ArticleCritical care explorations2021
Machine Learning-Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome.
Article in Critical care explorations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed, 15 citations in OpenAlex.
- Tackling the small imbalanced horizontal dataset regressions by Stability Selection and SMOGN: a case study of ventilation-free days prediction in the pediatric intensive care unit and the importance of PRISM.International journal of medical informatics · 2025Article
- RNA Sequencing Analysis of Monocytes Exposed to Airway Fluid From Children With Pediatric Acute Respiratory Distress Syndrome.Critical care explorations · 2024Article
- Dysfunctional neutrophil type 1 interferon responses in preschool children with recurrent wheezing and IL-4-mediated aeroallergen sensitization.The journal of allergy and clinical immunology. Global · 2024Article
- Functional immunophenotyping of blood neutrophils identifies novel endotypes of viral response in preschool children with recurrent wheezing.The Journal of allergy and clinical immunology · 2023Article
- RNA Sequencing Analysis of CD4Critical care explorations · 2023Article
- Understanding clinical and biological heterogeneity to advance precision medicine in paediatric acute respiratory distress syndrome.The Lancet. Respiratory medicine · 2023Review
- Pathobiology, Severity, and Risk Stratification of Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2023Review
- Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning.Frontiers in pediatrics · 2023Article
- Expression Patterns of Airway Fluid Cytokines From Intubated Children With Pediatric Acute Respiratory Distress Syndrome.Critical care explorations · 2022Article
- Functional immunophenotyping of children with critical status asthmaticus identifies differential gene expression responses in neutrophils exposed to a poly(I:C) stimulus.Scientific reports · 2022Observational
- Article
- Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS.Metabolites · 2022Article
- Article
- Mechanisms and modulation of sepsis-induced immune dysfunction in children.Pediatric research · 2022Review
- Utility of a Cell-Direct Polymerase Chain Reaction-Based Nucleic Acid Lateral Flow Immunoassay for Detection of Bacteria in Peripheral Blood Leukocytes of Suspected Sepsis Cases.Infection and drug resistance · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo 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.
designProspective cohort study.
settingThirty-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.
interventionsNone. MEASUREMENTS AND MAIN
resultsWe 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.
conclusionsWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.