Salvianolic acid B attenuates diabetic nephropathy through alleviating ADORA2B, NALP3 in flammasome, and NFκB activity
Bibliographic record
Abstract
Diabetic nephropathy is one of the microvascular complications of diabetes. This study is aimed at investigating the role and mechanisms of salvianolic acid B (Sal B) in diabetic nephropathy. High glucose (HG)-induced human renal tubular epithelial HK-2 cells were treated with Sal B, BAY-60-6583 (agonist of adenosine 2B receptor), or PSB-603 (antagonist of adenosine 2B receptor) for 24 h. Adenosine A2b receptor (ADORA2B), NACHT, leucine-rich repeat (LRR), and pyrin (PYD) domains-containing protein 3 (NALP3), and nuclear factor Kappa B (NFκB) expressions, mitochondrial membrane potential (MMP), and reactive oxygen species (ROS) levels were examined. Following 6 weeks of Sal B treatment, db/db mice blood and kidney tissue were harvested for biochemical detection with hematoxylin-eosin (H&E), Masson's, periodic acid schiff (PAS), and Sirius red staining and detection of ADORA2B, NALP3, NFκB, interleukin 1β (IL-1β), and toll-like receptor 4 (TLR4) activity. NFκB, NALP3, and ADORA2B were found to be downregulated in Sal B treated HK-2 cells exposed to high glucose (HG), accompanied by elevated levels of MMPs and reduced intracellular ROS production. Sal B-treated diabetic mice had the improvement in body weight, water intake, hyperglycemia, hyperlipidemia, and liver and kidney function. Altogether, Sal B attenuates HG-induced kidney tubule cell injury and diabetic nephropathy in diabetic mice, providing clues to other novel mechanisms by which Sal B is beneficial in diabetic nephropathy.
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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".