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

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 fight 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.

Protective effects of pharmacological therapies in animal models of multiple sclerosis: a review of studies 2014–2019

Multiple sclerosis(MS)is an inflammatory demyelinating disease of the central nervous system.The disability caused by inflammatory demyelination clinically dominates the early stages of relapsing-remitting MS and is reversible.Once there is considerable loss of axons,MS patients enter a secondary progressive stage.Disease-modifying drugs currently in use for MS suppress the immune system and reduce relapse rates but are not effective in the progressive stage.Various animal models of MS(mostly mouse and rat)have been established and proved useful in studying the disease process and response to therapy.The experimental autoimmune encephalomyelitis animal studies reviewed here showed that a chronic progressive disease can be induced by immunization with appropriate amounts of myelin oligodendrocyte glycoprotein together with mycobacterium tuberculosis and pertussis toxin in Freund's adjuvant.The clinical manifestations of autoimmune encephalomyelitis disease were prevented or reduced by treatment with certain pharmacological agents given prior to,at,or after peak disease,and the agents had protective effects as shown by inhibiting demyelination and damage to neurons,axons and oligodendrocytes.In the cuprizone-induced toxicity animal studies,the pharmacological agents tested were able to promote remyelination and increase the number of oligodendrocytes when administered therapeutically or prophylactically.A monoclonal IgM antibody protected axons in the spinal cord and preserved motor function in animals inoculated with Theiler's murine encephalomyelitis virus.In all these studies the pharmacological agents were administered singly.A combination therapy may be more effective,especially using agents that target neuroinflammation and neurodegeneration,as they may exert synergistic actions.

Neuroflament light and heterogeneity of disease progression in amyotrophic lateral sclerosis:development and validation of a prediction model to improve interventional trials

Background:Interventional trials in amyotrophic lateral sclerosis(ALS)sufer from the heterogeneity of the disease as it considerably reduces statistical power.We asked if blood neuroflament light chains(NfL)could be used to antici‑pate disease progression and increase trial power.Methods:In 125 patients with ALS from three independent prospective studies-one observational study and two interventional trials-we developed and externally validated a multivariate linear model for predicting disease pro‑gression,measured by the monthly decrease of the ALS Functional Rating Scale Revised(ALSFRS-R)score.We trained the prediction model in the observational study and tested the predictive value of the following parameters assessed at diagnosis:NfL levels,sex,age,site of onset,body mass index,disease duration,ALSFRS-R score,and monthly ALSFRS-R score decrease since disease onset.We then applied the resulting model in the other two study cohorts to assess the actual utility for interventional trials.We analyzed the impact on trial power in mixed-efects models and compared the performance of the NfL model with two currently used predictive approaches,which anticipate disease progression using the ALSFRS-R decrease during a three-month observational period(lead-in)or since disease onset(ΔFRS).Results:Among the parameters provided,the NfL levels(P<0.001)and the interaction with site of onset(P<0.01)contributed signifcantly to the prediction,forming a robust NfL prediction model(R=0.67).Model application in the trial cohorts confrmed its applicability and revealed superiority over lead-in andΔFRS-based approaches.The NfL model improved statistical power by 61%and 22%(95%confdence intervals:54%-66%,7%-29%).Conclusion:The use of the NfL-based prediction model to compensate for clinical heterogeneity in ALS could signif‑cantly increase the trial power.NCT00868166,registered March23,2009;NCT02306590,registered December 2,2014.

Promoting remyelination for the treatment of multiple sclerosis:opportunities and challenges

Multiple sclerosis （MS） is a chronic and devastating autoimmune demyelinating disease of the central nervous system. With the increased understanding of the pathophysiology of this disease in the past two decades, many disease-modifying therapies that primarily target adaptive immunity have been shown to prevent exacerbations and new lesions in patients with relapsing-remitting MS. However, these therapies only have limited efficacy on the progression of disability. Increasing evidence has pointed to innate immunity, axonal damage and neuronal loss as important contributors to disease progression. Remyelination of denuded axons is considered an effective way to protect neurons from damage and to restore neuronal function. The identification of several key molecules and pathways controlling the differentiation of oligodendrocyte progenitor cells and myelination has yielded clues for the development of drug candidates that directly target remyelination and neuroprotection. The long-term efficacy of this strategy remains to be evaluated in clinical trials. Here, we provide an overview of current and emerging therapeutic concepts, with a focus on the opportunities and challenges for the remyelination approach to the treatment of MS.

