Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases
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PROVIDER: PRJNA454300 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:In this study, whole blood samples were used to determine the gene expression of febrile culture confirmed enteric fever cases (ST = S. Typhi; SPT = S. Paratyphi), febrile culture negative individuals presenting to hospital in Kathamandu (sEF = suspected enteric fever), and healthy community controls (CTRL).
2019-09-02 | GSE113866 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2019-09-02 | GSE113867 | GEO
Project description: Not available
| S-SCDT-EMM-2019-10431 | biostudies-other
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| S-EPMC6783646 | biostudies-literature
Project description:ABSTRACT Background. Acute Kawasaki disease (KD) is difficult to distinguish from other acute rash/fever illnesses, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed. Methods. We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with three illnesses that resemble KD. Results. Genes associated with platelet and neutrophil activation were expressed at higher levels in KD patients than in patients with acute adenovirus infections or systemic adverse drug reactions but not in patients with scarlet fever; genes associated with B cell activation were also expressed at higher levels in KD patients than in controls. A striking absence of interferon-stimulated gene expression in the KD patients was confirmed in an independent cohort of KD subjects. We successfully predicted the diagnosis in 21 of 23 KD patients and 7 of 8 adenovirus patients using a set of 38 gene transcripts. Conclusions. These findings provide insight into the molecular features that distinguish KD from other febrile illnesses, and support the feasibility of developing novel diagnostic reagents for KD based on the host response. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: One of Kawasaki Disease (KD) or control (C) of Scarlet fever (C-sf), adenovirus infection (C-ai) or drug reaction (C-dr) disease_state_design
2010-06-24 | E-GEOD-15297 | biostudies-arrayexpress
Project description:ABSTRACT Background. Acute Kawasaki disease (KD) is difficult to distinguish from other acute rash/fever illnesses, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed. Methods. We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with three illnesses that resemble KD. Results. Genes associated with platelet and neutrophil activation were expressed at higher levels in KD patients than in patients with acute adenovirus infections or systemic adverse drug reactions but not in patients with scarlet fever; genes associated with B cell activation were also expressed at higher levels in KD patients than in controls. A striking absence of interferon-stimulated gene expression in the KD patients was confirmed in an independent cohort of KD subjects. We successfully predicted the diagnosis in 21 of 23 KD patients and 7 of 8 adenovirus patients using a set of 38 gene transcripts. Conclusions. These findings provide insight into the molecular features that distinguish KD from other febrile illnesses, and support the feasibility of developing novel diagnostic reagents for KD based on the host response. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: One of Kawasaki Disease (KD) or control (C) of Scarlet fever (C-sf), adenovirus infection (C-ai) or drug reaction (C-dr)
2009-08-12 | GSE15297 | GEO
Project description:Background: Salmonella Typhi and Salmonella Paratyphi A are the agents of enteric (typhoid) fever; both can establish chronic carriage in the gallbladder. Chronic Salmonella carriers are typically asymptomatic, intermittently shedding bacteria in the feces, and contributing to disease transmission. Detecting chronic carriers is of public health relevance in areas where enteric fever is endemic, but there are no routinely used methods for prospectively identifying those carrying Salmonella in their gallbladder. Methodology/Principal Findings: Here we aimed to identify biomarkers of Salmonella carriage using metabolite profiling. We performed metabolite profiling on plasma from Nepali patients undergoing cholecystectomy with confirmed S. Typhi or S. Paratyphi A gallbladder carriage (and non-carriage controls) using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS) and supervised pattern recognition modeling. We were able to significantly discriminate Salmonella carriage samples from non-carriage control samples. We were also able to detect differential signatures between S. Typhi and S. Paratyphi A carriers. We additionally compared carriage metabolite profiles with profiles generated during acute infection; these data revealed substantial heterogeneity between metabolites associated with acute enteric fever and chronic carriage. Lastly, we found that Salmonella carriers could be significantly distinguished from non-carriage controls using only five metabolites, indicating the potential of these metabolites as diagnostic markers for detecting chronic Salmonella carriers. Conclusions/Significance: Our novel approach has highlighted the potential of using metabolomics to search for diagnostic markers of chronic Salmonella carriage. We suggest further epidemiological investigations of these potential biomarkers in alternative endemic enteric fever settings.
