High-throughput design of cultured tissue moulds using a biophysical model: optimising cell alignment.

The technique presented here identifies tethered mould designs, optimised for growing cultured tissue with very highly-aligned cells. It is based on a microscopic biophysical model for polarised cellular hydrogels. There is an unmet need for tools to assist mould and scaffold designs for the growth of cultured tissues with bespoke cell organisations, that can be used in applications such as regenerative medicine, drug screening and cultured meat. High-throughput biophysical calculations were made for a wide variety of computer-generated moulds, with cell-matrix interactions and tissue-scale forces simulated using a contractile network dipole orientation model. Elongated moulds with central broadening and one of the following tethering strategies are found to lead to highly-aligned cells: (1) tethers placed within the bilateral protrusions resulting from an indentation on the short edge, to guide alignment (2) tethers placed within a single vertex to shrink the available space for misalignment. As such, proof-of-concept has been shown for mould and tethered scaffold design based on a recently developed biophysical model. The approach is applicable to a broad range of cell types that align in tissues and is extensible for 3D scaffolds.

Rapid prediction of lab-grown tissue properties using deep learning.

The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. We develop a process for the automated generation of mould designs with and without key symmetries. We create a large training set withN = 6400 cases by running detailed biophysical simulations of cell-matrix interactions using the contractile network dipole orientation model for the self-organisation of cellular hydrogels within these moulds. These are used to train an implementation of thepix2pixdeep learning model, with an additional 100 cases that were unseen in the training of the neural network for review and testing of the trained model. Comparison between the predictions of the machine learning technique and the reserved predictions from the biophysical algorithm show that the machine learning algorithm makes excellent predictions. The machine learning algorithm is significantly faster than the biophysical method, opening the possibility of very high throughput rational design of moulds for pharmaceutical testing, regenerative medicine and fundamental studies of biology. Future extensions for scaffolds and 3D bioprinting will open additional applications.

Three-dimensional simulations of embolic stroke and an equation for sizing emboli from imaging.

Stroke simulations are needed to run in-silico trials, develop hypotheses for clinical studies and to interpret ultrasound monitoring and radiological imaging. We describe proof-of-concept three-dimensional stroke simulations, carrying out in silico trials to relate lesion volume to embolus diameter and calculate probabilistic lesion overlap maps, building on our previous Monte Carlo method. Simulated emboli were released into an in silico vasculature to simulate 1000 s of strokes. Infarct volume distributions and probabilistic lesion overlap maps were determined. Computer-generated lesions were assessed by clinicians and compared with radiological images. The key result of this study is development of a three-dimensional simulation for embolic stroke and its application to an in silico clinical trial. Probabilistic lesion overlap maps showed that the lesions from small emboli are homogeneously distributed throughout the cerebral vasculature. Mid-sized emboli were preferentially found in posterior cerebral artery (PCA) and posterior region of the middle cerebral artery (MCA) territories. For large emboli, MCA, PCA and anterior cerebral artery (ACA) lesions were comparable to clinical observations, with MCA, PCA then ACA territories identified as the most to least probable regions for lesions to occur. A power law relationship between lesion volume and embolus diameter was found. In conclusion, this article showed proof-of-concept for large in silico trials of embolic stroke including 3D information, identifying that embolus diameter could be determined from infarct volume and that embolus size is critically important to the resting place of emboli. We anticipate this work will form the basis of clinical applications including intraoperative monitoring, determining stroke origins, and in silico trials for complex situations such as multiple embolisation.

The role of vascular complexity on optimal junction exponents.

We examine the role of complexity on arterial tree structures, determining globally optimal vessel arrangements using the Simulated AnneaLing Vascular Optimization algorithm, a computational method which we have previously used to reproduce features of cardiac and cerebral vasculatures. In order to progress computational methods for growing arterial networks, deeper understanding of the stability of computational arterial growth algorithms to complexity, variations in physiological parameters (such as metabolic costs for maintaining and pumping blood), and underlying assumptions regarding the value of junction exponents is needed. We determine the globally optimal structure of two-dimensional arterial trees; analysing how physiological parameters affect tree morphology and optimal bifurcation exponent. We find that considering the full complexity of arterial trees is essential for determining the fundamental properties of vasculatures. We conclude that optimisation-based arterial growth algorithms are stable against uncertainties in physiological parameters, while optimal bifurcation exponents (a key parameter for many arterial growth algorithms) are affected by the complexity of vascular networks and the boundary conditions dictated by organs.

Neurological impact of emboli during adult cardiac surgery.

