Daio-Orengedokudo works as a cell-proliferating compound in endothelial cells
Bibliographic record
Abstract
Daio-Orengedokuto is a combination drug that has inhibitory effects on HMG-CoA reductase and pancreatic lipase. Here we investigated whether Daio-Orengedokuto has effects on vascular endothelial cells. To determine its effects on blood vessels, we examined roles of Daio-Orengedokuto in cell migration, cell apoptosis and cell cycle progression over bovine aortic endothelial cells (BAECs). Interestingly, Daio-Orengedokuto was shown to work as an anti-apoptotic agent, a cell cycle progressive agent and a cell-migration inducing agent in BAECs, whereas it was known to act as a tumor suppressor in cancer cells (unpublished data). The inducing effect of Daio-Orengedokuto on cell-cycle progression and cell migration in endothelium suggests that Daio-Orengedokuto may be referred to as a drug, inducing angiogenesis, healing wounds, and (or) remodeling vascular tissue. Then we further investigated which signaling molecules were activated by Daio-Orengedokuto and found that extracellular signal-regulated kinase (ERK) phosphorylation and IkappaB degradation were stimulated by the Daio-Orengedokuto treatment in BAECs. More interestingly, pretreatment with PD compound, an ERK inhibitor, blocked the anti-apoptosis induced by Daio-Orengedokuto. In conclusion, Daio-Orengedokuto plays a role in endothelial cell proliferation via activation of MAP kinase.
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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".