2018-01-17 | MTBLS579 | MetaboLights
Project description:Whole blood transcriptional profiles of patients with (1) active pulmonary ['AdjuVIT active TB' and 'New active pulmonary'] and (2) extrapulmonary TB ['New active-extrapulmonary'] at time of diagnosis, (3) long-term recovery after treatment for active pulmonary TB ['AdjuVIT active TB'], (4) febrile illnesses presenting to hospital ['Fever mixed infection'] and (5) febrile pneumonia ['Fever pneumonia'] before antibiotic treatment, and (6) healthy vounteers. Each array sample represents a separate individual in each group. This submission includes two human whole genome Agilent Array Designs: A-MEXP-2104 and A-AGIL-28. Each of the individual raw array files are included as well as a single processed file representing the data matrix of all the merged and normalised data for the probes that are shared by the two array designs.
2016-10-28 | E-MTAB-4257 | biostudies-arrayexpress
Project description:Recent progress in unbiased metagenomic next-generation sequencing (mNGS) allows simultaneous examination of microbial and host genetic material in a single test. Leveraging affordable bronchoalveolar lavage fluid (BALF) mNGS data, we employed machine learning to create a diagnostic approach distinguishing lung cancer from pulmonary infections, conditions prone to misdiagnosis in clinical settings. This prospective study analyzed BALF-mNGS data from lung cancer and pulmonary infection patients, delineating differences in DNA/RNA microbial composition, bacteriophage abundances, and host responses, including gene expression, transposable element levels, immune cell composition, and tumor fraction derived from copy number variation (CNV). Integrating these metrics into a host/microbe metagenomics-driven machine learning model (Model VI) demonstrated robustness, achieving an AUC of 0.87 (95% CI = 0.857-0.883), sensitivity = 73.8%, and specificity = 84.5% in the training cohort, and an AUC of 0.831 (95% CI = 0.819-0.843), sensitivity = 67.1%, and specificity = 94.4% in the validation cohort for distinguishing lung cancer from pulmonary infections. The application of a rule-in and rule-out strategy-based composite predictive model significantly enhances accuracy (ACC) in distinguishing between lung cancer and tuberculosis (ACC=0.913), fungal infection (ACC=0.955), and bacterial infection (ACC=0.836). These findings highlight the potential of cost-effective mNGS-based analysis as a valuable tool for early differentiation between lung cancer and pulmonary infections, offering significant benefits through a single comprehensive testing.
2024-01-08 | GSE252118 | GEO
Project description:Background: We report the detailed development of biomarkers to predict the clinical outcome under dengue infection. Transcriptional signatures from purified peripheral blood mononuclear cells were derived from whole-genome gene-expression microarray data and validated by quantitative PCR and tested in independent samples. Methodology/Principal Findings: The study was performed on patients of a well-characterized dengue cohort from Recife, Brazil. The samples analyzed were collected prospectively from acute febrile dengue patients who evolved with different degrees of disease severity, classic dengue fever or dengue hemorrhagic fever (DHF) and compared with similar samples from other non-dengue febrile illnesses. The DHF samples were collected 2-3 days before the presentation of the plasma leakage symptoms. Differentially-expressed genes were selected by univariate statistical tests as well as multivariate classification techniques. The results showed that at early stages of dengue infection, the genes involved in effector mechanisms of innate immune response presented a weaker activation on patients who later developed hemorrhagic fever, whereas the genes involved in apoptosis were expressed in higher levels. Conclusions/Significance: Some of the gene expression signatures displayed estimated accuracy rates of more than 95%, indicating that expression profiling with these signatures may provide a useful means of DHF prognosis at early stages of infection The samples correspond to blood samples from 26 patients from a cohort in Brazil, divided into three classes, DHF, DF, ND, as follows: 18 were confirmed dengue 3, genotype III cases, among which 10 were diagnosed as dengue hemorrhagic fever (DHF) and 8 as classical dengue fever (DF), and 8 control samples (ND) from febrile patients confirmed to be not infected with dengue. None of the DHF patients presented vasculopathy signs and symptoms at the time the samples used in the functional genomic characterization were collected. At the time of collection the patients referred approximately 5 days of disease and the absence of fever was reported two to three days after enrollment. The samples from the DF, DHF and ND patients were matched to avoid significant differences regarding patient age, gender, dengue infection history and days of symptoms among the groups.
2010-09-01 | E-GEOD-18090 | biostudies-arrayexpress