中国神经再生研究(英文版) ›› 2016, Vol. 11 ›› Issue (12): 1900-1903.doi: 10.4103/1673-5374.195274

• 综述:退行性病与再生 • 上一篇    下一篇

更好地监测多发性硬化的症病情进展:需多种手段

  

  • 收稿日期:2016-11-28 出版日期:2016-12-31 发布日期:2016-12-31

Multiple sclerosis: integration of modeling with biology, clinical and imaging measures to provide better monitoring of disease progression and prediction of outcome

Shikha Jain Goodwin1, 2, 3, *   

  1. 1 Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, USA 2 Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA 3 Brain Sciences Center, VA Medical Center, Minneapolis, MN, USA
  • Received:2016-11-28 Online:2016-12-31 Published:2016-12-31
  • Contact: Shikha Jain Goodwin, Ph.D., shikha@umn.edu.

摘要:

我们讨论和验证了结合生物学、临床和影像学资料的计算模型多种手段相结合对于检测多发性硬化进展及预测结局的作用。多发性硬化症是成人神经性残疾的主要病因,在美国每年有大约有280亿美元花费于此。遗憾的是,多发性硬化症目前还没有治愈方法,该疾病的其中两个方面使得护理和治疗变得更加复杂:极度可变的疾病过程,且往往是临床上无记载的过程。具体来说,多发性硬化症具有可变性,因为这种疾病有各种亚型。这些亚型的复发率、临床症状、轨迹、根本原因以及疾病治疗方法都不同。由于根本病因为脱髓鞘(沿轴突变性),其可以针对任何脑区产生大范围临床症状。这导致多发性硬化症的临床病例缺失,目前用于评估病情严重程度的行为和成像方法本身不足,让科学家和医生无法对疾病进展进行准确预测及评估。因此功能建模和客观功能检测可极大地改善这种预测精度。

orcid: 0000-0001-9935-4359 (Shikha Jain Goodwin)

Abstract: Multiple Sclerosis (MS) is a major cause of neurological disability in adults and has an annual cost of approximately $28 billion in the United States. MS is a very complex disorder as demyelination can happen in a variety of locations throughout the brain; therefore, this disease is never the same in two patients making it very hard to predict disease progression. A modeling approach which combines clinical, biological and imaging measures to help treat and fght this disorder is needed. In this paper, I will outline MS as a very heterogeneous disorder, review some potential solutions from the literature, demonstrate the need for a biomarker and will discuss how computational modeling combined with biological, clinical and imaging data can help link disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism.

Key words: multiple sclerosis, modeling, integration, disease progression, disease prediction