Supplementing diet with Manitoba lingonberry juice reduces kidney ischemia‐reperfusion injury
Bibliographic record
Abstract
BACKGROUND: Lingonberry (Vaccinium vitis-idaea L.) contains high levels of anthocyanins which are bioavailable in the kidney and may be protective against ischemia-reperfusion (IR)-induced acute kidney injury. This study investigated the effect of lingonberry juice on the IR-induced stress-activated signalling pathway and inflammatory response in the kidney. RESULTS: Sprague-Dawley rats subjected to kidney IR had significantly impaired kidney function, with increased activation of the JNK signalling pathway and increased inflammatory response, measured using a multiplex panel containing an extensive array of inflammatory biomarkers. In rats fed 1 mL lingonberry juice daily for 3 weeks prior to IR, kidney function was protected and attenuation of inflammatory response and JNK signalling was reflected in the reduction of the measured biomarkers. In vitro results in cultured HK-2 cells confirmed that lingonberry anthocyanins reduced JNK signalling and inflammatory gene expression after IR. CONCLUSION: This study shows, for the first time, that daily supplementation with lingonberry juice may protect against loss of kidney function induced by IR injury by modulating JNK signalling and inhibiting the subsequent inflammatory response. © 2017 Her Majesty the Queen in Right of Canada. Journal of the Science of Food and Agriculture © 2017 Society of Chemical Industry.
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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) | 0.003 | 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".