Characterizing the sympathetic neuropeptide Y system in 4T1 murine mammary carcinoma model
Bibliographic record
Abstract
Neuropeptide Y (NPY) released from the sympathetic nervous system (SNS) is a potent mitogenic and angiogenic factor. Recent studies have illustrated the prevalence of NPY receptors in many cancer cell lines. Remarkably, little is known of the role that NPY and its Y1‐ and Y2‐receptors play in the progression of breast cancer tumor development. Moreover, no in vivo models have addressed the impact of NPY in breast cancer. In this study we used the murine 4T1 cell line as it mimics growth and metastatic patterns seen in human breast cancer. Cells (10 3 ) were injected into the inguinal mammary fat pad of female BALB/c mice (n=10), after 29 days tumors were harvested and prepared for immunofluorescence and molecular analysis (Western blot and qRT‐PCR). 4T1 tumors were characterized by: 1) extensive vascular development that was innervated by sympathetic neurons, and 2) robust expression of NPY, Y1 and Y2 receptors. Additionally, Y1 receptor mRNA was augmented in 4T1 tumors compared to cultured cells (p<0.05), suggesting potential for a Y1‐mediated functional role of NPY in this cancer model. Based on these data we show for the first time that breast cancer tumors raised from the 4T1 cell line have a complete and intact sympathetic NPY system. Thus, we conclude that this is an ideal model to investigate the functional impact of the SNS on breast cancer growth. CIHR
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".