ID
45699
Description
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.
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Versions (1)
- 12/05/2023 12/05/2023 - Simon Heim
Détendeur de droits
Ann Falsey, MD, University of Rochester, Rochester, NY, USA
Téléchargé le
12 mai 2023
DOI
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Licence
Creative Commons BY 4.0
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dbGaP phs001248 Gene Array to Predict Bacterial Infection with in Respiratory Illness
This sample attributes table includes body site where sample was collected, analyte type, tumor status, and histological type.
- StudyEvent: SEV1
- Eligibility Criteria
- The subject consent data table contains subject IDs, consent group information, and subject aliases.
- This subject sample mapping data table includes a mapping of study subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs. The data table also includes sample aliases and sample use.
- This subject phenotype data table includes the subject's age, sex, and race, predicted pathogen and class, the presence of various diseases or symptoms (n=10 variables; COPD, CHF, diabetes, nasal congestion, cough, sputum, dyspnea, rigors, wheezing, and rales), and measurements for body temperature, systolic BP, saO2, CXR infiltrates, WBC counts, band cell percentage, and BUN.
- This sample attributes table includes body site where sample was collected, analyte type, tumor status, and histological type.
Similar models
This sample attributes table includes body site where sample was collected, analyte type, tumor status, and histological type.
- StudyEvent: SEV1
- Eligibility Criteria
- The subject consent data table contains subject IDs, consent group information, and subject aliases.
- This subject sample mapping data table includes a mapping of study subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs. The data table also includes sample aliases and sample use.
- This subject phenotype data table includes the subject's age, sex, and race, predicted pathogen and class, the presence of various diseases or symptoms (n=10 variables; COPD, CHF, diabetes, nasal congestion, cough, sputum, dyspnea, rigors, wheezing, and rales), and measurements for body temperature, systolic BP, saO2, CXR infiltrates, WBC counts, band cell percentage, and BUN.
- This sample attributes table includes body site where sample was collected, analyte type, tumor status, and histological type.
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