Morroniside and loganin extracted from Cornus officinalis have protective effects on rat mesangial cell proliferation exposed to advanced glycation end products by preventing oxidative stress
Bibliographic record
Abstract
Advanced glycation end products (AGE) are involved in the alterations of renal mesangial cell (MCs) growth, a feature of early stages of diabetic nephropathy (DN). We postulate that morroniside and loganin, 2 components extracted from Cornus officinalis, may ameliorate the detrimental effects of AGE-induced MCs proliferation by preventing oxidative stress. Rat MCs cultured in AGE milieu were treated with morroniside and loganin. Results showed that morroniside and loganin inhibited AGE-induced MC proliferation as measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Fluorescence microscopy revealed that the morroniside and loganin improved the morphological changes of MCs. Flow cytometric analysis showed that morroniside and loganin inhibited the cell cycle of rat MCs. Furthermore, the level of reactive oxygen species was significantly reduced, and the activities of superoxide dismutase and glutathione peroxidase were markedly increased, whereas the level of malondialdehyde was not significantly reduced. These results suggest that morroniside and loganin regulate MC growth by preventing oxidative stress. Thus, this study provides a molecular basis for the use of morroniside and loganin in the early stages of DN.
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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".