Ketamine attenuates high-glucose-mediated endothelial inflammation in human umbilical vein endothelial cells
Bibliographic record
Abstract
Hyperglycemia mediates oxidative stress, thus inducing transcription factor nuclear factor kappa B (NF-κB) activation, increasing endothelial adhesion molecule expression and monocyte/endothelial interaction, and resulting in endothelial injury. Ketamine was reported to attenuate oxidative stress in many cases. In this research, we determined whether and how ketamine protects against high-glucose-mediated augmentation of monocyte/endothelial interaction and endothelial adhesion molecule expression in human umbilical vein endothelial cells. High glucose augmented monocyte/endothelial adhesion and endothelial adhesion molecule expression. High glucose induced reactive oxygen species (ROS) production and augmented phospho-protein kinase C (p-PKC) βII expression and PKC activity. Moreover, high glucose inhibited the inhibitory subunit of nuclear factor-κBα (IκBα) expression in the cytoplasm and induced NF-κB nuclear translocation. Importantly, the effects induced by high glucose were counteracted by ketamine treatment. Further, CGP53353, a PKC βII inhibitor, inhibited high-glucose-mediated NF-κB nuclear translocation, attenuated adhesion molecule expression, and reduced monocyte/endothelial interaction. Further, these effects of ketamine against high-glucose-induced endothelial injury were inhibited by phorbol 12-myristate 13-acetate, a PKC βII activator. In conclusion, ketamine, via reducing ROS accumulation, inhibited PKC βII Ser660 phosphorylation and PKC and NF-κB activation and reduced high-glucose-induced expression of endothelial adhesion molecules and monocyte/endothelial interaction.
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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".