Targeted delivery of galunisertib attenuates fibrogenesis in an integrated <i>ex vivo</i> renal transplant and fibrosis model
Bibliographic record
Abstract
Abstract Normothermic machine perfusion is an emerging preservation technique for kidney allografts to reduce post-transplant complications, including interstitial fibrosis and tubular atrophy. This technique, however, could be improved by adding antifibrotic molecules to perfusion solutions. We established Machine perfusion and Organ slices as a Platform for Ex vivo Drug delivery (MOPED), to explore fibrogenesis suppression strategies. We perfused porcine kidneys ex vivo with galunisertib—a potent inhibitor of the transforming growth factor beta signaling pathway. To determine whether effects persisted, we also cultured precision-cut tissue slices prepared from the respective kidneys. Galunisertib supplementation improved the general viability, without negatively affecting renal function or elevating levels of injury markers or byproducts of oxidative stress. Galunisertib also reduced inflammation and more importantly, strongly suppressed the onset of fibrosis, especially when the treatment was continued in slices. Our results illustrate the value of targeted drug delivery, using isolated organ perfusion, for reducing post-transplant complications. One Sentence Summary Galunisertib supplementation during normothermic machine perfusion attenuates fibrogenesis without compromising renal function.
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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 | 0.001 |
| 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".