Trial reportPloS one2019
Distinct transcriptional modules in the peripheral blood mononuclear cells response to human respiratory syncytial virus or to human rhinovirus in hospitalized infants with bronchiolitis.
Trial report in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Rhinovirus infection and co-infection in children with severe acute respiratory infection during the COVID-19 pandemic period.Virulence · 2024Article
- Blood leukocyte transcriptional modules and differentially expressed genes associated with disease severity and age in COVID-19 patients.Scientific reports · 2023Article
- Article
- Article
- Elucidation of the Host Bronchial Lymph Node miRNA Transcriptome Response to Bovine Respiratory Syncytial Virus.Frontiers in genetics · 2021Article
- Weighted Gene Co-expression Network Analysis of Key Biomarkers Associated With Bronchopulmonary Dysplasia.Frontiers in genetics · 2020Article
- Human Leukocyte Transcriptional Response to SARS-CoV-2 Infection.Clinics (Sao Paulo, Brazil) · 2020Article
- A curated transcriptome dataset collection to investigate the blood transcriptional response to viral respiratory tract infection and vaccination.F1000Research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human respiratory syncytial virus (HRSV) is the main cause of bronchiolitis during the first year of life, when infections by other viruses, such as rhinovirus, also occur and are clinically indistinguishable from those caused by HRSV. In hospitalized infants with bronchiolitis, the analysis of gene expression profiles from peripheral blood mononuclear cells (PBMC) may be useful for the rapid identification of etiological factors, as well as for developing diagnostic tests, and elucidating pathogenic mechanisms triggered by different viral agents. In this study we conducted a comparative global gene expression analysis of PBMC obtained from two groups of infants with acute viral bronchiolitis who were infected by HRSV (HRSV group) or by HRV (HRV group). We employed a weighted gene co-expression network analysis (WGCNA) which allows the identification of transcriptional modules and their correlations with HRSV or HRV groups. This approach permitted the identification of distinct transcription modules for the HRSV and HRV groups. According to these data, the immune response to HRSV infection-comparatively to HRV infection-was more associated to the activation of the interferon gamma signaling pathways and less related to neutrophil activation mechanisms. Moreover, we also identified host-response molecular markers that could be used for etiopathogenic diagnosis. These results may contribute to the development of new tests for respiratory virus identification. The finding that distinct transcriptional profiles are associated to specific host responses to HRSV or to HRV may also contribute to the elucidation of the pathogenic mechanisms triggered by different respiratory viruses, paving the way for new therapeutic strategies.
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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.