ArticleImmune network2017
Comparative Analyses of Signature Genes in Acute Rejection and Operational Tolerance.
Article in Immune network, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Rapid progression of adult T-cell leukemia/lymphoma as tumor-infiltrating Tregs after PD-1 blockade.Blood · 2019Trial
- Transcriptome Analysis of Myocardial Ischemic-Hypoxic Injury in Rats and Hypoxic H9C2 Cells.ESC heart failure · 2024Article
- Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning.Scientific reports · 2024Article
- Restoration of Immune Privilege in Human Dermal Papillae Controlling Epithelial-Mesenchymal Interactions in Hair Formation.Tissue engineering and regenerative medicine · 2022Article
- Single-Cell RNA Sequencing Identifies Intra-Graft Population Heterogeneity in Acute Heart Allograft Rejection in Mouse.Frontiers in immunology · 2022Article
- Molecular Diversity of Clinically Stable Human Kidney Allografts.JAMA network open · 2021Article
- An Unbiased Machine Learning Exploration Reveals Gene Sets Predictive of Allograft Tolerance After Kidney Transplantation.Frontiers in immunology · 2021Article
- Knockdown of FOXA2 Impairs Hair-Inductive Activity of Cultured Human Follicular Keratinocytes.Frontiers in cell and developmental biology · 2020Article
- Mechanisms and biomarkers of immune quiescence in kidney transplantation.Human immunology · 2018Review
- A Machine Learning Analysis of Prognostic Genes Associated With Allograft Tolerance After Renal Transplantation.Cell transplantationArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using biomarkers as prediction tools or therapeutic targets can be a valuable strategy in transplantation. Recent studies identified biomarkers of acute rejection (AR) and operational tolerance (TOL) through the application of meta-analysis. In this study, we comparatively analyzed the signature genes in acute rejection and operational tolerance seen in human allogeneic transplantations using massive bioinformatical meta-analysis. To identify the signature genes in opposite immunological conditions, AR and TOL, we first collected the 1,252 gene expression data specifically intended for those circumstances. Then we excluded based on biological cut-values, Principal Component Analysis (PCA) as well as Multi-Dimensional Scaling (MDS). Using differentially expressed genes (DEGs) from meta-analysis, we then applied a ranked scoring system to identify the signature genes of AR and TOL. We identified 53 up-regulated and 32 down-regulated signature genes in acute rejection condition. Among them,
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.