Vitronetcin promotes cell growth and inhibits apoptotic stimuli in a human hepatoma cell line via the activation of caspases
Bibliographic record
Abstract
This study sought to understand the effects of vitronectin (VTN) on the growth of SMMC-7721 hepatoma cells. In addition, this study examined how VTN inhibits the induction of apoptosis in SMMC-7721 cells by 3,3'-diindolylmethane (DIM), a metabolite of natural phytochemicals, and preliminarily investigated the signaling molecules involved in this process. A cell proliferation reagent was used to observe the effects of VTN on cell proliferation rates. Laser scanning confocal microscopy was performed to observe the effects of VTN on the morphology of tubulin, a component of the cytoskeleton. Flow cytometry and Western blotting assays were used to observe the inhibitory effects of VTN on DIM-induced apoptosis in SMMC-7721 cells and changes in the expression levels of the signaling molecules involved in this process. VTN promoted tumor cell growth in a concentration-dependent manner and inhibited apoptosis caused by the effects of apoptosis-inducing agents. Under in vitro experimental conditions, VTN contributed to the growth of SMMC-7721 hepatoma cells and protected them from the effects of an apoptosis-inducing agent. These findings suggest that during hepatocellular carcinogenesis, VTN may promote tumor cell growth and inhibit chemically induced apoptosis.
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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) | 0.002 | 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".