Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections.

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE277851 | GEO | 2024/09/28

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [DiscoveryCohort]

Project description:Host gene expression responses can be used to determine the etiology of acute infection. PBMCs were stimulated with bacterial, viral, and fungal stimuli and then analyzed for differential gene expression utilizing microarrays to derive pathogen class-specific gene expression classifiers of infection. Discovery Cohort: In vitro human PBMC challenges were performed with bacteria (gram postive and gram negative), viruses (4 strains of influenza), and fungi (Cryptococcus and Candida)

2024-09-28 | GSE277848 | GEO

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [ValidationCohort]

Project description:Host gene expression responses can be used to determine the etiology of acute infection. PBMCs were stimulated with bacterial, viral, and fungal stimuli and then analyzed for differential gene expression utilizing microarrays to derive pathogen class-specific gene expression classifiers of infection. Validation Cohort: peripheral blood samples of human patients with acute viral, bacterial, or fungal infections

2024-09-28 | GSE277849 | GEO

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections.

Project description:Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections.

| PRJNA1164321 | ENA

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [ValidationCohort]

Project description:Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [ValidationCohort]

| PRJNA1164325 | ENA

Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [DiscoveryCohort]

Project description:Pathogen Class-specific Transcriptional Responses Derived from PBMCs Accurately Discriminate Between Fungal, Bacterial, and Viral Infections [DiscoveryCohort]

| PRJNA1164323 | ENA

Host-response Transcriptional Biomarkers Accurately Discriminate Bacterial and Viral Infections by Global Pathogens

Project description:Global endemic infections, such as leptospirosis, rickettsial diseases, and dengue infections present diagnostic challenges, posing a dilemma for antibiotic stewardship worldwide. The goal of this project was to identify accurate transcriptional classifiers able to discriminate between bacterial and viral illness of global pathogens.

2023-12-19 | GSE211567 | GEO

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance.

Project description:Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

| S-EPMC10728077 | biostudies-literature

Host-response Transcriptional Biomarkers Accurately Discriminate Bacterial and Viral Infections by Global Pathogens

Project description:Host-response Transcriptional Biomarkers Accurately Discriminate Bacterial and Viral Infections by Global Pathogens

| PRJNA870919 | ENA

Transcriptional Profiling is Superior to Procalcitonin to Discriminate Bacterial vs. Viral Lower Respiratory Tract Infections in Hospitalized Adults

Project description:Background: Distinguishing between bacterial and viral lower respiratory tract infections (LRTI) in hospitalized patients remains challenging. Transcriptional profiling is a promising tool for improving diagnosis in LRTI. Methods: We performed whole blood transcriptional analysis in a cohort of 118 adult patients (median [IQR] age, 61 [50-76] years) hospitalized with bacterial, viral or viral-bacterial LRTI, and 40 age-matched healthy controls (60 [46-70] years). We applied class comparisons, modular analysis and class prediction algorithms to identify distinct biosignatures for bacterial and viral LRTI, which were validated in an independent group of patients. Results: Patients were classified as bacterial (B, n=22), viral (V, n=71) and bacterial-viral LRTI (BV, n=25) based on comprehensive microbiologic testing. Compared with healthy controls statistical group comparisons (p<0.01; with multiple test corrections) identified 3,376 differentially expressed genes in patients with B-LRTI; 2,391 in V-LRTI, and 2,628 in BV-LRTI. Independent of etiologic pathogen, patients with LRTI demonstrated overexpression of innate immunity and underexpression of adaptive immunity genes. Patients with B-LRTI showed significant overexpression of inflammation (B>BV>V) and neutrophils (B>BV>V) while those with V-LRTI displayed significantly greater overexpression of interferon genes (V>BV>B). The K-Nearest Neighbors (K-NN) algorithm identified 10 classifier genes that discriminated patients with bacterial vs viral LRTI with 97% [95%CI: 84-100] sensitivity and 92% [77-98] specificity. In comparison, procalcitonin classified bacterial vs viral LRTI with 38% [18-62] sensitivity and 91% [76-98] specificity. Conclusions: Transcriptional profiling can be used as a helpful tool for the diagnosis of adults hospitalized with LRTI. 158 samples, no replicates; bacterial LRTI n=22, viral LRTI n=71, bacterial-viral coinfections n=25, and healthy controls n=40

2015-09-14 | E-GEOD-60244 | biostudies-arrayexpress

Transcriptional Profiling is Superior to Procalcitonin to Discriminate Bacterial vs. Viral Lower Respiratory Tract Infections in Hospitalized Adults

2015-09-14 | GSE60244 | GEO