Glycyrrhizic Acid Ameliorates HMGB1-Mediated Cell Death and Inflammation after Renal Ischemia Reperfusion Injury
Bibliographic record
Abstract
BACKGROUND: Renal ischemia reperfusion injury (IRI) leads to acute kidney injury (AKI) and the death of tubular epithelial cells (TEC). The release of high-mobility group box-1 (HMGB1) and other damage-associated molecular pattern moieties from dying cells may promote organ dysfunction and inflammation by effects on TEC. Glycyrrhizic acid (GZA) is a functional inhibitor of HMGB1, but its ability to attenuate the HMGB1-mediated injury of TEC has not been tested. METHODS/RESULTS: In vitro, hypoxia and cytokine treatment killed TEC and resulted in the progressive release of HMGB1 into the supernatant. GZA reduced the hypoxia-induced TEC death as measured by annexin-V and propidium iodide. Hypoxia increased the expression of MCP-1 and CXCL1 in TEC, which was reduced by GZA in a dose-dependent manner. Similarly, the HMGB1 activation of effector NK cells was inhibited by GZA. To test the effect of HMGB1 neutralization by GZA in vivo, mice were subjected to renal IRI. HMGB1 protein expression increased progressively in kidneys from 4 to 24 h after ischemia and was detected in tubular cells by 4 h using immunohistochemistry. GZA preserved renal function after IRI and reduced tubular necrosis and neutrophil infiltration by histological analyses and ethidium homodimer staining. CONCLUSIONS: Importantly, these data demonstrate for the first time that AKI following hypoxia and renal IRI may be promoted by HMGB1 release, which can reduce the survival of TEC and augment inflammation. Inhibition of the interaction of HMGB1 with TEC through GZA may represent a therapeutic strategy for the attenuation of renal injury following IRI and transplantation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".