Effect of fasudil on growth, adhesion, invasion, and migration of 95D lung carcinoma cells in vitro
Bibliographic record
Abstract
The objective of the study was to investigate the effects of a Rho-kinase inhibitor on 95D lung carcinoma cell growth, adhesion, invasion, and migration and to explore the underlying molecular mechanisms involved in this process. After treatment of 95D lung carcinoma cells with fasudil, an inhibitor of Rho-kinase, cell biological behaviors such as growth, adhesion, invasion, and migration were observed. Matrix metalloproteinase (MMP) activity and Western blot assay were used to evaluate underlying molecular mechanisms. The IC50 of fasudil to 95D lung carcinoma cells was approximately 0.79 mg/mL (95% confidence limits 0.58-1.11 mg/mL). After treatment with 0.75 mg/mL fasudil, the ability of 95D lung carcinoma cells for growth, adhesion, migration, and invasion was decreased significantly. Total active MMP2 was decreased approximately 22.7% (p < 0.05) and total MMP9 65.9% (p < 0.01). Myosin phosphatase target subunit 1 (MYPT1) was reduced by 29.4% (p < 0.05). We conclude that the Rho-kinase inhibitor prevents the growth, adhesion, invasion, and migration of 95D lung carcinoma cells by inhibiting the Rho/Rho-kinase pathway. Changes in MMP2, MMP9, and MYPT1 may be part of its molecular mechanisms.
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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".