect the phenotypic manifestations of OPMD.

Biallelic mutation of MSH2 in primary human cells is associated with sensitivity to irradiation and altered RAD51 foci kinetics.

BACKGROUND: Reports of differential mutagen sensitivity conferred by a defect in the mismatch repair (MMR) pathway are inconsistent in their conclusions. Previous studies have investigated cells established from immortalised human colorectal tumour lines or cells from animal models. METHODS: We examined primary human MSH2-deficient neonatal cells, bearing a biallelic truncating mutation in MSH2, for viability and chromosomal damage after exposure to DNA-damaging agents. RESULTS: MSH2-deficient cells exhibit no response to interstrand DNA cross-linking agents but do show reduced viability in response to irradiation. They also show increased chromosome damage and exhibit altered RAD51 foci kinetics after irradiation exposure, indicating defective homologous recombinational repair. DISCUSSION: The cellular features and sensitivity of MSH2-deficient primary human cells are broadly in agreement with observations of primary murine cells lacking the same gene. The data therefore support the view that the murine model recapitulates early features of MMR deficiency in humans, and implies that the variable data reported for MMR-deficient immortalised human cells may be due to further genetic or epigenetic lesions. We suggest caution in the use of radiotherapy for treatment of malignancies in individuals with functional loss of MSH2.

Sequential UV- and chlorine-based disinfection to mitigate Escherichia coli in drinking water biofilms.

This study was designed to examine the potential downstream benefits of sequential disinfection to control the persistence of Escherichia coli under conditions relevant to drinking water distribution systems. Eight annular reactors (four polycarbonate and four cast iron) were setup in parallel to address various factors that could influence biofilm growth in distribution systems. Eight reactors were treated with chlorine, chlorine dioxide and monochloramine alone or in combination with UV to examine the effects on Escherichia coli growth and persistence in both the effluent and biofilm. In general, UV-treated systems in combination with chlorine or chlorine dioxide and monochloramine achieved greater log reductions in both effluent and biofilm than systems treated with chlorine-based disinfectants alone. However, during UV-low chlorine disinfection, E. coli was found to persist at low levels, suggesting that the UV treatment had instigated an adaptive mutation. During UV-chlorine-dioxide treatment, the E. coli that was initially below the detection limit reappeared during a low level of disinfection (0.2 mg/L) in the cast iron systems. Chloramine was shown to be effective in disinfecting suspended E. coli in the effluent but was unable to reduce biofilm counts to below the detection limit. Issues such as repair mechanism of E. coli and nitrification could help explain some of these aberrations. Improved understanding of the ability of chlorine-based disinfectant in combination with UV to provide sufficient disinfection will ultimately effect in improved management and safety of drinking water.

Identifying the myogenic and metabolic components of cerebral autoregulation.

Cerebral autoregulation is the term used to describe a number of mechanisms that act together to maintain a near constant cerebral blood flow in response to changes in arterial blood pressure. These mechanisms are complex and known to be affected in a range of cerebrovascular diseases. However, it can be difficult to assign an alteration in cerebral autoregulation to one of the underlying physiological mechanisms without the use of a complex mathematical model. In this paper, we thus set out a new approach that enables these mechanisms to be related to the autoregulation behaviour and hence inferred from experimental measurements. We show that the arteriolar response is a function of just three parameters, which we term the elastic, the myogenic and the metabolic sensitivity coefficients, and that the full vascular response is dependent upon only seven parameters. The ratio of the strengths of the myogenic and the metabolic responses is found to be in the range 2.5 to 5 over a wide range of pressure, indicating that the balance between the two appears to lie within this range. We validate the model with existing experimental data both at the level of an individual vessel and across the whole vasculature, and show that the results are consistent with findings from the literature. We then conduct a sensitivity analysis of the model to demonstrate which parameters are most important in determining the strength of static autoregulation, showing that autoregulation strength is predominantly set by the arteriolar sensitivity coefficients. This new approach could be used in future studies to help to interpret the components of the autoregulation response and how they are affected under different conditions, providing a greater insight into the fundamental processes that govern autoregulation.

Synchronization between arterial blood pressure and cerebral oxyhaemoglobin concentration investigated by wavelet cross-correlation.

Wavelet cross-correlation (WCC) is used to analyse the relationship between low-frequency oscillations in near-infrared spectroscopy (NIRS) measured cerebral oxyhaemoglobin (O(2)Hb) and mean arterial blood pressure (MAP) in patients suffering from autonomic failure and age-matched controls. Statistically significant differences are found in the wavelet scale of maximum cross-correlation upon posture change in patients, but not in controls. We propose that WCC analysis of the relationship between O(2)Hb and MAP provides a useful method of investigating the dynamics of cerebral autoregulation using the spontaneous low-frequency oscillations that are typically observed in both variables without having to make the assumption of stationarity of the time series. It is suggested that for a short-duration clinical test previous transfer-function-based approaches to analyse this relationship may suffer due to the inherent nonstationarity of low-frequency oscillations that are observed in the resting brain.

A model for generating synthetic arterial blood pressure.

A new model capable of simulating many important aspects of human arterial blood pressure (ABP) is proposed. Both data-driven approach and physiological principles have been applied to describe the time series of diastolic, systolic, dicrotic notch and dicrotic peak pressure points. Major static and dynamic features of the model can be prescribed by the user, including heart rate, mean systolic and diastolic pressure, and the corresponding physiological control quantities, such as baroreflex sensitivity coefficient and Windkessel time constant. A realistic ABP generator can be used to compile a virtual database of signals reflecting individuals with different clinical conditions and signals containing common artefacts. The ABP model permits to create a platform to assess a wide range of biomedical signal processing approaches and be used in conjunction with, e.g. Kalman filters to improve the quality of ABP signals.