Punicalagin attenuates hyperuricemia via restoring hyperuricemia-induced renal and intestinal dysfunctions
Bibliographic record
Abstract
INTRODUCTION: It is estimated that 90% of hyperuricemia cases are attributed to the inability to excrete uric acid (UA). The two main organs in charge of excreting UA are the kidney (70%) and intestine (30%). Previous studies have reported that punicalagin (PU) could protect against kidney and intestinal damages, which makes it a potential candidate for alleviating hyperuricemia. However, the effects and deeper action mechanisms of PU for managing hyperuricemia are still unknown. OBJECTIVE: To investigate the effect and action mechanisms of PU for ameliorating hyperuricemia. METHODS: The effects and action mechanisms of PU on hyperuricemia were assessed using a hyperuricemia mice model. Phenotypic parameters, metabolomics analysis, and 16S rRNA sequencing were applied to explore the effect and fundamental action mechanisms inside the kidney and intestine of PU for improving hyperuricemia. RESULTS: PU administration significantly decreased elevated serum uric acid (SUA) levels in hyperuricemia mice, and effectively alleviated the kidney and intestinal damage caused by hyperuricemia. In the kidney, PU down-regulated the expression of UA resorption protein URAT1 and GLUT9, while up-regulating the expression of UA excretion protein ABCG2 and OAT1 as mediated via the activation of MAKP/NF-κB in hyperuricemia mice. Additionally, PU attenuated renal glycometabolism disorder, which contributed to improving kidney dysfunction and inflammation. Similarly, PU increased UA excretion protein expression via inhibiting MAKP/NF-κB activation in the intestine of hyperuricemia mice. Furthermore, PU restored gut microbiota dysbiosis in hyperuricemia mice. CONCLUSION: This research revealed the ameliorating impacts of PU on hyperuricemia by restoring kidney and intestine damage in hyperuricemia mice, and to be considered for the development of nutraceuticals used as UA-lowering agent.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".