EPSRC Centre for New Mathematical Sciences Capabilities for Healthcare Technologies

EPSRC 医疗保健技术新数学科学能力中心

• 批准号: EP/N014499/1
• 负责人: K Chen
• 金额: $ 255.39万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN014499%2F1
• 关键词: EPSRC; Centre; New; Mathematical; Sciences

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.

随着生活质量的不断提高，平均寿命将继续增加。强调这一改善的是英国政府对NHS的大量支持（2011/12年度为1335亿英镑）和英国制药行业的研发巨额投资（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保健。这种方法需要来自广泛专业学科的研究人员的集体努力。 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中心将由应用和纯数学家，统计学家以及医疗保健研究人员，临床医生和实业家组成的大型多学科团队组成，与15个高等教育机构和40个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体：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（英国独一无二），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。以下重点介绍了几个研究主题：首先，更好地了解细胞和组织的相互作用动力学对于开发有效的未来癌症治疗方法至关重要。目前的大部分工作都是二维的，有限制性的假设，无法获得用于建模的真实的数据。我们将使用在UoL的CCI生成的3D设置中细胞相互作用的无与伦比的真实的数据。所获得的真实图像将具有低对比度和噪音，并将由我们的成像团队通过开发准确和高分辨率的成像模型进行分析和增强。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三维模型，使用我们的成像数据和分析工具，调查和预测的时空动态。 其次，更好的药物如何传递到组织细胞的模型将改善药物毒性的个性化预测。我们将结合联合收割机的新的成像数据的药物渗透到三维实验模型系统与多尺度的数学模型，从细胞水平的这些模型系统的比例，最终目的是更好地在体外到体内的预测。 第三，存在许多相互竞争的成像处理模型和软件。然而，对于具有噪声和低对比度的真实的图像，很少有方法是鲁棒的和准确的。为了改进建模，应用数学家和纯数学家合作考虑使用更复杂的双曲几何和黎曼曲面和分数微积分工具来满足对准确性的要求，应用数学家和统计学家将合作设计更好的数据保真度项来模拟图像差异。 第四，对现有抗生素的抗药性意味着以前可以治疗的疾病再次变得致命。为了理解和减轻这一点，需要更好地了解这种耐药性如何在人类相互作用网络中建立，以及它如何依赖于抗生素处方实践。为了理解这些场景，需要更好地理解异构环境中的数学竞赛。我们的团队将分析动力系统的数学专家与抗菌素耐药性专家和全科医生联系起来，以确定减轻或减缓抗菌素耐药性发展的策略。 我们的研究主题是一致的，并将增加价值，现有的和当前的UOL和研究理事会战略投资，活动和未来的计划。

项目成果

Martin Integral Representation for Nonharmonic Functions and Discrete Co-Pizzetti Series

非调和函数和离散 Co-Pizzetti 级数的 Martin 积分表示

• DOI: 10.1134/s0001434619110014
• 发表时间: 2019
• 期刊: Mathematical Notes
• 影响因子: 0.6
• 作者: Boiko T

Coordinating the real-time use of global influenza activity data for better public health planning

• DOI: 10.1111/irv.12705
• 发表时间: 2019-12-03
• 期刊: INFLUENZA AND OTHER RESPIRATORY VIRUSES
• 影响因子: 4.4
• 作者: Biggerstaff, Matthew;Dahlgren, Fredrick Scott;Wu, Joseph T.
• 通讯作者: Wu, Joseph T.

A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity

• DOI: 10.1016/j.patcog.2015.08.022
• 发表时间: 2016-03-01
• 期刊: PATTERN RECOGNITION
• 影响因子: 8
• 作者: Ali, Haider;Badshah, Noor;Khan, Gulzar Ali
• 通讯作者: Khan, Gulzar Ali

On Periodic Asymmetric Extrapolation

关于周期性不对称外推

• DOI: 10.1134/s0001434618110044
• 发表时间: 2018
• 期刊: Mathematical Notes
• 影响因子: 0.6
• 作者: Boiko T

Deformation and dynamic response of abdominal aortic aneurysm sealing.

• DOI: 10.1038/s41598-017-17759-3
• 发表时间: 2017-12-18
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Argani LP;Torella F;Fisher RK;McWilliams RG;Wall ML;Movchan AB
• 通讯作者: Movchan AB