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dbGaP phs001248 Gene Array to Predict Bacterial Infection with in Respiratory Illness - Eligibility

- 12-05-23 - 5 Formulieren, 1 Itemgroep, 10 Data-elementen, 1 Taal

Itemgroep: IG.elig

Principal Investigator: Ann Falsey, MD, University of Rochester, Rochester, NY, USA MeSH: Respiratory Tract Infections https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001248 Accurate tests for microbiologic diagnosis of lower respiratory tract infections (LRTI) are needed. Gene expression profiling of whole blood represents a powerful new approach for analysis of the host response during respiratory infection that can be used to supplement pathogen detection testing. Using qPCR, we prospectively validated the differential expression of 10 genes previously shown to discriminate bacterial and non-bacterial LRTI confirming the utility of this approach. In addition, a novel approach using RNAseq analysis identified 141 genes differentially expressed in LRTI subjects with bacterial infection. Using "pathway-informed" dimension reduction, we identified a novel 11 gene set (selected from lymphocyte, α-linoleic acid metabolism, and IGF regulation pathways) and demonstrated a predictive accuracy for bacterial LRTI (nested CV-AUC=0.87). RNAseq represents a new and an unbiased tool to evaluate host gene expression for the diagnosis of LRTI.

Respiratory Tract Infections

pht006092.v1.p1

1 Itemgroep 4 Data-elementen

pht006093.v1.p1

1 Itemgroep 5 Data-elementen

pht006094.v1.p1

1 Itemgroep 23 Data-elementen

pht006095.v1.p1

1 Itemgroep 5 Data-elementen

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