Author: Grunwell, Jocelyn R.; Rad, Milad G.; Stephenson, Susan T.; Mohammad, Ahmad F.; Opolka, Cydney; Fitzpatrick, Anne M.; Kamaleswaran, Rishikesan
Title: Machine Learning–Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome Cord-id: d4ht9dv0 Document date: 2021_6_15
ID: d4ht9dv0
Snippet: OBJECTIVES: To identify differentially expressed genes and networks from the airway cells within 72 hours of intubation of children with and without pediatric acute respiratory distress syndrome. To test the use of a neutrophil transcription reporter assay to identify immunogenic responses to airway fluid from children with and without pediatric acute respiratory distress syndrome. DESIGN: Prospective cohort study. SETTING: Thirty-six bed academic PICU. PATIENTS: Fifty-four immunocompetent child
Document: OBJECTIVES: To identify differentially expressed genes and networks from the airway cells within 72 hours of intubation of children with and without pediatric acute respiratory distress syndrome. To test the use of a neutrophil transcription reporter assay to identify immunogenic responses to airway fluid from children with and without pediatric acute respiratory distress syndrome. DESIGN: Prospective cohort study. SETTING: Thirty-six bed academic PICU. PATIENTS: Fifty-four immunocompetent children, 28 with pediatric acute respiratory distress syndrome, who were between 2 days to 18 years old within 72 hours of intubation for acute hypoxemic respiratory failure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We applied machine learning methods to a Nanostring transcriptomics on primary airway cells and a neutrophil reporter assay to discover gene networks differentiating pediatric acute respiratory distress syndrome from no pediatric acute respiratory distress syndrome. An analysis of moderate or severe pediatric acute respiratory distress syndrome versus no or mild pediatric acute respiratory distress syndrome was performed. Pathway network visualization was used to map pathways from 62 genes selected by ElasticNet associated with pediatric acute respiratory distress syndrome. The Janus kinase/signal transducer and activator of transcription pathway emerged. Support vector machine performed best for the primary airway cells and the neutrophil reporter assay using a leave-one-out cross-validation with an area under the operating curve and 95% CI of 0.75 (0.63–0.87) and 0.80 (0.70–1.0), respectively. CONCLUSIONS: We identified gene networks important to the pediatric acute respiratory distress syndrome airway immune response using semitargeted transcriptomics from primary airway cells and a neutrophil reporter assay. These pathways will drive mechanistic investigations into pediatric acute respiratory distress syndrome. Further studies are needed to validate our findings and to test our models.
Search related documents:
Co phrase search for related documents- absence presence and academic hospital: 1
- absence presence and acid inducible: 1
- absence presence and acid inducible gene: 1
- absence presence and acid inducible gene retinoic: 1
- absence presence and activator transducer: 1
- absence presence and acute ards respiratory distress syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
- absence presence and acute lung injury: 1, 2, 3
- absence presence and adenosine triphosphate: 1
- absence presence and loss function: 1, 2
- absence presence and lung disease: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
- absence presence and lung edema: 1, 2
- absence presence and lung inflammation: 1, 2, 3, 4, 5, 6, 7, 8, 9
- absence presence and lung injury: 1, 2, 3, 4
- absence presence and lupus erythematosus: 1
- absence presence and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
- absence presence and machine learning model: 1, 2, 3, 4
- abundant rna and loss function: 1
- abundant rna and machine learning: 1
- abundant rna and machine learning model: 1
Co phrase search for related documents, hyperlinks ordered by date