Propofol Dose-Dependently Reduces Tumor Necrosis Factor-??-Induced Human Umbilical Vein Endothelial Cell Apoptosis: Effects on Bcl-2 and Bax Expression and Nitric Oxide Generation
Bibliographic record
Abstract
In Brief We investigated whether propofol can inhibit tumor necrosis factor (TNF)-α-induced apoptosis in cultured human umbilical vein endothelial cells (HUVECs). Isolated HUVECs were cultured in Dulbecco’s modified Eagle medium supplemented with 20% bovine calf serum. HUVECs in untreated and propofol control groups were cultured at 37°C for 24.5 h. HUVECs in the TNF treatment groups were initially cultured for 30 min in the presence of TNF or various concentrations of propofol, respectively, which were then cultured for 24 h with the addition of TNF at 40 ng/mL in the medium. Apoptosis was detected using terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) and confirmed by electron microscopy. The antiapoptotic Bcl-2 and proapoptotic Bax protein expressions were measured by immunocytochemical analysis. TNF stimulation resulted in a reduced Bcl-2/Bax ratio and increased apoptotic index (AI: percentage of apoptotic cells) in HUVECs. Propofol, at concentrations ≥12 μM, significantly (P < 0.001) and dose-dependently attenuated TNF-induced increase in AI and decrease in Bcl-2/Bax ratio. This was accompanied by increases in nitric oxide production. There is an inverse correlation between the ratio of Bcl-2/Bax expression and AI (P = 0.0009). These results suggest that propofol, at clinical relevant concentrations, can reduce TNF-induced HUVEC apoptosis. IMPLICATIONS: Our study provides evidence that propofol, a frequently used anesthetic for cardiac surgery and for postoperative sedation, attenuates tumor necrosis factor-induced human endothelial cell programmed cell death in a dose-dependent manner. Propofol may prove to be a promising drug in reducing peri- and postoperative cardiovascular symptoms.
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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".