Effect of tetramethylpyrazine combined with cisplatin on VEGF, KLF4 and ADAMTS1 in Lewis lung cancer mice
Bibliographic record
Abstract
OBJECTIVE: To further explore the function of combine use of tetramethylpyrazine (TMP) and cisplatin (DDP) in lung carcinoma. METHODS: We used the combination drug to treat Lewis lung cancer mice, investigated the expression level of vascular endothelial growth factor (VEGF), Kruppel-like factor 4 (KLF4) and A disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1) and to further explore the inhibitory effects and potential mechanism of TMP combined with DDP on tumor angiogenesis. RESULTS: The tumor growth was suppressed in TMP group, DDP group and TMP combined with DDP group. Furthermore, the weights and volume of tumor, the expression level of VEGF, KLF4 and ADAMTS1 were found significantly changed between experiment group and control group. These findings suggest that TMP with DDP had additional or synergistic effects to inhibit the tumor growth effectively, might be achieved through reducing the expression of angiogenesis promoting factor VEGF and increasing expression of angiogenesis inhibitors KLF4 and ADAMTS1. CONCLUSION: KLF4 and ADAMTS1 may be synergically involved in the angiogenesis in mouse Lewis lung cancer through the different signal ways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".