Transcriptomic Hallmarks of Ischemia-Reperfusion Injury
Bibliographic record
Abstract
Ischemia reperfusion injury (IRI) is associated with a broad array of life-threatening medical conditions including myocardial infarct, cerebral stroke, and organ transplant. Although the pathobiology and clinical manifestations of IRI are well reviewed by previous publications, IRI-related transcriptomic alterations are less studied. This study aimed to reveal a transcriptomic hallmark for IRI by using the RNA-sequencing data provided by several studies on non-human preclinical experimental models. In this regard, we focused on the transcriptional responses of IRI in an acute time-point up to 48 h. We compiled a list of highly reported genes in the current literature that are affected in the context of IRI. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and found many of the up-regulated genes to be involved in cell survival, cell surface signaling, response to oxidative stress, and inflammatory response, while down-regulated genes were predominantly involved in ion transport. Furthermore, by GO analysis, we found that multiple inflammatory and stress response processes were affected after IRI. Tumor necrosis factor alpha (TNF) and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathways were also highlighted in the Kyoto Encyclopedia of Genes and Genomes enrichment analysis. In the last section, we discuss the treatment approaches and their efficacy for IRI by comparing RNA sequencing data from therapeutic interventions with the results of our cross-comparison of differentially expressed genes and pathways across IRI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".