OBJECTIVES: This study draws on advances in Doppler ultrasound bubble sizing to investigate whether high volumes of macro-bubbles entering the brain during cardiac surgery increase the risk of new cerebral microbleeds (CMBs), ischemic MR lesions, or post-operative cognitive decline (POCD). METHODS: Transcranial Doppler (TCD) ultrasound recordings were analysed to estimate numbers of emboli and macrobubbles (>100 µm) entering the brain during cardiac surgery. Logistic regression was used to explore the hypothesis that emboli characteristics affect the incidence of new brain injuries identified through pre- and post-operative MRI and neuropsychological testing. RESULTS: TCD, MRI, and neuropsychological test data were compared between 28 valve and 18 CABG patients. Although valve patients received over twice as many emboli per procedure [median: 1995 vs. 859, p = .004], and seven times as many macro-bubbles [median: 218 vs. 28, p = .001], high volumes of macrobubbles were not found to be significantly associated with new CMBs, new ischaemic lesions, or POCD. The odds of acquiring new CMBs increased by approximately 5% [95% CI: 1 to 10%] for every embolus detected in the first minute after the release of the aortic cross-clamp (AxC). Logistic regression models also confirmed previous findings that cardiopulmonary bypass time and valve surgery were significant predictors for new CMBs (both p = .03). Logistic regression analysis estimated an increase in the odds of acquiring new CMBs of 6% [95% CI: 1 to 12%] for every minute of bypass time over 91 mins. CONCLUSIONS: This small study provides new information about the properties and numbers of bubbles entering the brain during surgery, but found no evidence to substantiate a direct link between large numbers of macrobubbles and adverse cognitive or MR outcome. Clinical Trial Registration URL - http://www.isrctn.com. Unique identifier: 66022965.

Development of a globally optimised model of the cerebral arteries.

The cerebral arteries are difficult to reproduce from first principles, featuring interwoven territories, and intricate layers of grey and white matter with differing metabolic demand. The aim of this study was to identify the ideal configuration of arteries required to sustain an entire brain hemisphere based on minimisation of the energy required to supply the tissue. The 3D distribution of grey and white matter within a healthy human brain was first segmented from magnetic resonance images. A novel simulated annealing algorithm was then applied to determine the optimal configuration of arteries required to supply brain tissue. The model was validated through comparison of this ideal, entirely optimised, brain vasculature with the structure and properties of real arteries. This analysis established that the human cerebral vasculature is highly optimised; closely resembling the most energy efficient arrangement of vessels. In addition to local adherence to fluid dynamical optimisation principles, the optimised vasculature reproduced expected brain perfusion territories, featuring well-defined boundaries between anterior, middle and posterior regions. This validated brain vascular model and algorithm can be used for patient-specific modelling of stroke and cerebral haemodynamics, identification of sub-optimal conditions associated with vascular disease, and optimising vascular structures for tissue engineering applications and artificial organ design.

Simulated annealing approach to vascular structure with application to the coronary arteries.

Do the complex processes of angiogenesis during organism development ultimately lead to a near optimal coronary vasculature in the organs of adult mammals? We examine this hypothesis using a powerful and universal method, built on physical and physiological principles, for the determination of globally energetically optimal arterial trees. The method is based on simulated annealing, and can be used to examine arteries in hollow organs with arbitrary tissue geometries. We demonstrate that the approach can generate in silico vasculatures which closely match porcine anatomical data for the coronary arteries on all length scales, and that the optimized arterial trees improve systematically as computational time increases. The method presented here is general, and could in principle be used to examine the arteries of other organs. Potential applications include improvement of medical imaging analysis and the design of vascular trees for artificial organs.

Size distribution of air bubbles entering the brain during cardiac surgery.

BACKGROUND: Thousands of air bubbles enter the cerebral circulation during cardiac surgery, but whether high numbers of bubbles explain post-operative cognitive decline is currently controversial. This study estimates the size distribution of air bubbles and volume of air entering the cerebral arteries intra-operatively based on analysis of transcranial Doppler ultrasound data. METHODS: Transcranial Doppler ultrasound recordings from ten patients undergoing heart surgery were analysed for the presence of embolic signals. The backscattered intensity of each embolic signal was modelled based on ultrasound scattering theory to provide an estimate of bubble diameter. The impact of showers of bubbles on cerebral blood-flow was then investigated using patient-specific Monte-Carlo simulations to model the accumulation and clearance of bubbles within a model vasculature. RESULTS: Analysis of Doppler ultrasound recordings revealed a minimum of 371 and maximum of 6476 bubbles entering the middle cerebral artery territories during surgery. This was estimated to correspond to a total volume of air ranging between 0.003 and 0.12 mL. Based on analysis of a total of 18667 embolic signals, the median diameter of bubbles entering the cerebral arteries was 33 µm (IQR: 18 to 69 µm). Although bubble diameters ranged from ~5 µm to 3.5 mm, the majority (85%) were less than 100 µm. Numerous small bubbles detected during cardiopulmonary bypass were estimated by Monte-Carlo simulation to be benign. However, during weaning from bypass, showers containing large macro-bubbles were observed, which were estimated to transiently affect up to 2.2% of arterioles. CONCLUSIONS: Detailed analysis of Doppler ultrasound data can be used to provide an estimate of bubble diameter, total volume of air, and the likely impact of embolic showers on cerebral blood flow. Although bubbles are alarmingly numerous during surgery, our simulations suggest that the majority of bubbles are too small to be harmful.

