Effect of Auraptene on angiogenesis in Xenograft model of breast cancer
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Objectives Angiogenesis is the most important challenge in breast cancer treatment. Recently, scientists become interesting in rare natural products and intensive researches was performed to identify their pharmacological profile. Auraptene shows helpful effects such as cancer chemo-preventive, anti-inflammatory, anti-oxidant, immuno-modulatory. In this regard, we investigated the anti-angiogenesis effect of Auraptene in in-vitro and in-vivo model of breast cancer. Methods In this study, 4T, MDA-MB-231 and HUVEC cell lines were used. The proliferation study was done by MTT assay. For tube formation assay, 250 matrigel, 1 × 10 4 HUVEC treated with Auraptene, 20 ng/mL EGF, 20 ng/mL bFGF and 20 ng/mL VEGF were used. Gene expression of important gene related to angiogenesis in animal model of breast cancer was investigated by Real-time PCR. Protein expression of VCAM-1 and TNFR-1 gene related to angiogenesis in animal model of breast cancer was investigated by western-blot. Results Auraptene treatment led to reduction in cell viability of MDA-MB-231 in a concentration-dependent manner. Also, we observed change in the number of tubes or branches formed by cells incubated with 40 and 80 μM Auraptene. Auraptene effect the gene expression of important gene related to angiogenesis (VEGF, VEGFR2, COX2, IFNɣ). Moreover, the western blot data exhibited that Auraptene effect the protein expression of VCAM-1 and TNFR-1. Conclusions Overall, this study shows that Auraptene significantly suppressed angiogenesis via down-regulation of VEGF, VEGFR2, VCAM-1, TNFR-1, COX-2 and up-regulation of IFNγ.
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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 | 0.000 | 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) | 0.000 | 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 it