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

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

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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摘要创伤后癫痫（PTE）影响脑外伤（TBI）的15-40％，导致脑损伤（TBI）即使从TBI初次恢复后，也有明显的发病率。理解的主要局限 PTE的病理生理学缺乏对脑脑大脑中PTE的生物质体建模和识别或预测PTE的生物标志物。我们的长期目标是了解病理生理学导致PTE，以开发停止或抑制导致发病机理进展的疗法创伤后癫痫。总体目的是识别术语中PTE的生物标志物大脑和PTE的离子基础。我们的中心假设是长期离子变化改变了可以通过机器学习来识别导致PTE和PTE生物标志物的GABA能信号传导增强算法。理由是一种自动化方法的工具包，用于量化生物标志物 PTE的生物质模型以及对介导GABA能信号传导的离子变化的见解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。我们将以两个具体的目的测试中心假设：目标1：我们将培训先前建立的识别最佳模式或组合模式的算法，以识别其中的癫痫发作候选者 PTE的模型，甚至可以预测PTE。我们假设我们可以使用现有算法来快速分析行为和ECOG，识别癫痫发作相关，并与周围串行一起生物标志物，使用现有的增强算法来预测哪些受试者将发展为PTE。目标2：我们将确定GABA能信号的离子基础和新皮质网络的慢性变化是否发生变化活动在潜在时期由IED和生物标志物表示。我们假设那些猪开发PTE将具有与炎症和血液相关的周围血浆生物标志物大脑屏障的打开，CL-O和CL-I的更大变化以及超同步的更快发展本地和全球网络活动（EEG）。 IS方法具有创新性，因为我们将细胞成像技术在啮齿动物和有机培养模型和新开发的术语算法与室脑大脑合并高科技和大型动物模型的领域。我们的贡献是工具和生物标志物将能够更广泛地使用PTE的副脑模型和PTE的预测大型研究领域尚不可能。了解细胞外矩阵诱导的变化 GABA能信号传导的离子基础以及同一主题的局部和全球网络变化在癫痫发生过程中，可以鉴定癫痫发生的机制对于可能阻止PTE发展的疗法。