Circular RNA mediated gene regulation in chronic diabetic complications
Bibliographic record
Abstract
Chronic diabetic complications affect multiple organs causing widespread organ damage. Although there are some commonalities, the phenotype of such changes show tissue specific variation. Given this, we examined whether differences in circular RNA (circRNA) mediated gene regulatory mechanisms contribute to changes in gene expression at the basal level and in diabetes. CircRNAs are single-stranded RNA with covalently closed loop structures and act as miRNA sponges, factors of RNA splicing, scaffolding for proteins, regulators of transcription, and modulators of the expression of parental genes, among other roles. We examined heart and retinal tissue from Streptozotocin-induced diabetic mice with established diabetes related tissue damage and tissue from non-diabetic controls. A custom array analysis was performed and the data were analysed. Two major circRNA mediated processes were uniquely upregulated in diabetic heart tissue, namely, positive regulation of endothelial cell migration and regulation of mitochondria: mitochondrial electron transport. In the retina, circRNAs regulating extracellular matrix protein production and endothelial to mesenchymal transition (EndMT) were found to be upregulated. The current study identified regulatory and potential pathogenetic roles of specific circRNA in diabetic retinopathy and cardiomyopathy. Understanding such novel mechanisms, may in the future, be useful to develop RNA based treatment strategies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".