Gap modification of atomically thin boron nitride by phonon mediated interactions.

: A theory is presented for the modification of bandgaps in atomically thin boron nitride (BN) by attractive interactions mediated through phonons in a polarizable substrate, or in the BN plane. Gap equations are solved, and gap enhancements are found to range up to 70% for dimensionless electron-phonon coupling λ =1, indicating that a proportion of the measured BN bandgap may have a phonon origin.

Sizing gaseous emboli using Doppler embolic signal intensity.

Extension of transcranial Doppler embolus detection to estimation of bubble size has historically been hindered by difficulties in applying scattering theory to the interpretation of clinical data. This article presents a simplified approach to the sizing of air emboli based on analysis of Doppler embolic signal intensity, by using an approximation to the full scattering theory that can be solved to estimate embolus size. Tests using simulated emboli show that our algorithm is theoretically capable of sizing 90% of "emboli" to within 10% of their true radius. In vitro tests show that 69% of emboli can be sized to within 20% of their true value under ideal conditions, which reduces to 30% of emboli if the beam and vessel are severely misaligned. Our results demonstrate that estimation of bubble size during clinical monitoring could be used to distinguish benign microbubbles from potentially harmful macrobubbles during intraoperative clinical monitoring.

Embolus trajectory through a physical replica of the major cerebral arteries.

BACKGROUND AND PURPOSE: The observed distribution of cerebral infarcts varies markedly from expectations based on blood-flow volume or Doppler embolus detection. In this study, we used an in vitro model of the cerebral arteries to test whether embolus microspheres encountering the circle of Willis are carried proportionally to volume flow or express a preferred trajectory related to arterial morphology or embolus size. METHODS: Our model consisted of a patient-specific silicone replica of the cerebral macrocirculation featuring physiologically realistic pulsatile flow of a blood-mimicking fluid at approximately 1000 mL/min and an input pressure of approximately 150/70 mm Hg. Particles of 200, 500, and 1000 microm diameter with equivalent density to thrombus were introduced to the carotid arteries and counted on exiting the model outlets. RESULTS: The middle cerebral arteries (MCAs) of the replica attracted a disproportionate number of emboli compared with the anterior cerebral arteries; 98%+/-3% of 1000 microm and 93%+/-2% of 500 microm emboli entered the MCA compared with 82%+/-5% of the flow. The observed distribution of large emboli was consistent with the ratio of MCA:anterior cerebral artery infarcts, approximately 95% of which occur in territories supplied by the MCA. With decreasing embolus size, the distribution of emboli approaches that of the flow (approximately 89% of 200 microm emboli took the MCA). CONCLUSIONS: Embolus trajectory through the cerebral arteries is dependent on embolus size and strongly favors the MCA for large emboli. The 70:30 ratio of MCA:anterior cerebral artery emboli observed by Doppler ultrasound is consistent with the trajectories of small emboli that tend to be asymptomatic.

Revealing the mechanisms underlying embolic stroke using computational modelling.

Computational forecasting of arterial blockages in a virtual patient has the potential to provide the next generation of advanced clinical monitoring aids for stroke prevention. As a first step towards a physiologically realistic virtual patient, we have created a computer model investigating the effects of emboli (particles or gas bubbles) as they become lodged in the brain. Our model provides a framework for predicting the severity of microvascular obstruction by simulating fundamental interactions between emboli and the fractal geometry of the arterial tree through which they travel. The model vasculature consisted of a bifurcating fractal tree comprising over a million branches ranging between 1 mm and 12 microm in diameter. Motion of emboli through the tree was investigated using a Monte Carlo simulation to evaluate the effects of the embolus size, clearance time and embolization rate on the number and persistence of blocked arterioles. Our simulations reveal with striking clarity that the relationship between embolus properties and vascular obstruction is nonlinear. We observe a rapid change between free-flowing and severely blocked arteries at specific combinations of the embolus size and embolization rate. The model predicts distinct patterns of cerebral injury for solid and gaseous emboli which are consistent with clinical observations. Solid emboli are predicted to be responsible for focal persistent injuries, while fast-clearing gas emboli produce diffuse transient blockages similar to global hypoperfusion. The impact of solid emboli was found to be dramatically reduced by embolus fragmentation. Computer simulations of embolization provide a novel means of investigating the role of emboli in producing neurological injury and assessing effective strategies for stroke prevention.