Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Leveraging the pulmonary immune response and microbiome for improved lower respiratory tract infection diagnosis in critically ill children

ABSTRACT: Leveraging the pulmonary immune response and microbiome for improved lower respiratory tract infection diagnosis in critically ill children

PROVIDER: PRJNA875913 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR22847000_1.fastq.gz	Fastqsanger.gz
	SRR22847000_2.fastq.gz	Fastqsanger.gz
	SRR22847100_1.fastq.gz	Fastqsanger.gz
	SRR22847100_2.fastq.gz	Fastqsanger.gz
	SRR22847200_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 586

Similar Datasets

Leveraging the pulmonary immune response and microbiome for improved lower respiratory tract infection diagnosis in critically ill children

Project description:BACKGROUND. Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging since non-infectious respiratory illnesses appear clinically similar and existing microbiologic tests are often falsely negative or detect incidentally-carried microbes common in children. These challenges result in antimicrobial overuse and adverse patient outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and precision treatment remains unclear. METHODS. We used tracheal aspirate RNA-sequencing to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a random forest gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n=117) or of non-infectious respiratory failure (n=50). We then developed a classifier that integrates the: i) host LRTI probability, ii) abundance of respiratory viruses, and iii) dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm. RESULTS. The host classifier achieved a median AUC of 0.967 by 5-fold cross-validation, driven by activation markers of T cells, alveolar macrophages and the interferon response. The integrated classifier achieved a median AUC of 0.986 and significantly increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n=94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those. CONCLUSIONS. Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.

2022-12-06 | GSE212532 | GEO

Lower respiratory tract microbiome in criticall illness

Project description:Lower respiratory tract microbiome in critically ill patients

| PRJEB64676 | ENA

Microarray Analysis of MicroRNA Expression in Peripheral Blood Mononuclear Cells of critically ill Patients with Influenza A (H1N1)

Project description:To determine whether differential expression of cellular microRNAs plays a role in the host response to Influenza A (H1N1) infection, we have employed the Agilent miRNA microarray (V3) as a discovery platform to identify microRNAs between the critically ill Patients with Influenza A (H1N1) and the healthy controls. Five critically ill patients with a diagnosis of 2009 Inflluenza A (H1N1) and three healthy controls were included in the study. The Peripheral Blood Mononuclear Cells (PBMCs) were isolated and total RNA was extracted respectively.

2012-12-28 | E-GEOD-24956 | biostudies-arrayexpress

Transcriptomic clustering of critically-ill COVID-19 patients

Project description:Transcriptomic clustering of critically-ill COVID-19 patients

| PRJNA809558 | ENA

Transcriptional Profiling of Leukocytes in Critically Ill COVID19 Patients

Project description:Transcriptional Profiling of Leukocytes in Critically Ill COVID19 Patients

| PRJNA648120 | ENA

Expression profiling of critically ill influenza and bacterial pneumonia patients, also influenza vaccination recipients

Project description:Longitudinal Gene expression profiling of whole blood from critically ill influenza and bacterial pneumonia patients. In addition before vs 7 days post influenza vaccination volunteer samples are assayed.

2011-06-30 | GSE20346 | GEO

Clinical significance of interferon signal based on mRNA-microRNA integration and plasma protein analyses in the critically ill patients with COVID19

Project description:It is known that about 60% of all human messenger RNAs (mRNAs) regulated by microRNAs, the role of mRNAs and microRNAs in the critically ill patients with Coronavirus Infection 2019 (COVID-19) is unknown. To evaluate mRNA and microRNA in whole blood of the critically ill patients with COVID-19 and to elucidate the pathogenesis of COVID-19 including the subsequent proteins profile following mRNA and microRNA integration analysis. RNA was extracted from the whole blood in 5 healthy controls and 10 critically ill patients with COVID-19 at the time of admission. mRNA and miRNA were measured by RNA sequence, and gene expression variation and pathway analysis were performed. As the IFNs proteins profile cohort, IFN-α2, IFN-β, IFN-γ, IL-27 and IFN-λ1 were measured on the day of admission (day 1, 181 critical and 22 non-critical patients) and day 6-8 (168 critical patients) in COVID19 patients and 19 healthy controls. Compared to healthy controls, 3488 mRNA and 31 miRNA genes were identified in the differentially expressed genes in the critically ill patients with COVID-19 (p-value<0.05, Log 2 fold change> |2|). In the canonical pathway analysis using Ingenuity Pathway Analysis (IPA), interferon signaling pathway was the most activated. In plasma interferon levels, IFN-β was elevated along with the increase of severity compared to healthy controls. IFN-λ1 was elevated in moderate disease compared to healthy controls, and conversely, IFN-λ1 was lower in severe disease than in moderate disease. Integration of mRNA and microRNA analysis showed activated interferon signaling. The plasma interferon proteins profile revealed that IFN-β (type I) and IFN-λ1 (type III) played an important role in the disease progression of COVID-19.

2022-06-22 | GSE182152 | GEO

Reanalysis of peripheral blood gene expression of IFIH1 rs1990760 variants in critically-ill COVID-19 patients [third-party re-analysis of GSE168400]

Project description:Peripheral blood gene expression analysis of IFIH1 rs1990760 variants in critically-ill COVID-19 patients

2024-05-22 | GSE233626 | GEO

Feasibility of Ultra-Rapid Exome Sequencing in Critically Ill Infants and Children With Suspected Monogenic Conditions in the Australian Public Health Care System

Project description:Critically ill infants and children with suspected monogenic conditions underwent ultra-rapid whole exome genetic testing. A molecular diagnosis was established in 51% of the patients. This study suggests feasibility of ultra-rapid genomic testing in critically ill pediatric patients.

| EGAS00001005430 | EGA

Whole transcriptome analysis of critically ill elderly patients with sepsis

Project description:Whole transcriptome analysis of critically ill elderly patients with sepsis

| PRJNA896158 | ENA