Involvement of the mitogen-activated protein kinase kinase 2 in the induction of cell dissociation in pancreatic cancer
Bibliographic record
Abstract
In our previous investigation, mitogen-activated protein kinase kinase 2 (MEK2) was detected as a factor which was correlated to the potential of invasion-metastasis. In this study, the immunocytochemical, immunohistochemical and mRNA expressions of MEK2 were examined in pancreatic cancer cell lines and tissue samples, respectively. Constitutive expressions of MEK2 and phosphorylated MEK (p-MEK) were observed in PC-1.0 and ASPC-1 cells, which exhibited a growth pattern of single cells, whereas the relevant expressions were quite faint in PC-1 cells and CAPAN-2 cells, which exhibited a growth pattern of island-like clonies. Simultaneous inductions of MEK2 expressions and cell dissociation were observed after the treatment with a conditioned medium (CM) of PC-1.0 cells. The expression of MEK2 and p-MEK were reduced and the cell aggregation was found in PC-1.0 and ASPC-1 cells after U0126 (a MEK inhibitor) treatment. In vivo, both the MEK2 and p-MEK overexpressed in human pancreatic cancer tissues and p-MEK was found to be more strongly expressed in the invasive front than that in the center of tumor (P<0.05). MEK2 is closely related to pancreatic cancer cell dissociation. MEK2 activation is probably involved in the first step of the cascade in the invasion-metastasis of pancreatic cancer.
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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) | 0.001 | 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".