Differential effects of diphenyl diselenide (PhSe) <sub>2</sub> on mitochondria-related pathways depending on the cellular energy status in Bovine Vascular Endothelial Cells
Bibliographic record
Abstract
Abstract Cellular energy metabolism varies depending on tissue and cell type, as well as the availability of energy substrates and energy demands. We recently investigated the variations in cellular metabolism and antioxidant responses in primary bovine vascular endothelial cells (BAECs) under different energetic substrate conditions in vitro , specifically glucose or galactose. In this context, pharmacological agents may affect cells differently depending on their energy metabolism status. In this study, we aimed to characterize the effects of diphenyl diselenide ((PhSe) 2 ), a redox-active molecule known for its prominent cardiovascular effects, on redox-bioenergetic cellular pathways under glycolytic or oxidative conditions in BAECs. Under glucose conditions, (PhSe) 2 positively impacted mitochondrial oxidative capacity, as assessed by respirometry, and was associated with changes in mitochondrial cellular dynamics. However, these changes were not observed in cells cultured with galactose. Although (PhSe) 2 induced the nuclear translocation of the redox sensitive nuclear factor erythroid 2-related factor 2 (Nrf2) in both glucose and galactose media, Nrf2 remained in the nuclei of cells cultured in galactose for a longer duration. Additionally, activation of another redox sensitive transcription factor, forkhead O3 (FOXO3a) was only detected in galactose media. Notably, (PhSe) 2 induced the expression of genes controlling mitochondrial antioxidant capacity and glutathione synthesis and recycling in glucose media, whereas its effects in galactose media were primarily focused on glutathione homeostasis. In conclusion, our findings underscore the critical influence of cellular metabolic status on the antioxidant capacity of redox-active molecules such as (PhSe) 2 .
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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".