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Molecular profiling of immune dysregulation in acute exacerbations: from peanut allergy to COVID-19

Molecular profiling of immune dysregulation in acute exacerbations: from peanut allergy to COVID-19
急性加重时免疫失调的分子分析:从花生过敏到 COVID-19
批准号:
2784650
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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英文摘要
An effective immune response is essential to health and well-being but, for reasons that are not well understood, they can become dysregulated causing numerous inflammatory conditions including allergies and asthma as well as life-threatening systemic inflammatory syndromes observed in response to infections which can result in sepsis, including the current pandemic SARS-CoV-2. Recognition of acute dysregulated immune responses is essential to ensure prompt and appropriate treatment - yet much of clinical decision making relies on subjective signs and symptoms linked with certain laboratory findings. An alternative approach is to develop molecular phenotyping tools using mass spectrometry to provide detailed molecular metabolic and proteomic profiles. The resulting rich data sets can also support development of diagnostic biomarkers as well as providing insights into disease mechanisms which can support more effective therapeutic and management strategies. Capitalising on two contrasting populations experiencing a dysregulated immune responses resulting from a food allergic reaction and individuals experiencing severe immune disruption during a COVID-19 infection. Working with the industrial partner, Waters Corporation, the plasma proteome metabolome of the two groups will be comprehensively characterised using state-of-the art ion mobility mass spectrometry which provides data that allows the 3D structural characterisation of molecules. Bioinformatic analysis will be used to identify proteins and metabolites in the data sets and apply techniques such as pathway analysis. The resulting high dimensional data sets will also be used to identify proteomic and metabolomic markers of clinical phenotypes using machine learning methods and Bayesian inference to support rapid clinical decision making and identify disease pathways which will help support identification of new treatments and management strategies.
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柴胡类生药鉴定与质量评价的二元条形码系统的研究
  • 批准号:
    30873387
  • 项目类别:
    面上项目
  • 资助金额:
    32.0万元
  • 批准年份:
    2008
  • 负责人:
    晁志
  • 依托单位: