Stroke secondary prevention: everyone's business

Stroke secondary prevention is everyone’s business and requires cohesive working across the multiprofessional team and beyond [...

Scientific Machine Learning for Modeling and Simulating Complex Fluids

The formulation of rheological constitutive equations -- models that relate internal stresses and deformations in complex fluids -- is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine learning constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data, and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these 'digital fluid twins' to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation -- a task that is not achievable using any previously developed data-driven rheological equation of state.Comment: 13 pages, 4 figure

Altered mitochondrial function and energy metabolism is associated with a radioresistant phenotype in oesophageal adenocarcinoma

Neoadjuvant chemoradiation therapy (CRT) is increasingly the standard of care for locally advanced oesophageal cancer. A complete pathological response to CRT is associated with a favourable outcome. Radiation therapy is important for local tumour control, however, radioresistance remains a substantial clinical problem. We hypothesise that alterations in mitochondrial function and energy metabolism are involved in the radioresistance of oesophageal adenocarcinoma (OAC). To investigate this, we used an established isogenic cell line model of radioresistant OAC. Radioresistant cells (OE33 R) demonstrated significantly increased levels of random mitochondrial mutations, which were coupled with alterations in mitochondrial function, size, morphology and gene expression, supporting a role for mitochondrial dysfunction in the radioresistance of this model. OE33 R cells also demonstrated altered bioenergetics, demonstrating significantly increased intracellular ATP levels, which was attributed to enhanced mitochondrial respiration. Radioresistant cells also demonstrated metabolic plasticity, efficiently switching between the glycolysis and oxidative phosphorylation energy metabolism pathways, which were accompanied by enhanced clonogenic survival. This data was supported in vivo, in pre-treatment OAC tumour tissue. Tumour ATP5B expression, a marker of oxidative phosphorylation, was significantly increased in patients who subsequently had a poor pathological response to neoadjuvant CRT. This suggests for the first time, a role for specific mitochondrial alterations and metabolic remodelling in the radioresistance of OAC

The Medium Amplitude Response of Nonlinear Maxwell-Oldroyd Type Models in Simple Shear

A general framework for Maxwell-Oldroyd type differential constitutive models is examined, in which an unspecified nonlinear function of the stress and rate-of-deformation tensors is incorporated into the well-known corotational version of the Jeffreys model discussed by Oldroyd. For medium amplitude simple shear deformations, the recently developed mathematical framework of medium amplitude parallel superposition (MAPS) rheology reveals that this generalized nonlinear Maxwell model can produce only a limited number of distinct signatures, which combine linearly in a well-posed basis expansion for the third order complex viscosity. This basis expansion represents a library of MAPS signatures for distinct constitutive models that are contained within the generalized nonlinear Maxwell model. We describe a framework for quantitative model identification using this basis expansion, and discuss its limitations in distinguishing distinct nonlinear features of the underlying constitutive models from medium amplitude shear stress data. The leading order contributions to the normal stress differences are also considered, revealing that only the second normal stress difference provides distinct information about the weakly nonlinear response space of the model. After briefly considering the conditions for time-strain separability within the generalized nonlinear Maxwell model, we apply the basis expansion of the third order complex viscosity to derive the medium amplitude signatures of the model in specific shear deformation protocols. Finally, we use these signatures for estimation of model parameters from rheological data obtained by these different deformation protocols, revealing that three-tone oscillatory shear deformations produce data that is readily able to distinguish all features of the medium amplitude, simple shear response space of this generalized class of constitutive models.Comment: 26 pages, 11 figure

Effect of Motion on the ADC Quantification Accuracy of Whole-Body DWIBS

y Diffusion-weighted whole-body imaging with background body signal subtraction was introduced as a qualitative approach to detecting metastases in the body. A liver-mimicking phantom with embedded tumours that could be moved to replicate respiratory motion was developed to assess its ability to accurately quantify ADC values. RESULTS: Mean tumour ADC values were unaltered by the motion; however, a significant (p \u3c 0.05) increase in the spread of ADC values was measured, even for relatively large tumours. CONCLUSIONS: These findings may be of significance in cancer therapy monitoring where subtle changes in ADC histograms may reveal changes in tumour heterogeneity

Advances in precision medicine: tailoring individualised therapies

The traditional bench-to-bedside pipeline involves using model systems and patient samples to provide insights into pathways deregulated in cancer. This discovery reveals new biomarkers and therapeutic targets, ultimately stratifying patients and informing cohort-based treatment options. Precision medicine (molecular profiling of individual tumors combined with established clinical-pathological parameters) reveals, in real-time, individual patient's diagnostic and prognostic risk profile, informing tailored and tumor-specific treatment plans. Here we discuss advances in precision medicine presented at the Irish Association for Cancer Research Annual Meeting, highlighting examples where personalized medicine approaches have led to precision discovery in individual tumors, informing customized treatment programs

Big data-led cancer research, applications and insights

Insights distilled from integratingmultiple big-data or "omic" datasets have revealed functional hierarchies of molecular networks driving tumorigenesis and modifiers of treatment response. Identifying these novel key regulatory and dysregulated elements is now informing personalized medicine. Crucially, although there are many advantages to this approach, there are several key considerations to address. Here, we examine how this big data-led approach is impacting many diverse areas of cancer research, through review of the key presentations given at the Irish Association for Cancer Research Meeting and importantly how the results may be applied to positively affect patient outcomes