Effect of Blocking of Neuropeptide Y Y2 Receptor on Tumor Angiogenesis and Progression in Normal and Diet-Induced Obese C57BL/6 Mice
Bibliographic record
Abstract
BACKGROUND: Obesity is a risk factor for some types of cancers. Angiogenesis is a necessary step in the multistage progression of tumors such as melanoma. Previous studies reported that neuropeptide Y (NPY) regulates angiogenesis by activating the Y2 receptor on endothelial cells. The present study examined the effects of the NPY Y2 receptor antagonist on tumor weight, angiogenesis and serum levels of vascular endothelial growth factor (VEGF), VEGF receptor-1 (VEGF-R1), and nitric oxide (NO). METHODS: Twenty four male C57BL/6 mice were divided into control and obese groups. The control group was fed a normal diet whereas the obese group was fed a high fat diet. After 16 weeks, 2×10(6) B16F10 melanoma cells were injected subcutaneously into all animals. Half of the control and the obese animals received 1 µM, 100 µL/kg NPY Y2 receptor antagonist (BIIE 0246) intraperitoneally. After two weeks, the animals were sacrificed, and angiogenic factors and tumor weights and angiogenesis were analyzed. RESULTS: Tumor weight in the obese mice was higher than in the control (p<0.05). Treatment with BIIE 0246 reduced tumor weight in the obese animals (p<0.05), without effect on control group (p>0.05). Administration of an NPY Y2 receptor antagonist decreased tumor angiogenesis (evaluated as capillary density/mm2) and serum VEGF concentration in the obese group without altering serum VEGF-R1 and NO concentrations. CONCLUSIONS: Blockade of the NPY Y2 receptor suppressed tumor growth in obese mice by affecting tumor angiogenesis. Thus, it seems that NPY and its Y2 receptor antagonist might be new targets in melanoma tumor therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".