Vicenin-2 and scolymoside inhibit high-glucose-induced vascular inflammation in vitro and in vivo
Bibliographic record
Abstract
The vascular inflammatory process has been suggested to play a key role in the initiation and progression of atherosclerosis, a major complication of diabetes mellitus. Thus, in this study, we attempted to determine whether 2 structurally related flavonoids found in Cyclopia subternata, vicenin-2 and scolymoside, can suppress high-glucose (HG)-induced vascular inflammatory processes in human umbilical vein endothelial cells (HUVECs) and mice. The effects of vicenin-2 and scolymoside on HG-induced vascular inflammation were determined by measuring vascular permeability, leukocyte adhesion and migration, cell adhesion molecule (CAM) expression levels, and reactive oxygen species (ROS) formation. In addition, the anti-inflammation mechanism was investigated using immunofluorescence staining and Western blotting. The data showed that HG markedly increased vascular permeability, monocyte adhesion, expression of CAMs, formation of reactive oxygen species (ROS), and activation of nuclear factor (NF)-κB. Remarkably, pretreatment with vicenin-2 and scolymoside attenuated all of the above-mentioned vascular inflammatory effects of HG. HG-induced vascular inflammatory responses are critical events underlying the development of various diabetic complications; therefore, our results suggest that vicenin-2 and scolymoside have significant therapeutic benefits against diabetic complications and atherosclerosis.
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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".