Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| ERR11008928.fastq.gz | Fastqsanger.gz | |||
| ERR11008929.fastq.gz | Fastqsanger.gz | |||
| ERR11008930.fastq.gz | Fastqsanger.gz | |||
| ERR11008931.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 71 |
Similar Datasets
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:mNGS for BALF diagnostics in patients with lower respiratory tract infections
| PRJEB54732 | ENA
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:This paper presents a teleoperation system of robot grasping for undefined objects based on a real-time EEG (Electroencephalography) measurement and shared autonomy. When grasping an undefined object in an unstructured environment, real-time human decision is necessary since fully autonomous grasping may not handle uncertain situations. The proposed system allows involvement of a wide range of human decisions throughout the entire grasping procedure, including 3D movement of the gripper, selecting proper grasping posture, and adjusting the amount of grip force. These multiple decision-making procedures of the human operator have been implemented with six flickering blocks for steady-state visually evoked potentials (SSVEP) by dividing the grasping task into predefined substeps. Each substep consists of approaching the object, selecting posture and grip force, grasping, transporting to the desired position, and releasing. The graphical user interface (GUI) displays the current substep and simple symbols beside each flickering block for quick understanding. The tele-grasping of various objects by using real-time human decisions of selecting among four possible postures and three levels of grip force has been demonstrated. This system can be adapted to other sequential EEG-controlled teleoperation tasks that require complex human decisions.
| S-EPMC10770246 | biostudies-literature