Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1078115 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Diagnostic performance and clinical impact of blood metagenomic next-generation sequencing (mNGS) in ICU patients
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Project description:Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. Here, we describe identification of transcriptional mRNA biomarkers able to identify severe systemic inflammation and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study. All patients were recruited in Intensive Care Units (ICUs) from multiple UK hospitals including 59 patients with abdominal sepsis, 84 patients with pulmonary sepsis, 42 SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), at four time points including 30 healthy control donors. Multiple clinical parameters were measured, including SOFA score etc., with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using PBL mRNA microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools. Nineteen select high-performance, differentially-expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed ‘indicators of inflammation’ (IoI), including CD177, FAM20A and OLAH. Combinations of these were trialed. Best-performing minimal panels e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (ROC/AUC>0.99). Twenty select entities were differentially-expressed between sepsis and SIRS (FC>2.0, p-value<0.05), termed ‘SIRS or Sepsis’ (SoS) biomarkers. Panels of biomarkers able to differentiate sepsis from SIRS were also identified and performance assessed using AUCROC. The best performing panel was CMTM5/CETP/PLA2G7/MIA/MPP3 using our dataset (AUCROC=0.9758). The IoI and SoS signatures were evaluated on other independent gene expression datasets, with some reduced performance observed, which maybe in part due to study/platform technical variation.
2024-01-08 | GSE236713 | GEO
Project description:Targeted Next-Generation Sequencing for Clinical Diagnosis of 561 Mendelian Diseases
| PRJNA285944 | ENA
Project description:metagenomic next-generation sequencing of plasma improves the clinical coincidence rate of pathogen diagnosis in patients with suspected sepsis
| PRJEB53323 | ENA
Project description:Neonates manifest a unique host response to sepsis even among other children. Preterm neonates may experience sepsis soon after birth or during often protracted birth hospitalizations as they attain physiologic maturity. We examined the transcriptome using genome-wide expression profiling on prospectively collected peripheral blood samples from infants evaluated for sepsis within 24 hours after clinical presentation. Simultaneous plasma samples were examined for alterations in inflammatory mediators. Group designation (sepsis or uninfected) was determined retrospectively based on clinical exam and laboratory results over the next 72 hours from the time of evaluation. Unsupervised analysis showed the major node of separation between groups was timing of sepsis episode relative to birth (early, <3 days or late, >3 days). Principal component analyses revealed significant differences between patients with early or late sepsis despite the presence of similar key immunologic pathway aberrations in both groups. Unique to neonates, the uninfected state and host response to sepsis is significantly affected by timing relative to birth. Future therapeutic approaches may need to be tailored to the timing of the infectious event based on post-natal age. We used human microarrays to detail the molecular profile of the events that occur following sepsis in hospitalized neonates Please note that 'uninfected chorio' represents babies who were not infected but had chorioamnionitis exposure
2015-07-06 | E-GEOD-69686 | biostudies-arrayexpress
Project description:We used next-generation sequencing (NGS) to sequence and differentially quantitate miRNAs in 10 pools of plasma derived from individuals with sepsis and SIRS.Plasma pools were preferred to individual samples because they decrease the impact of individual outliers on the analysis. Total RNA was then extracted from equal volumes of plasma and technical duplicates of cDNA libraries for Illumina NGS created. Results from 10 pools representative of 89 individuals (including no-SIRS controls) are uploaded to the repository.
2016-12-01 | E-MTAB-4637 | biostudies-arrayexpress
Project description:Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. The aims of this current study were to independently validate our previous biomarker and consolidate gene selection from each of our microarray data set for establishing a targeted assay for the differential diagnosis of melioidosis. Blood samples were collected from patients who presented with severe inflammatory response syndromes from 3 provincial hospitals in Northeast of Thailand during September 2009 and November 2011. Only culture-confirmed sepsis were included in the study (n=166). We generated a new microarray dataset comprising of 29 patients with septicemic melioidosis and 54 patients with sepsis due to other pathogens. Validation of the 37-gene signature using this new dataset demonstrated the prediction accuracy of approximately 80% for detecting type of sepsis. In order to develop a nanoliter-scale high throughput PCR technology, we further identified additional gene signature from this new microarray dataset and by revisiting our published data. Altogether 85 genes including 6 housekeeping genes were selected. Using multi-steps iteration approach we could reduce the number of biomarkers to 12 genes while the performance is comparable to that of the full panel. The high performance (accuracy >70%) of this 12-gene signature could be validated in a second independent set of samples. The 12-gene panel identified by our study provides high performance for the differential diagnosis of septicemic melioidosis. This finding will be useful for improving the management of septicemic melioidosis in term of diagnosis, treatment and follow up. Total RNA from whole blood obtained from patients with sepsis caused by B.pseudomallei (n=29) or other pathogens (n=54) and uninfected controls (28 healthy and 27 subjects with type 2 diabetes mellitus) were collected. In order to validate the published signature, microarray data were generated from these samples. This dataset was also used for an independent selection of signature for septicemic melioidosis. The same RNA samples were used for validation by a high throughput real-time PCR technique, Fluidigm.
2015-06-04 | E-GEOD-69528 | biostudies-arrayexpress