Donor Graft MicroRNAs: A Newly Identified Player in the Development of New-onset Diabetes After Liver Transplantation
Bibliographic record
Abstract
New-onset diabetes after liver transplantation (NODALT) is a frequent complication with an unfavorable outcome. We previously demonstrated a crucial link between donor graft genetics and the risk of NODALT. We selected 15 matched pairs of NODALT and non-NODALT liver recipients using propensity score matching analysis. The donor liver tissues were tested for the expression of 10 microRNAs (miRNAs) regulating human hepatic glucose homeostasis. The biological functions of potential target genes were predicted using gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Both miR-103 and miR-181a were significantly highly expressed in the NODALT group as compared to the non-NODALT group. The predicted target genes (e.g. Irs2, Pik3r1, Akt2, and Gsk3b) were involved in glucose import and the insulin signaling pathway. We also observed dysregulation of miRNAs (e.g. let-7, miR-26b, miR-145, and miR-183) in cultured human hepatocytes treated with tacrolimus or high glucose, the two independent risk factors of NODALT identified in this cohort. The hepatic miRNA profiles altered by tacrolimus or hyperglycemia were associated with insulin resistance and glucose homeostatic imbalance as revealed by enrichment analysis. The disease susceptibility miRNA expressive pattern could be imported directly from the donor and consolidated by the transplant factors.
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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 | 0.001 | 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".