Alexiou Athanasios, Mantzavinos Vasileios D, Greig Nigel H, Kamal Mohammad A
Novel Global Community Educational FoundationalHebersham, NSW, Australia.
Department of Computer Science and Biomedical Informatics, University of ThessalyLamia, Greece.
Front Aging Neurosci. 2017 Mar 31;9:77. doi: 10.3389/fnagi.2017.00077. eCollection 2017.
Alzheimer's disease treatment is still an open problem. The diversity of symptoms, the alterations in common pathophysiology, the existence of asymptomatic cases, the different types of sporadic and familial Alzheimer's and their relevance with other types of dementia and comorbidities, have already created a myth-fear against the leading disease of the twenty first century. Many failed latest clinical trials and novel medications have revealed the early diagnosis as the most critical treatment solution, even though scientists tested the amyloid hypothesis and few related drugs. Unfortunately, latest studies have indicated that the disease begins at the very young ages thus making it difficult to determine the right time of proper treatment. By taking into consideration all these multivariate aspects and unreliable factors against an appropriate treatment, we focused our research on a non-classic statistical evaluation of the most known and accepted Alzheimer's biomarkers. Therefore, in this paper, the code and few experimental results of a computational Bayesian tool have being reported, dedicated to the correlation and assessment of several Alzheimer's biomarkers to export a probabilistic medical prognostic process. This new statistical software is executable in the Bayesian software Winbugs, based on the latest Alzheimer's classification and the formulation of the known relative probabilities of the various biomarkers, correlated with Alzheimer's progression, through a set of discrete distributions. A user-friendly web page has been implemented for the supporting of medical doctors and researchers, to upload Alzheimer's tests and receive statistics on the occurrence of Alzheimer's disease development or presence, due to abnormal testing in one or more biomarkers.
阿尔茨海默病的治疗仍然是一个悬而未决的问题。症状的多样性、常见病理生理学的改变、无症状病例的存在、散发性和家族性阿尔茨海默病的不同类型及其与其他类型痴呆症和合并症的相关性，已经引发了对21世纪这一主要疾病的神话般的恐惧。尽管科学家们对淀粉样蛋白假说及少数相关药物进行了测试，但许多最新临床试验和新型药物的失败表明早期诊断是最关键的治疗方案。不幸的是，最新研究表明该疾病在非常年轻时就开始了，因此难以确定恰当治疗的合适时机。考虑到所有这些不利于恰当治疗的多变量因素和不可靠因素，我们将研究重点放在对最知名且被认可的阿尔茨海默病生物标志物进行非经典统计评估上。因此，在本文中，报告了一个计算贝叶斯工具的代码及一些实验结果，该工具致力于对几种阿尔茨海默病生物标志物进行相关性分析和评估，以输出一个概率性医学预后过程。这个新的统计软件可在贝叶斯软件Winbugs中执行，基于最新的阿尔茨海默病分类以及通过一组离散分布与阿尔茨海默病进展相关的各种生物标志物已知相对概率的公式。已经实现了一个用户友好的网页，以支持医生和研究人员上传阿尔茨海默病测试结果，并接收因一种或多种生物标志物检测异常而导致阿尔茨海默病发展或存在情况发生的统计数据。
