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Establishing translational neuroimaging tools for quantitative assessment of energy metabolism and metabolic reprogramming in healthy and diseased human brain at 7T

Establishing translational neuroimaging tools for quantitative assessment of energy metabolism and metabolic reprogramming in healthy and diseased human brain at 7T
建立转化神经影像工具,用于定量评估 7T 健康和患病人脑的能量代谢和代谢重编程
批准号:
10714863
负责人:
Wei Chen
金额:
$63.02万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-06-01 至 2028-05-31
关键词:
ATP phosphohydrolaseAdenosine TriphosphateAffectAgingAreaBiochemicalBioenergeticsBiomedical ResearchBrainBrain DiseasesBrain NeoplasmsBrain imagingCell RespirationCerebrovascular CirculationCerebrumCitric Acid CycleClinicalClinical ResearchCollaborationsComplexConsumptionCreatine KinaseDeuteriumDiagnosisDiseaseEnergy MetabolismEnergy Metabolism PathwayEngineeringFDA approvedFaceGlucoseGlycolysisGoalsHealthHomeostasisHumanImageImaging DeviceImaging TechniquesLeadershipLifeMagnetic Resonance ImagingMagnetic Resonance SpectroscopyMalignant NeoplasmsMapsMeasurementMeasuresMental disordersMetabolicMetabolic PathwayMethodsMitochondriaMonitorMotorNMR SpectroscopyNeurodegenerative DisordersNeuronsNeurosciences ResearchNicotinamide adenine dinucleotideNoiseNuclearOxidation-ReductionOxidative PhosphorylationOxygenOxygen ConsumptionPathway interactionsPatientsPerformancePhosphorusPhysiologicalPilot ProjectsPlayProcessProductionReactionResearchResearch DesignResolutionResourcesRoleScanningSignal TransductionStrokeTechnologyTestingTimeVisualclinical applicationdetection sensitivityglucose metabolismhuman diseaseimaging capabilitiesimaging detectionimaging facilitiesimaging modalityimprovedinnovationinsightkinetic modelmembermetabolic imagingmetabolic ratenervous system disorderneuroimagingneurophysiologynext generationnon-invasive imagingnovelquantitative imagingradio frequencyresponseskillsspatiotemporalsuccesstemporal measurementtooltranslational applicationstranslational studytreatment effecttumor heterogeneity

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PROJECT SUMMARY Cellular energy metabolism is a fundamental process of life that produces biochemical energy in the form of adenosine triphosphate (ATP) to support neuronal activity and brain function. Glucose and oxygen are the main energy substrates of the brain and are metabolized through glycolysis, the tricarboxylic acid cycle and oxidative phosphorylation pathways, constituting a neuroenergetic network that effectively regulates ATP production and homeostasis. ATP production and homeostasis are affected when brain states change, as signs of altered cerebral glucose and oxidative metabolism are commonly seen in aging, neurodegenerative diseases, psychiatric disorders, stroke and cancer. Despite the important roles of brain energy metabolism, metabolic alteration and reprogramming in health and disease, noninvasive neuroimaging tools capable of mapping and quantifying key features of neuroenergetic network in the human brain are still lacking. Over the past two decades, we have developed three ultrahigh-field (UHF) metabolic imaging techniques based on deuterium-2 (2H), oxygen-17 (17O), and phosphorus-31 (31P) magnetic resonance spectroscopy (MRSI) imaging capable of noninvasive and quantitative assessment of brain energy metabolism along major metabolic pathways. However, X-nuclear MRSI-based methods face severe challenges in translational applications due to low detection sensitivity and metabolite content, and prolonged scanning time. This project aims to develop and integrate multiple cutting-edge technologies to build next generation high- resolution, high-performance and translatable neuroimaging tools on an FDA-approved 7 Tesla clinical scanner for quantitatively imaging key metabolic rates and other essential neurophysiological parameters related to energy metabolism in healthy and diseased human brains. Three pilot studies are proposed to test and demonstrate the utility and feasibility of the novel neuro-metabolic imaging tools to quantitatively study neuroenergetics and metabolic reprogramming in brain activation, aging processes and brain tumors, aiming to understand their critical roles in brain function and disease. This project leverages the interdisciplinary expertise of an outstanding team leading in the research field, excellent imaging facilities and resources, and close collaboration among team members. The advanced neuroimaging tools established by this project is expected to have significant impact on changing the paradigm of neurometabolic imaging and energy metabolism research, and enable translational studies of human brain bioenergetics and metabolic reprogramming under physiopathological conditions.
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An ensemble deep learning model for tumor bud detection and risk stratification in colorectal carcinoma.
  • 批准号:
    10564824
  • 项目类别:
  • 资助金额:
    $54.37万
  • 财政年份:
    2023
  • 负责人:
    Wei Chen
  • 依托单位:
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate
SCH: New Advanced Machine Learning Framework for Mining Heterogeneous Ocular Data to Accelerate
Cellular Interactions in Vascular Calcification of Chronic Kidney Disease
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