Multifidelity and multiscale modeling of the spleen function in sickle cell disease with in vitro, ex vivo and in vivo validations

镰状细胞病脾功能的多保真度和多尺度建模，并进行体外、离体和体内验证

基本信息

批准号：
10685262
负责人：
Pierre BUFFET
金额：
$ 63.63万
依托单位：
BROWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-19 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10685262
关键词：
Accounting Acute Adhesions Adhesiveness Adhesives Anemia Biomechanics Blood specimen Cell Communication Cell Shape Cell model Circulation Clinical Collaborations Complement Complex Complication Computer Models Coupling Data Decision Making Deterioration Devices Erythrocytes Expectancy Fiber Filtration Goals Hematological Disease Hepatic Hereditary Disease Hereditary Spherocytosis Human Hypoxia Immune system In Vitro Learning Life Link Macrophage Malaria Measures Mechanics Medical Microfluidic Microchips Microfluidics Modeling Molecular Morphology Mutate Organ Output Oxygen Paper Paris, France Pathogenicity Patients Perfusion Phagocytosis Physicians Play Process Prognosis Proteins Quality of life Residual state Role Sampling Shapes Sickle Cell Sickle Cell Anemia Sickle Hemoglobin Source Spleen Stem cell transplant Surface System Training Validation acute chest syndrome biophysical properties cohort data integration deep learning deep neural network design ex vivo perfusion experimental study feeding gene therapy hemoglobin polymer high dimensionality human subject improved in silico in vivo learning progression mimetics molecular dynamics mouse model multi-scale modeling neural network neurotensin mimic 2 particle predictive modeling prevent retention rate risk prediction senescence sickling simulation spatiotemporal tool vaso-occlusive crisis

项目摘要

Project Summary The spleen plays a key role in the human immune system but also clears senescent red blood cells (RBC) from the circulation and those altered by acquired or inherited diseases. In patients with sickle cell disease (SCD), the spleen is one of the first targets of pathogenic processes and a potential protector against major complications. Under hypoxic conditions, mutated sickle hemoglobin (HbS) polymerizes to fibers which increase both the stiffness and adhesion of RBC. Splenic filtration of altered RBC prone to sickling (a process that cannot be directly observed in human subjects) contributes to anemia and likely triggers acute splenic sequestration crises (ASSC). On the other hand, it potentially prevents complications associated with intravascular sickling. Self- amplified blockade of vessels with sickled RBCs is indeed a hallmark of vaso-occlusive crises, acute chest syndrome, and acute hepatic crises, that severely impact the life quality and expectancy of patients with SCD. We propose to formulate and validate a new predictive modeling framework for how the spleen filters altered RBC in SCD by synergistically integrating in silico, in vitro, ex vivo and in vivo data using multifidelity-based neural networks (NN). This will deliver predictive models that can continuously learn when new data become available, a paradigm shift in biomedical modeling. We will develop multiscale/multifidelity computational models (and corresponding NN implementations) that link sub-cellular, cellular, and vessel level phenomena spanning across four orders of magnitude in spatio-temporal scales. This scale coupling will be accomplished using a molecular dynamics/dissipative particle dynamics (MD/DPD) framework. We will validate these predictive computational models by data from in vitro and ex vivo experiments, and RBC quantitative features collected in SCD patients. Specifically, we will use three new spleen-on-a-chip microfluidic devices with oxygen control and the unique human spleen perfusion setup of our foreign partner, with the following aims: Aim 1: Develop and validate a splenic inter-endothelial slit filtration model; Aim 2: Develop new models of RBC macrophage adhesion and of phagocytosis in the spleen; Aim 3: Perform Spleen-on-a-Chip experiments and validation; Aim 4: Validate the predictive framework based on RBC samples from patients. Realization of our four Specific Aims will significantly increase our understanding of the complex pathogenic and protective roles of the spleen in SCD. Feeding our new multifidelity neural networks with morphological and functional measures of RBC circulating in SCD patients will lead to models for residual spleen function in SCD, which should help predict the risk of acute splenic sequestration crises, and guide optimal timing for Stem Cell Transplantation or Gene Therapy. The new paradigm in using deep learning tools to integrate data from different sources will be applicable to modeling many other blood diseases.

