EPSRC Centre for New Mathematical Sciences Capabilities for Healthcare Technologies

随着生活质量的不断提高，人们的平均寿命将继续增加。突显这一改善的是英国政府对NHS的大量支持(2011/12年度为1335亿GB)，以及英国制药业的研发巨额投资(2011/12年度为49亿英镑)。所有利益相关者对高质量医疗保健的期望不可避免地很高。幸运的是，最近科学技术的进步使我们能够努力实现个性化医疗和预防性护理。这种方法需要来自广泛专业领域的研究人员的共同努力。科学和工程的进步往往伴随着数学科学的重大发展，因为后者是所有其他科学的基础。UOL中心将由一支由应用和纯数学家、统计学家和医疗保健研究人员、临床医生和工业家组成的大型多学科团队组成，与15所高等院校和40家NHS信托基金以及其他行业合作伙伴合作，包括我们最强大的团队：MRC药物安全科学中心、细胞成像中心(CCI用于实时3D和4D成像)、数学成像技术中心(在英国独有)、利物浦生物医学EM单位、MRC再生医学中心、NIHR健康保护研究单位、MRC试验方法研究中心。以下是几个研究主题的重点：首先，提高对细胞和组织相互作用动力学的理解对于开发未来有效的癌症治疗方法至关重要。目前的大部分工作都是2D的，具有限制性的假设，并且无法获得用于建模的真实数据。我们将在3D环境中使用UOL CCI生成的无与伦比的细胞相互作用的真实数据。获得的真实图像将具有低对比度和噪声，我们的成像团队将通过开发准确和高分辨率的成像模型来分析和增强这些图像。所需的主要成像工具是分割方法(根据大小、形状和精确边界识别细胞和组织区域等对象)。我们将提出和研究一类新的3D模型，利用我们的成像数据和分析工具来研究和预测时空动力学。其次，更好的药物如何输送到组织细胞的模型将提高对药物毒性的个性化预测。我们将把药物渗透的新成像数据与从细胞水平扩大到这些模型系统的多尺度数学模型结合起来，最终目的是做出更好的体外到体内预测。第三，存在许多相互竞争的图像处理模型和软件。然而，对于含有噪声和低对比度的真实图像，很少有方法是稳健和准确的。为了改进建模，应用数学家和纯数学家将考虑使用双曲几何、黎曼曲面和分数微积分等更复杂的工具来满足精度要求，应用数学家和统计学家将合作设计更好的数据保真项来对图像差异进行建模。第四，对当前抗生素的抗药性意味着以前可以治疗的疾病正在再次变得致命。为了了解和缓解这种情况，需要更好地了解这种耐药性是如何在人类互动网络中建立起来的，以及它如何依赖于抗生素处方做法。要理解这些场景，需要更好地理解异类环境中的数学竞赛。我们的团队将动态系统分析方面的数学专家与抗菌素耐药性专家和全科医生联系起来，以确定缓解或减缓抗菌素耐药性发展的策略。我们的研究主题与UOL和研究理事会现有和当前的战略投资、活动和未来计划保持一致，并将增加其价值。

As quality of life constantly improves, the average lifespan will continue to increase. Underlining this improvement is the vast amount of the UK government's support to NHS (£133.5 billion in year 2011/12) and the UK pharmaceutical industry's R&D large investment (4.9 billion to R&D in year 2011/12). The expectation of quality healthcare is inevitably high from all stakeholders. Fortunately recent advances in science and technology have enabled us to work towards personalised medicine and preventative care. This approach calls for a collective effort of researchers from a vast spectrum of specialised subjects. Advances in science and engineering is often accompanied by major development of mathematical sciences, as the latter underpin all other sciences. The UoL Centre will consist of a large and multidisciplinary team of applied and pure mathematicians, and statisticians together with healthcare researchers, clinicians and industrialists, collaborating with 15 HEIs and 40 NHS trusts plus other industrial partners and including our strongest groups:MRC Centre in Drug Safety Science, Centre for Cell imaging (CCI for live 3D and 4D imaging), Centre for Mathematical Imaging Techniques (unique in UK), Liverpool Biomedical EM unit, MRC Regenerative Medicine Hub, NIHR Health Protection Research Units, MRC Hub for Trials Methodology Research. Several research themes are highlighted below:Firstly, an improved understanding of the interaction dynamics of cells and tissues is crucial to developing effective future cures for cancer. Much of the current work is in 2D, with restrictive assumptions and without access to real data for modelling. We shall use the unparalleled real data of cell interactions in a 3D setting, generated at UoL's CCI. The real-life images obtained will have low contrast and noise and they will be analysed and enhanced by our imaging team through developing accurate and high resolution imaging models. The main imaging tools needed are segmentation methods (identifying objects such as cells and tissues regions in terms of sizes, shapes and precise boundaries). We shall propose and study a class of new 3D models, using our imaging data and analysis tools, to investigate and predict the spatial-temporal dynamics. Secondly, better models of how drugs are delivered to cells in tissues will improve personalised predictions of drug toxicity. We shall combine novel-imaging data of drug penetration into 3D experimental model systems with multi-scale mathematical models which scale-up from the level of cells to these model systems, with the ultimate aim of making better in-vitro to in-vivo predictions. Thirdly, there exist many competing models and software for imaging processing. However, for real images that have noise and are of low contrast, few methods are robust and accurate. To improve the modelling, applied and pure mathematicians team up to consider using more sophisticated tools of hyperbolic geometry and Riemann surfaces and fractional calculus to meet the demand for accuracy, and, applied mathematicians and statisticians will team up to design better data fidelity terms to model image discrepancies. Fourthly, resistance to current antibiotics means that previously treatable diseases are becoming deadly again. To understand and mitigate this, a better understanding is needed for how this resistance builds up across the human interaction networks and how it depends on antibiotic prescribing practices. To understand these scenarios, the mathematics competition in heterogeneous environments needs to be better understood. Our team links mathematical experts in analysing dynamical systems with experts in antimicrobial resistance and GPs to determine strategies that will mitigate or slow the development of anti-microbial resistance. Our research themes are aligned with, and will add value to, existing and current UoL and Research Council strategic investments, activities and future plans.