Activation of β‐adrenergic receptors increases the <i>in vitro</i> migration of malignant hepatocytes
Bibliographic record
Abstract
AIM: Activation of adrenergic receptors (AR) has been reported to enhance the growth and invasion of various malignancies. The effects of AR agonists on malignant hepatocyte proliferation and migration have yet to be determined. METHODS: PLC/PRF/5 (PLC) and Huh-7 cells were exposed to a wide range of concentrations of the AR agonists noradrenaline (NA) and isoprenaline. Cell proliferation, migration, intracellular cyclic adenosine monophosphate (cAMP), protein kinase A (PKA) and C (PKC), matrix metalloproteinases (MMP)-2, -3, -7 and -9, and α(1) -, β(1) - and β(2) -AR expression were documented in both cell lines. RESULTS: Cell proliferative activity was unaltered following exposure to physiological and stress-related concentrations of AR agonists but migration was accelerated, an effect that was inhibited by the nonselective β-AR antagonist labetalol. cAMP, PKA, PKC or MMP expression remained unchanged. Although α(1) - and β(1) -AR expressions were abundant, β(2) -AR expression was limited in both cell lines. CONCLUSION: Unlike other malignancies studied to date, in this study, the proliferative activity of malignant hepatocytes was not increased by exposure to AR agonists, a finding that could be explained by downregulation of β(2) -AR expression. The increase in malignant hepatocyte migration observed remains unexplained but does not appear to involve adenyl cyclase or MMP signaling pathways.
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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".