A Biofidelic Model of PTE (Project 1)

PTE 的 Biofidelic 模型（项目 1）

基本信息

批准号：
10713244
负责人：
Beth A Costine-Bartell
金额：
$ 26.1万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10713244
关键词：
Affect Algorithms Animal Model Animals Antiepileptic Agents Behavior Bilateral Biological Markers Blood - brain barrier anatomy Brain Categories Cells Chronic Complex Contusions Convulsions Development Electroencephalography Epilepsy Epileptogenesis Extracellular Matrix Family suidae Frequencies Functional disorder Goals Human Imaging technology Immune Inflammation Injury Investigation Label Lesion Machine Learning Mediating Methods Modeling Morbidity - disease rate Neocortex Neuroglia Noise Pathogenesis Peripheral Phase Plasma Post-Traumatic Epilepsy Refractory Rodent Seizures Serum Signal Transduction Site Synapses TBI Patients Technology Testing Therapeutic Training Traumatic Brain Injury Traumatic Brain Injury recovery base biomarker identification black hole cellular imaging field study healing injury and repair innovation insight machine learning pipeline neocortical neuroblast neurovascular unit new therapeutic target porcine model prevent specific biomarkers targeted treatment therapy development tool

项目摘要

Project 1 Summary Post-traumatic epilepsy (PTE) affects 15-40% of those with traumatic brain injury (TBI) causing significant morbidity even after initial recovery from the TBI. Major limitations in understanding the pathophysiology of PTE are a lack of biofidelic modeling of PTE in the gyrencephalic brain and biomarkers that identify or predict PTE. Our long-term goal is to understand the pathophysiology the leads to PTE in order to develop therapies that halt or inhibit the progression of pathogenesis that leads to post-traumatic epilepsy. The overall objective is to identify biomarkers of PTE in a gyrencephalic brain and the ionic basis of PTE. Our central hypotheses are that long-term ionic changes alters GABAergic signaling leading to PTE and biomarkers of PTE can be identified with machine learning boosting algorithms. The rationale is that a toolkit of automated methods of quantifying biomarkers in a biofidelic model of PTE along with insight into the ionic changes that mediate GABAergic signaling will allow identification of new therapeutic targets and allow efficient testing of those targets to prevent the development of PTE in patients with TBI. We will test our central hypotheses with two Specific Aims: Aim 1: We will train previously established algorithms to identify the best mode, or combination of modes, to identify seizure candidates in these models of PTE and maybe to even predict PTE. We hypothesize that we can use existing algorithms to rapidly analyze behavior and ECoG, identify seizure correlates, and together with peripheral serum biomarkers, use existing boosting algorithms to predict which subjects will develop PTE. Aim 2: We will determine if chronic changes in the ionic basis of GABAergic signaling and in the neocortical network activity are indicated by IED’s and biomarkers in the latent period. We hypothesize that those pigs that develop PTE will have greater peripheral plasma biomarkers associated with inflammation and blood brain barrier opening, greater changes in Cl-o and Cl-I, and faster development of hypersynchronous local and global network activity (EEG). The is approach is innovative in that, we bring cell imaging technology developed in rodent and organotypic culture models and newly developed algorithms to the gyrencephalic brain merging the fields of high technology and large animal models. Our contribution is significant as tools and biomarkers will enable wider use of gyrencephalic models of PTE and prediction of PTE will open up large fields of study not yet possible. Understanding the extracellular-matrix-induced changes that alter the ionic bases of GABAergic signaling and local and global network changes in the same subjects during the course of epileptogenesis may identify mechanisms of epileptogenesis that serve as targets for therapies that may prevent the development of PTE.

项目1概要 15-40% 的创伤性脑损伤 (TBI) 患者患有创伤后癫痫 (PTE) 即使从 TBI 中初步恢复后，发病率仍显着，这是理解 TBI 的主要局限性。 PTE 的病理生理学是在环脑中缺乏 PTE 的生物忠实模型识别或预测 PTE 的生物标志物我们的长期目标是了解 PTE 的病理生理学。导致 PTE 的目的是为了开发能够阻止或抑制导致 PTE 的发病机制进展的疗法总体目标是确定环脑患者 PTE 的生物标志物。我们的中心假设是长期离子变化会改变。导致 PTE 的 GABA 信号和 PTE 的生物标志物可以通过机器学习来识别增强算法的基本原理是量化生物标志物的自动化方法的工具包。 PTE 的生物忠实模型以及对介导 GABA 信号传导的离子变化的深入了解将允许识别新的治疗靶标并允许对这些靶标进行有效测试以防止 TBI 患者 PTE 的发展。我们将通过两个具体目标来测试我们的中心假设：目标 1：我们将训练之前建立的识别最佳模式或模式组合的算法，以识别这些中的癫痫候选者我们发现我们可以使用现有的算法来预测 PTE。快速分析行为和 ECoG，识别癫痫发作相关性，并与外周血清一起生物标志物，使用现有的增强算法来预测哪些受试者将发展 PTE 目标 2：我们将。确定 GABA 信号传导的离子基础和新皮质网络是否发生慢性变化潜伏期的简易爆炸装置和生物标志物表明了这些猪的活动。发展 PTE 将具有更多与炎症和血液相关的外周血浆生物标志物脑屏障开放，Cl-o和Cl-I变化更大，超同步发展更快本地和全球网络活动（EEG）。该方法的创新之处在于，我们带来了在啮齿动物和动物身上开发的细胞成像技术器官型培养模型和新开发的脑环脑算法作为工具和工具，我们在高科技和大型动物模型领域做出了重大贡献。生物标志物将使 PTE 的环脑模型得到更广泛的应用，PTE 的预测也将开放目前尚不可能了解细胞外基质引起的变化。 GABAergic 信号传导的离子基础以及同一受试者的局部和全局网络变化在癫痫发生过程中可以确定作为目标的癫痫发生机制寻找可能预防 PTE 发展的疗法。