Protective effects of pioglitazone on vascular endothelial cell dysfunction induced by high glucose via inhibition of IKKα/β–NFκB signaling mediated by PPARγ in vitro
Bibliographic record
Abstract
PIO, a synthetic ligand for PPARγ, is used clinically to treat T2DM. However, little is known about its protective effects on endothelium and the underlying mechanisms. In this study, we sought to investigate the protective effects of PIO on endothelium and its probable mechanisms: 95% confluent wild type (WT) HUVECs and PPARγLow-HUVECs that we first injured with HG (33 mmol·L–1) were first pretreated with 10 μmol·L–1 of GW9662 for 30 min, and then treated the cells with different concentrations of PIO (5, 10, or 20 μmol·L–1) for 24 h. Finally, we measured the levels of NO, ET1, TNFα, and IL6 in the cell culture supernatant. These cells were then used to determine cell viability, caspase3 activity, the levels of IKKα/β mRNA, IKKα/β, and NFκB-p65. Severe dysfunction and activation of IKKα/β–NFκB signaling occurred after we exposed HUVECs to HG. Conversely, treatment with PIO significantly attenuated the dysfunction and the activation of IKKα/β–NFκB signaling induced by HG in a dose-dependent manner. Moreover, the protective effects of PIO were completely abrogated by GW9662 or down-regulation of PPARγ. Taken together, the results indicate that PIO protects HUVECs against the HG-induced dysfunction through the inhibition of IKKα/β–NFκB signaling mediated by PPARγ.
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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) | 0.001 | 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".