Shenqi Fuzheng Injection (SFI) Enhances IFN-<i>α</i> Inhibitory Effect on Hepatocellular Carcinoma Cells by Reducing VEGF Expression: Validation by Gene Silencing Technique
Bibliographic record
Abstract
Shenqi Fuzheng Injection (SFI) is a traditional Chinese medicine injection with anticancer properties and is mainly composed of ginseng and astragalus. Its efficacy has been confirmed in clinical trials, but the mechanism remains unclear. We investigated the effect of SFI on vascular endothelial growth factor (VEGF) gene expression in hepatocellular carcinoma (HCC) cells and identified its possible mechanism of synergistic effects when combined with the chemotherapeutic drug interferon (IFN-) α . An MTT assay was used to measure the inhibition effects of low-dose IFN- α (6000 IU) with or without SFI (0.5 g/L) on the HCC cell line MHCC97. VEGF-silenced MHCC97L-mir200 cell lines were prepared using lentiviral vectors and evaluated by real-time PCR to determine the inhibition effect. We examined MHCC97L-mir200 and MHCC97L cells by MTT assay, using IFN- α alone or in combination with SFI. The inhibition ratio of IFN- α (6000 IU) was -29.5%, while that for IFN- α (6000 IU) + SFI (0.5 g/L) was 17.0%, which was significantly higher than that for the IFN- α group (P < 0.01). The VEGF gene was silenced successfully in MHCC97-L cells. After interference of VEGF, the inhibition by SFI and IFN- α in MHCC97L-mir200 did not differ from that in MHCC97-L cells (P > 0.05). SFI can reduce the expression of VEGF in HCC, which can increase the efficacy of IFN- α , providing a theoretical basis for clinical application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".