Overexpression of GDF15 protects kidneys from ischemia reperfusion injury and affects circular RNA expression
Bibliographic record
Abstract
Background: Renal failure and dysfunction remain one of the most significant morbidities impacting patient's life. Effective treatments still lack in the context of an increasing number of patients with renal failure. This study aims to investigate the impact of growth differentiation factor 15 (GDF15) in treating renal dysfunction and to explore its therapeutic potential. Methods: Renal injury was induced with a murine ischemia reperfusion injury (IRI) model. Mice overexpressing GDF15 (GDF15 transgenic (GDF15TG) mice, GDF15 knock out (GDF15 KO) mice and wild type (WT) mice all underwent IRI to test the effects of GDF15 on renal injury. Renal function and histopathological changes were measured 24 h after reperfusion. Cell apoptosis was detected by TUNEL and tissue inflammation was detected by myeloperoxidase (MPO) activity. qRT-PCR was conducted to determine the expression of genes and circular RNAs. Results: Overexpression of GDF15 reduced mortality of mice with lethal renal IRI whereas GDF15 deficiency increased the mortality. GDF15TG mice had better renal function with the lower levels of blood creatinine and blood urea nitrogen (BUN). Over-expression of GDF15 reduced kidney pathological changes, cell apoptosis, neutrophil infiltration and mortality. Over-expression of GDF15 also decreased the expression of apoptotic genes (high mobility group 1, HMGA1 and Bax), inflammatory genes IL-1β, IL-6, tumor necrosis factor (TNF-α), chemokine 1 (CK1), and senescent gene p21 whereas increases Bcl-XL, Importin 11 and CRIM1. IRI upregulated circular RNA Smad3 and reduced circular RNA Hipk3 and circular RNA Crim1, which was offset by GDF15. Conclusion: Over-expression of GDF15 protects renal function and prevents renal failure, highlighting its potential in treating renal failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".