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- Klinische proef NCT02164786
Metabolomics Dynamics Study for Severe Patient
Modeling Metabolomic Dynamics Based on Nuclear Magnetic Resonance and High Performance Liquid Chromatography for Severe Patient: a Cohort Study
Acute severe disease is a major public health challenge that often affects young adults.In past decade, there are lot of new techniques have been developed that aim to improve the outcome of acute severe disease, But few of these works success. According to recently studies, the mortality of the multiple organ dysfunction syndrome(MODS) that is the major cause of death in patients who suffering from acute severe disease, is not improved. On the contrary, if MODS be predicted in early stage of acute severe disease, the death can be prevented. Because acute severe disease poses complex injury that involves multiple pathological processes, understanding the cellular and metabolic network malfunction during acute severe disease is crucial for clinical monitoring and intervention.
Human metabolism is a complex network with hundreds of cross-linked paths. During critical illness, the metabolic network is dynamically disturbed at multiple points. Classical research typically isolates a small part of this network to investigate the impact of pathological physiology molecular mechanisms on clinical outcome. In particular, researchers have examined metabolic disturbances such as cytokine network dysfunction, skeletal muscle breakdown, insulin resistance, dyslipidemia, testosterone and growth hormone/Insulin like growth factor (IGF)dysfunctions, low thyroxine syndrome, and deficiency of vitamin D and calcium with secondary hyperparathyroidism. These complex metabolic disturbances appear and interact at different stages during the pathological process after acute severe illness. Therefore, an integrated approach that combines the biochemical/molecular changes with network disturbances is the key to understanding acute severe illness at the systems biology level and establishing an accurate quantitative model for clinical monitoring.
An interdisciplinary method that includes high-throughput quantitative techniques and effective mathematical and visualization tools is necessary. Furthermore, interdisciplinary methods present the opportunity to develop innovative clinical diagnosis and monitoring methods for severe injuries. The aim of this study is to provide a novel high-throughput method that integrated proton-nuclear magnetic resonance (NMR) metabolomic fingerprinting and High Performance Liquid Chromatography with advance mathematics tools to modeling metabolic dynamics after acute severe disease.
Studie Overzicht
Toestand
Studietype
Inschrijving (Verwacht)
Contacten en locaties
Studie Locaties
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Sichuan
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Chengdu, Sichuan, China, 610072
- Werving
- Sichuan Academy of Medical Sciences
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Contact:
- Hua Jiang, MBBS,PhD
- Telefoonnummer: 8613980001701
- E-mail: cdjianghua@gmail.com
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Contact:
- Bin Cai, MBBS
- Telefoonnummer: 86-18981838125
- E-mail: bin.cai@traumabank.org
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Deelname Criteria
Geschiktheidscriteria
Leeftijden die in aanmerking komen voor studie
Accepteert gezonde vrijwilligers
Geslachten die in aanmerking komen voor studie
Bemonsteringsmethode
Studie Bevolking
Beschrijving
Inclusion Criteria:
- Age:18-70 years
- Acute Physiology And Chronic Health Evaluation(APACHE)II>10
Exclusion Criteria:
- With comorbidity (Diabetes,Hyperthyroidism or primary organ dysfunction )
- Pregnancy
Studie plan
Hoe is de studie opgezet?
Ontwerpdetails
Cohorten en interventies
Groep / Cohort |
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Acute severe disease
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Wat meet het onderzoek?
Primaire uitkomstmaten
Uitkomstmaat |
Tijdsspanne |
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Sterfte bij ziekenhuisopname
Tijdsspanne: Overlijdensgebeurtenissen van opname tot ontslag (tot 10 weken)
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Overlijdensgebeurtenissen van opname tot ontslag (tot 10 weken)
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Secundaire uitkomstmaten
Uitkomstmaat |
Tijdsspanne |
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Multi Organ Dysfunction Syndrome(MODS)
Tijdsspanne: MODS events occurence from admission to discharge(up to 10 weeks)
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MODS events occurence from admission to discharge(up to 10 weeks)
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Medewerkers en onderzoekers
Onderzoekers
- Studie stoel: Hua Jiang, PhD, MBBS, Sichuan Academy of Medical Sciences
Studie record data
Bestudeer belangrijke data
Studie start
Primaire voltooiing (Verwacht)
Studie voltooiing (Verwacht)
Studieregistratiedata
Eerst ingediend
Eerst ingediend dat voldeed aan de QC-criteria
Eerst geplaatst (Schatting)
Updates van studierecords
Laatste update geplaatst (Werkelijk)
Laatste update ingediend die voldeed aan QC-criteria
Laatst geverifieerd
Meer informatie
Termen gerelateerd aan deze studie
Trefwoorden
Andere studie-ID-nummers
- MetaLab_2014_001
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