Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA299647 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:A microarray analysis involving whole blood samples isolated from critically ill patients in the medical intensive care unit at Brigham and Women's Hospital. Four groups of intubated subjects undergoing mechanical ventilation were recruited for the study: those with sepsis alone (Sepsis), those with sepsis + ARDS (se/ARDS), those with SIRS (SIRS), and those whithout sepsis, SIRS, or ARDS (untreated). Blood was obtained from patients on the day of admission (day 0) and 7 days later. RNA was isolated from the whole blood samples and microarrays were prepared to determine differential gene expression between the four groups. Total RNA obtained from whole blood samples of critically ill patients
2012-07-10 | E-GEOD-32707 | biostudies-arrayexpress
Project description:Normal children, children with SIRS, children with sepsis, and children with septic shock. Objectives: To advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum. Experiment Overall Design: Prospective observational study involving microarray-based bioinformatics.
2009-01-05 | E-GEOD-13904 | biostudies-arrayexpress
Project description:Normal children, children with SIRS, children with sepsis, and children with septic shock. Objectives: To advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum. Keywords: Normal vs diseased
2008-12-12 | GSE13904 | GEO
Project description:A microarray analysis involving whole blood samples isolated from critically ill patients in the medical intensive care unit at Brigham and Women's Hospital. Four groups of intubated subjects undergoing mechanical ventilation were recruited for the study: those with sepsis alone (Sepsis), those with sepsis + ARDS (se/ARDS), those with SIRS (SIRS), and those whithout sepsis, SIRS, or ARDS (untreated). Blood was obtained from patients on the day of admission (day 0) and 7 days later. RNA was isolated from the whole blood samples and microarrays were prepared to determine differential gene expression between the four groups.
2012-07-11 | GSE32707 | GEO
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:Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset.
2021-07-12 | GSE168442 | GEO
Project description:Sepsis is a time-sensitive condition associated with significant mortality, morbidity, and healthcare costs, especially when the diagnosis is delayed. Clinicians often fail to accurately differentiate between sepsis and a sterile systemic inflammatory response syndrome (SIRS) among patients who incur sterile tissue damage from major surgery. Sepsis is driven by a dysregulated host response to pathogens; SIRS is driven by tissue damage. Transcriptomic profiling of whole blood or of specific cellular components of blood have been utilized for discovering underlying etiological differences between sepsis and uninfected SIRS. Blood-based gene microarrays have demonstrated efficacy in differentiating sepsis from SIRS. Urine is often collected from critically ill patients as standard clinical care, but the diagnostic utility of urine sepsis biomarkers is unknown. In this study we used single-center prospective cohorts of SIRS and sepsis patients, we tested the hypothesis that machine learning feature selection from whole genome transcriptomic urinary RNA signatures can identify gene expression patterns that differentiate between sepsis and sterile SIRS within twelve hours of sepsis onset.
2021-07-12 | GSE168440 | GEO
Project description:Expression data from CD8+ T cells and CD68+ monocytes from patients with hemophagocytic lymphohistiocytosis, sepsis, and persistent systemic inflammatory response syndrome Hemophagocytic lymphohistiocytosis (HLH) is a syndrome characterized by pathologic immune activation in which prompt recognition and initiation of immune suppression is essential for survival. Children with HLH have many overlapping clinical features with critically ill children with sepsis and persistent systemic inflammatory response syndrome (SIRS) in whom alternative therapies are indicated. To determine if plasma biomarkers could differentiate HLH from other inflammatory conditions and to better define a ‘core inflammatory signature’ of HLH, concentrations of inflammatory plasma proteins were compared in 40 patients with HLH to 47 pediatric patients with severe sepsis or SIRS. Seventeen of 135 analytes were significantly different in HLH plasma compared to SIRS/sepsis, including increased interferon-gamma (IFNg)-regulated chemokines CXCL9, CXCL10 and CXCL11. Further, a 5-analyte plasma protein classifier including these chemokines was able to differentiate HLH from SIRS/sepsis. Gene expression in CD8+ T cells and CD68+ monocytes from blood were also enriched for IFNg pathway signatures in peripheral blood cells from patients with HLH compared to SIRS/sepsis. This study identifies differential expression of inflammatory proteins as a diagnostic strategy to identify critically ill children with HLH. Further, comprehensive unbiased analysis of inflammatory plasma proteins and global gene expression demonstrates that IFNg signaling is uniquely elevated in HLH. In addition to demonstrating the ability of diagnostic criteria for HLH, sepsis and SIRS to identify groups with distinct inflammatory patterns, results from this study support the potential for prospective evaluation of inflammatory biomarkers to aid in diagnosis of and optimizing therapeutic strategies for children with distinctive hyperinflammatory syndromes.
2021-05-16 | GSE150707 | GEO
Project description:Critically ill patients with either gram-positive sepsis or gram-negative sepsis
| PRJNA98813 | ENA
Project description:<p>Sepsis, defined as life-threatening organ dysfunction caused by infection is difficult to distinguish clinically from infection or post-operative inflammation. We hypothesized that in a heterogeneous group of critically ill children, there would be different metabolic profiles between post-operative inflammation, bacterial and viral infection and infection with or without organ dysfunction. 1D 1H nuclear magnetic resonance spectra were acquired in plasma samples from critically ill children. We included children with bacterial (n = 25) and viral infection (n = 30) and controls (n = 58) (elective cardiac surgery without infection). Principal component analysis was used for data exploration and partial least squares discriminant analysis models for the differences between groups. Area under receiver operating characteristic curve (AUC) values were used to evaluate the models. Univariate analysis demonstrated differences between controls and bacterial and viral infection. There was excellent discrimination between bacterial and control (AUC = 0.94), and viral and control (AUC = 0.83), with slightly more modest discrimination between bacterial and viral (AUC = 0.78). There was modest discrimination (AUC = 0.73) between sepsis with organ dysfunction and infection with no organ dysfunction. In critically ill children, NMR metabolomics differentiates well between those with a post-operative inflammation but no infection, and those with infection (bacterial and viral), and between sepsis and infection.</p>
2020-11-19 | MTBLS563 | MetaboLights