Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning.
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning.
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DOI：
10.1371/journal.pcbi.1009910
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发表时间：
2022-03
影响因子：
4.3
通讯作者：
Chopard B
中科院分区：
文献类型：
--
作者：
Dutta R;Zouaoui Boudjeltia K;Kotsalos C;Rousseau A;Ribeiro de Sousa D;Desmet JM;Van Meerhaeghe A;Mira A;Chopard B
心/脑血管疾病（CVD）已成为我们社会的主要健康问题之一。但最近的研究表明，目前检测CVD的病理学测试是无效的，因为它们没有考虑血小板活化的不同阶段或血小板相互作用中涉及的分子动力学，并且不能考虑个体间的差异。在这里，我们提出了一个随机血小板沉积模型和推理计划，估计生物学上有意义的模型参数，使用近似贝叶斯计算与汇总统计，最大限度地区分不同类型的患者。从健康志愿者和不同患者类型收集的数据推断的参数有助于我们确定特定的生物学参数，从而确定每种类型患者功能障碍背后的生物学推理。这项工作为CVD检测和医疗提供了前所未有的个性化病理测试机会。心血管意外往往是由于血液不足，如血小板功能障碍。目前检测这种功能障碍的诊断技术不够准确，无法确定哪些血小板特性受到影响。我们开发了一种新的方法来描述体外血小板沉积模式的临床意义的患者特定的生物物理量，允许个性化的临床诊断。这种方法结合了数学建模、统计推断技术、机器学习和高性能计算，以估计这些临床相关血小板特性的值。我们展示了我们的方法对三类捐助者，健康志愿者，透析患者和慢性阻塞性肺疾病患者。我们声称，我们的方法开启了心血管疾病治疗和诊断的范式转变，从而实现个性化医疗。
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment. Cardiovascular accidents often result from blood deficiencies, such as platelets dysfunction. Current diagnosis techniques to detect such dysfunctions are not sufficiently accurate and unable to determine which platelet properties are affected. We develop a novel approach to describe in-vitro platelets deposition patterns in terms of clinically meaningful patient specific bio-physical quantities that allow for personalized clinical diagnostics. This approach combines mathematical modeling, statistical inference techniques, machine learning and high performance computation to estimate the values of these clinically relevant platelet properties. We demonstrate our approach on three classes of donors, healthy volunteers, patients subject to dialysis and patients with chronic obstructive pulmonary disease. We claim that our approach opens a paradigm shift for the treatment and diagnosis of cardiovascular diseases, leading to personalized medicine.
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影响因子：
6.5
作者：
Lintusaari J;Gutmann MU;Dutta R;Kaski S;Corander J
通讯作者：
Corander J
影响因子：
5.6
作者：
Koltai K;Kesmarky G;Feher G;Tibold A;Toth K
通讯作者：
Toth K
影响因子：
120.7
作者：
Breet, Nicoline J.;van Werkum, Jochem W.;ten Berg, Jurrien M.
通讯作者：
ten Berg, Jurrien M.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
3.4
作者：
Bian X;Kim C;Karniadakis GE
通讯作者：
Karniadakis GE