建立老年冠心病中西医结合临床预测模型的构想

心血管病是严重威胁我国居民健康的第一杀手。建立老年冠心病临床预测模型对老年冠心病患者的二级预防有重要意义。现从心血管疾病临床预测模型的国内外研究的现状、建立老年冠心病中西医结合临床预测模型的必要性、难点问题与对策3个方面进行阐述。并认为中医作为祖国传统医学在疾病预后评估方面有其自身的理论体系,在中医理论的指导下,中西医结合临床预测模型必将有助于进一步提高大大提高对患者预后评估的准确度;缺乏高质量、大样本数据是建立老年冠心病中西医结合临床预测模型所面临的最大问题,必须加快构建冠心病中西医结合临床科研一体化云数据平台,实现高效收集整齐、真实、结构化的海量临床数据。

基于ARIMA乘积季节模型的中国流行性腮腺炎发病趋势预测分析

目的构建自回归求和移动平均(Auto-regressive integrated moving average,ARIMA)乘积季节模型,预测分析新型冠状病毒感染(Coronavirus disease 2019,COVID-19)疫情前后中国流行性腮腺炎(流腮)发病趋势。方法收集2008-2021年中国流腮月报告发病数据,基于2008-2018年数据拟合流腮发病ARIMA乘积季节模型;利用拟合模型预测2019-2021年流腮月发病数,评价预测效果。结果2008-2018年中国流腮发病呈3-5年一次流行高峰,夏季和冬季高发。流腮发病的最优拟合模型为ARIMA(2,1,2)(0,1,1)12,模型的相关参数估计值均具有显著性,其残差序列白噪声检验显示均为白噪声序列。2019年、2020年、2021年流腮月发病数的模型预测值与真实值的相对误差范围分别为1.56%-19.30%、41.24%-360.66%、64.46%-267.61%,平均相对误差分别为6.65%、159.08%、177.39%。结论拟合模型可准确预测COVID-19疫情前中国流腮发病,但对疫情期间的发病预测结果偏差较大;需要补充COVID-19疫情后流腮发病数据以拟合更优的预测模型。

乘法季节回归求和移动平均模型在安徽省手足口病预测中的应用研究

目的探讨乘法季节回归求和移动平均模型(ARIMA)在安徽省手足口病发病预测中应用,为手足口病预防控制提供参考。方法根据2009-2014年安徽省手足口病的周发病数据,运用R 3.0.2软件拟合乘法季节性ARIMA模型,并对2015年1~52周发病数进行预测。结果安徽省手足口病预测中最优模型为ARIMA(1,1,1)(0,1,1)52模型,残差统计量检验差异无统计学意义(Box-Ljung=0.004,P=0.950),提示残差为白噪声,模型拟合值和实际值平均绝对误差率为11.32%,2015年1~52周预测值和实际值平均绝对误差率为25.10%。结论建立的乘法季节性ARIMA模型能较好地拟合安徽省手足口病变动趋势,模型预测效果较好,可用于安徽省手足口病短期预测。

α-Sutte模型在疫情预测中的应用:基于R软件

目的 介绍α-Sutte模型的原理、方法,并利用R软件建立α-Sutte模型。比较α-Sutte模型与乘积季节自回归移动平均模型(SARIMA)拟合及预测效果,为α-Sutte模型在疫情预测中的应用提供参考。方法 收集2020年1月1日至2021年7月16日印度、美国、意大利、巴西、俄罗斯、南非各国新型冠状病毒肺炎(COVID-19)逐日累计报告病例数。以首例报告病例时间作为起点,起始日期至2021年6月16日数据作为训练数据,2021年6月17日至2021年7月16日作为测试数据。利用R语言根据α-Sutte模型计算公式自行编写拟合及预测函数α-Sutte。训练数据被用于训练α-Sutte模型和SARIMA模型。建立2个模型预测2021年6月17日至2021年7月16日COVID-19逐日报告病例数。拟合值与训练数据比较、预测值与测试数据比较评价模型拟合及预测效果。采用评价指标为平均绝对误差百分比(MAPE)。结果 印度、美国、意大利、巴西、俄罗斯和南非建立的最优SARIMA模型为SARIMA(5,2,2)、SARIMA(0,2,2)、SARIMA(2,2,2)、SARIMA(3,2,2)、SARIMA(0,2,1)和SARIMA(4,2,3)。α-Sutte和SARIMA模型在印度、美国、意大利、巴西、俄罗斯、南非6个国家拟合的MAPE分别为1.32%、1.34%、0.89%、1.65%、0.99%、0.99%,以及1.51%、1.59%、0.89%、1.67%、1.03%、1.13%。α-Sutte和SARIMA模型在6个国家预测的MAPE分别为0.81%、0.09%、0.13%、1.58%、1.73%、3.77%,以及0.09%、0.09%、0.18%、1.13%、1.83%、3.43%。结论 α-Sutte模型的原理、建模简单,利用R语言建立的模型拟合及预测精度高,值得在疾病监测领域推广使用。