项目概要脾脏在人体免疫系统中发挥着关键作用，同时还能清除体内衰老的红细胞 (RBC)。循环以及因获得性或遗传性疾病而改变的循环。在镰状细胞病 (SCD) 患者中，脾脏是致病过程的首要目标之一，也是预防重大并发症的潜在保护者。在缺氧条件下，突变的镰状血红蛋白 (HbS) 聚合成纤维，从而增加红细胞的硬度和粘附力。改变红细胞的脾滤过容易发生镰状化（这一过程不能被在人类受试者中直接观察到）导致贫血并可能引发急性脾隔离危机（ASSC）。另一方面，它有可能预防与血管内镰状化相关的并发症。自己- 镰状红细胞对血管的放大阻塞确实是血管闭塞危机、急性胸闷的标志综合征和急性肝危象，严重影响 SCD 患者的生活质量和预期。我们建议制定并验证一个新的预测模型框架，以了解脾脏过滤器如何改变使用基于多保真度的计算机模拟、体外、离体和体内数据协同整合 SCD 中的 RBC 神经网络（NN）。这将提供预测模型，可以在新数据出现时不断学习可用，这是生物医学建模的范式转变。我们将开发多尺度/多保真度计算模型（以及相应的神经网络实现）将亚细胞、细胞和血管水平现象联系起来时空尺度跨越四个数量级。这种规模耦合将使用分子动力学/耗散粒子动力学（MD/DPD）框架。我们将验证这些预测计算模型来自体外和离体实验的数据，以及收集的红细胞定量特征心源性猝死患者。具体来说，我们将使用三种新型脾脏芯片微流体装置，具有氧气控制和我们的外国合作伙伴独特的人脾灌注装置，其目标如下：目标 1：开发和验证脾内皮缝滤过模型；目标 2：开发红细胞巨噬细胞粘附的新模型和脾脏的吞噬作用；目标 3：进行脾脏芯片实验和验证；目标 4：验证基于患者红细胞样本的预测框架。实现我们的四个具体目标将显着增加我们对复杂致病和疾病的理解。脾脏在 SCD 中的保护作用。为我们新的多保真神经网络提供形态学和 SCD 患者中红细胞循环的功能测量将产生 SCD 中残余脾功能的模型，这应该有助于预测急性脾隔离危机的风险，并指导干细胞的最佳时机移植或基因治疗。使用深度学习工具集成来自不同领域的数据的新范例来源将适用于对许多其他血液疾病进行建模。

项目成果

期刊论文数量（22）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients.

深度迁移学习和数据增强可改善 2 型糖尿病患者的血糖水平预测。

DOI：
发表时间：
2021-07-14
期刊：
影响因子：
0
作者：
Deng, Yixiang;Lu, Lu;Aponte, Laura;Angelidi, Angeliki M;Novak, Vera;Karniadakis, George Em;Mantzoros, Christos S
通讯作者：
Mantzoros, Christos S

How the spleen reshapes and retains young and old red blood cells: A computational investigation.

脾脏如何重塑并保留年轻和年老的红细胞：一项计算研究。

DOI：
发表时间：
2021
期刊：
影响因子：
4.3
作者：
Li, He;Liu, Zixiang Leonardo;Lu, Lu;Buffet, Pierre;Karniadakis, George Em
通讯作者：
Karniadakis, George Em

Two-component macrophage model for active phagocytosis with pseudopod formation.

具有伪足形成的主动吞噬作用的双组分巨噬细胞模型。

DOI：
发表时间：
2024-03-25
期刊：
影响因子：
3.4
作者：
Wang, Shuo;Ma, Shuhao;Li, He;Dao, Ming;Li, Xuejin;Karniadakis, George Em
通讯作者：
Karniadakis, George Em

AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images.

AOSLO-net：一种基于深度学习的方法，用于从自适应光学扫描激光检眼镜图像自动分割视网膜微动脉瘤。

DOI：
发表时间：
2022-08-01
期刊：
影响因子：
3
作者：
Zhang, Qian;Sampani, Konstantina;Xu, Mengjia;Cai, Shengze;Deng, Yixiang;Li, He;Sun, Jennifer K;Karniadakis, George Em
通讯作者：
Karniadakis, George Em

Circulating cell clusters aggravate the hemorheological abnormalities in COVID-19.

循环细胞簇加剧了 COVID-19 的血液流变学异常。

DOI：
发表时间：
2022-09-20
期刊：
影响因子：
3.4
作者：
Javadi, Elahe;Li, He;Gallastegi, Ander Dorken;Frydman, Galit H;Jamali, Safa;Karniadakis, George Em
通讯作者：
Karniadakis, George Em