Mathematical Modeling of Influenza Severity in Outbred Mice
Mathematical Modeling of Influenza Severity in Outbred Mice
批准号:
10308106
负责人:
John F Alcorn
金额:
$18.98万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-11-25 至 2023-10-31
关键词:
AddressAdolescentAdultAgeAlgorithmsAnimalsBioinformaticsBiologicalBiological MarkersBloodCause of DeathCenters for Disease Control and Prevention (U.S.)Cessation of lifeCharacteristicsChildChildhoodClinicalComplexComputer ModelsCytokine ReceptorsDataDiseaseExposure toFlow CytometryFutureGenderGoalsHealthcare SystemsHistologyHospitalizationHumanImmuneImmune responseImmune systemImmunityImmunologicsInfectionInflammation MediatorsInflammatoryInfluenzaInfluenza A Virus, H1N1 SubtypeIntegration Host FactorsInterferonsInterventionKnowledgeLaboratoriesLifeLungLung immune responseMachine LearningMediatingMethodsModelingMolecularMolecular ProfilingMolecular TargetMorbidity - disease rateMusOutcomePathogenesisPathologicPathway interactionsPatient CarePatientsPattern recognition receptorPersonsPhenotypePhysiologyPilot ProjectsPneumoniaPopulationPositioning AttributeProcessProteinsPublishingReportingResearch DesignSeasonsSerumSeveritiesSignal TransductionSupportive careTestingUnited StatesVaccinesViral Load resultVirusage groupbasebiomarker selectioncandidate markerclinical decision-makingcohortcostcytokinedisease phenotypehigh riskimmunopathologyinfection rateinfluenza infectioninfluenza pneumoniajuvenile animallung injurymathematical methodsmathematical modelmortalitymouse modelnovelnovel markerpandemic diseasepandemic influenzapathogenpotential biomarkerpredictive markerpredictive modelingpulmonary functionresponsetranscriptometranscriptomicstranslational studyvaccine access
中文摘要
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英文摘要
PROJECT SUMMARY
In the United States, pulmonary influenza infection occurs annually in 5-20% of the population with mortality in
the range of 30,000 deaths. The recent 2009 influenza H1N1 pandemic illustrated the potential for higher
infection rates, which were reported to be as high as 45% in certain age groups. The 2017-18 influenza season
had the highest pediatric mortalities since the 2009 pandemic. Influenza infection is known to result in a broad
spectrum of disease phenotypes in humans, although severe pneumonia is relatively rare. Despite this, severe
disease often requires advanced supportive care in the young, including previously healthy children. Host
factors involved in determining the outcome of influenza infection are unclear and children are known to be at
higher risk of severe disease. First life exposure to influenza is also thought to dictate life-long immunity. Little
is known about the effects of young age and gender on influenza responses and severity. This underscores the
importance of understanding influenza pathogenesis in a pediatric population. Influenza pathogenesis is likely
mediated in large part by exuberant inflammatory host responses in the lung. It is likely that predictive soluble
inflammatory mediators are present in severe infection. Further, predictive biomarkers or mathematical models
of influenza pneumonia severity would enhance clinical decision making and patient care. We propose that
machine learning and mathematical modeling of host immune endpoints will define a molecular
fingerprint of severe influenza pneumonia in juveniles. This hypothesis will be tested in two Aims. Aim 1
will focus on characteristic molecular pathways related to influenza severity in juvenile animals, using outbred
mice. We will utilize machine learning and new mathematical approaches for pathway and biomarker selection.
Aim 2 will test mathematical models of influenza pathogenesis to elucidate new mechanisms that drive lung
injury. The overall goal of the proposed study is to identify novel biomarkers and mechanistic models of
influenza pneumonia severity that can be applied to children. To accomplish this we will use a broad,
exploratory, and unbiased approach. Candidate biomarkers and pathways would then be evaluated in future
mechanistic and translational studies in mice and humans.
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