Auto-induction and growth stimulatory effect of betacellulin in human pancreatic cancer cells.
Bibliographic record
Abstract
Betacellulin (BTC) was identified in mouse pancreatic beta cell tumors as a member of the epidermal growth factor (EGF) family, and was found to bind and activate the EGF receptor. BTC is also expressed in some human malignancies and may have an important role in tumor growth progression. We examined whether BTC and EGF have a growth stimulatory effect on human pancreatic cancer cell lines both in vitro and in vivo. We also investigated the BTC expression and autonomous induction of BTC in pancreatic cancer cells. in vitro, both BTC and EGF had almost the same proliferative effect on Panc-1, MIA PaCa-2 and AsPC-1. in vivo, in a Panc-1 inoculated athymic mice model, BTC-treated tumors grew approximately five times larger than in control. Immunocytochemistry showed that BTC expression occurred in three pancreatic cancer cell lines, with MIA PaCa-2 showing the strongest intensity. Semi-quantitative RT-PCR of MIA Paca-2 showed that mRNA levels of BTC gradually increased after treatment with 1 nM BTC. Immunocytochemistry also demonstrated that the intensity of BTC-like immunoreactivity was increased when treated with 1 nM BTC but was reduced after treatment with 100 nM of AG1478, an EGF receptor tyrosine kinase inhibitor. BTC has thus a significant growth stimulatory effect on pancreatic cancer cells and might function as an autocrine and paracrine growth factor. BTC expression in pancreatic cancer cells is, at least in part, controlled by an auto-induction mechanism.
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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) | 0.000 | 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) | 0.002 | 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".