Grape seed proanthocyanidin extract inhibits ferroptosis by activating Nrf2/HO-1 and protects against diabetic kidney disease
Bibliographic record
Abstract
BACKGROUND: The main pathological characteristic of diabetic kidney disease (DKD) is renal dysfunction caused by tubular injury. Ferroptosis is a recently discovered form of cell death closely linked to renal tubular injury in DKD. Nuclear factor erythroid-2 related factor 2/heme oxygenase 1(Nrf2/HO-1) is crucial in controlling ferroptosis. Grape seed proanthocyanidin extract (GSPE) mitigates renal dysfunction in DKD by activating Nrf2/HO-1 pathway. However, the role of GSPE in protecting against DKD through ferroptosis modulation has been insufficiently explored. METHODS: Streptozotocin (STZ)-induced diabetic rat models and HK2 cells cultured with high glucose were used as experimental objects in this study. HE and PAS staining was used to observe the morphological changes of rat kidney. Fe2+ and reactive oxygen species (ROS) levels as well as apoptosis were determined by fluorescent staining. Transferrin receptor protein 1(TfR1), acylcoa synthetase long chain family member 4(ACSL4), glutathione peroxidase 4 (GPX4), Nrf2 and HO-1 proteins were detected by western blot. Subsequently, Nrf2 was knocked down and oxidative stress and ferroptosis were observed in HK2 cells. RESULTS: In vivo and vitro result showed that GSPE treatment significantly lessened renal impairment, oxidative stress, and ferroptosis in DKD, while the application of Ferrostatin-1 (Fer-1) notably amplified the anti-ferroptotic effect of GSPE. Moreover, the anti-ferroptotic effect of GSPE was markedly diminished after Nrf2 expression was downregulated in HK2 cells. CONCLUSION: GSPE treatment reduced ferroptosis in DKD by modulating the Nrf2/HO-1. In conclusion, our results underscore the significance of ferroptosis in DKD and present new perspectives on the protective effects of GSPE in this condition.
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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".