Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1189034 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The skin Microbiome stratifies Patients with CTCL into two subgroups. One subgroup has a balanced microbiome, while the other subgroups has a skin dybiosis with S. aureus outgrowth. This is accompanied by impaired TCR repertoire and poor clinical outcome.
2023-12-01 | GSE218874 | GEO
Project description:The skin Microbiome stratifies Patients with CTCL into two subgroups. One subgroup has a balanced microbiome, while the other subgroups has a skin dybiosis with S. aureus outgrow. This is accompanied by impaired TCR repertoir and poor clinical outcome.
2023-12-01 | GSE221149 | GEO
Project description:In this study, we conducted an integrated analysis of skin measurements, clinical BSTI surveys, and the skin microbiome of 950 Korean subjects to examine the ideal skin microbiome-biophysical association. By utilizing four skin biophysical parameters, we identified four distinct Korean Skin Cutotypes (KSCs) and categorized the subjects into three aging groups based on their age distribution. We established strong connections between 15 core genera and the four KSC types within the three aging groups, revealing three prominent clusters of the facial skin microbiome. Together with skin microbiome variations, skin tone/elasticity distinguishes aging groups while oiliness/hydration distinguishes individual differences within aging groups. Our study provides prospective reality data for customized skin care based on the microbiome environment of each skin type.
| EGAS00001007334 | EGA
Project description:Squalene makes up 12 % of human skin surface lipids, however little is known about its affects on the host skin microbiome. Here we tested the effect of squalene on genetic regulation of staphylococci, showing that it profoundly affects expression virulence or colonisation determinants, and of iron uptake systems.
2018-06-30 | E-MTAB-6352 | biostudies-arrayexpress
Project description:Surgical specimens from children with infantile hemangioma or lymphatic malformations, as well as healthy appearing adjacent skin, were analyzed by microarray analysis of microRNA expression. Unsupervised hierarchical clustering was performed to identify microRNAs that were differentially expressed in IH compared to lymphatic malformations and skin
2016-06-09 | GSE69136 | GEO
Project description:We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. The present study aimed to examine the stability of machine-learning algorithms in new biopsies, compare 3AA vs. 4AA algorithms, assess supervised binary classifiers trained on histologic or molecular diagnoses, create a report combining many scores into an ensemble of estimates, and examine possible automated sign-outs. Algorithms derived in cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (AUCs>0.87) better than histologic rejection (AUCs<0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by one expert showed highly significant agreement with histology (p<0.001), but with many discrepancies as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated sign-outs.Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states. (ClinicalTrials.gov NCT02670408).
2019-08-16 | GSE124897 | GEO
Project description:We measured the expression profiles of a series of 30 Rhabdomyosarcoma biopsy samples (15 embryonal RMS, 10 translocation-positive alveolar RMS and 5 translocation-negative alveolar RMS). Expression data was used for unsupervised hierarchical clustering as well as supervised analysis to find gene expression signatures characteristic for the different histological RMS subgroups.
2004-06-30 | E-MEXP-121 | biostudies-arrayexpress
Project description:The common bottlenose dolphin (Tursiops truncatus) is carnivorous cetacean thriving in marine environment are one of the most common apex predators found in coastal and estuarine ecosystems. Although recent studies have focused on capturing the circulating metabolomes of these organisms, with respect to pollutants and exposures of the marine environment, the skin and blubber are important protective organs that have not been probed. Using 1HNMR based untargeted metabolomics we quantified 51 metabolites belonging to 74 different metabolic pathways in the skin and blubber of bottle nose dolphins (n=5) samples collected in 2017 from the coast of Mexico. Results indicate that the skin and blubber metabolism are quantitatively different. These metabolite abundances could help discriminate the tissue-types using supervised and unsupervised PCA and PLSDA analysis. Heat maps and random forest analysis point to unique metabolites that are important classifiers of the tissue-type. The altered metabolic patterns, mainly linking fatty acid metabolism and ketogenic amino acids, seem to constitute a characteristic of blubber, while the skin showed diverse metabolites involved in gluceoneogenic pathways. 1H NMR spectra allowed the identification of metabolites associated with these organ types, such as pyruvic acid, arginine, ornithine, 2-hydroxybutyric acid, 3-hydroxyisobutyric acid, and acetic acid, as discriminatory and classifying metabolites. These results would lead to further understanding of dolphin skin and blubber metabolism for better efforts in their conservation as well as a measure of marine pollution and ecotoxicology.
2018-09-12 | ST001053 | MetabolomicsWorkbench
Project description:We used a phenotypic cell sorting technique to ask whether phenotypically supervised scRNAseq analysis (pheno-scRNAseq) can provide more insight into heterogeneous cell behaviors than unsupervised scRNAseq. Using a simple 3D in vitro breast cancer (BRCA) model, we conducted pheno-scRNAseq on invasive and non-invasive cells and compared the results to phenotype-agnostic scRNAseq analysis. Pheno-scRNAseq identified unique and more selective differentially expressed genes (DEGs) than unsupervised scRNAseq analysis. Functional studies validated the utility of pheno-scRNAseq in understanding within-cell-type functional heterogeneity and revealed that migration phenotypes were coordinated with specific metabolic, proliferation, stress, and immune phenotypes.
2020-10-31 | GSE158844 | GEO
Project description:We performed transcriptome analysis and multimodal data integration of the transcriptome and the microbiome of the skin of Mycosis fungoides Patients.
2023-12-01 | GSE221148 | GEO