Similar Datasets
Project description:mNGS for BALF diagnostics in patients with lower respiratory tract infections
| PRJEB54732 | ENA
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:This study used proteomic, biomechanical, and functional analyses to further define neutrophil heterogeneity in the context of SLE. Mass spectrometry proteomic and phosphoproteomic analyses were performed in healthy control normal density neutrophils (NDNs), SLE NDNs and in autologous SLE LDGs. Proteomic and phosphoproteomic differences were detected when comparing control to SLE neutrophils and when comparing SLE NDNs to SLE LDGs.
2021-11-04 | PXD021096 | Pride
Project description:Microbiome and metabolome study of SLE
| PRJEB39044 | ENA
Project description:Systemic lupus erythermatosus (SLE) is a complex autoimmune disease, and epigenetic study is promissing for illustrating the mechanisms of SLE pathogenesis. Assay for transposase accessible chromatin in single cells sequencing (scATAC-seq) shows priority to trackle this barrier. Thus, scATAC-seq was applied to difine the landscape of active regulatory DNA in systemic lupus erythermatosus (SLE) at single cell resolusion. Peripheral blood mononuclear cells (PBMCs) were robustly clustered based on their types without using antibodies. 20 patterns of transcription factor (TF) activation that drive abnormal immune response in SLE patients were identified. Meaniwhile, 10 genes associated with SLE pathogenesis that alter T cell activity and 52 significantly enriched TF motifs in SLE patients were revealed. These results reveal candidate makers in SLE-PBMC, showing feasibility for epigenetic therapy, and providing a foundational insights on epigenetic therapy.
2021-05-28 | GSE158263 | GEO