Induction of Urokinase Activity by Retinoic Acid in Two Cell Lines of Neuronal Origin
Bibliographic record
Abstract
Retinoic acid is one of the most well-known agents able to induce differentiation in several types of tumours. Unfortunately, most of the tumours are refractive to the differentiation cues. The aim of this investigation was to analyse the effects of prolonged treatment with retinoic acid on two cell lines of neural origin refractive to differentiation. Cells were also treated with retinoic acid in combination with a poly(ADP-ribosyl) polymerase (PARP) inhibitor because PARP1 is a known chromatin modulator and can influence the process of differentiation. The main methods comprised tumour cell line culturing and treatment; analysis of RNA and protein expression after cell treatment; as well as analysis of urokinase activity, migration, and proliferation. Both cell lines continued to proliferate under the prolonged treatment and showed increase in urokinase plasminogen activator activity. Analysis of gene expression and cell phenotype revealed different mechanisms, which only in neuroblastoma H4 cells could indicate the process of epithelial-mesenchymal transition. The data collected indicate that the activity of the urokinase plasminogen activator, although belonging to an extracellular protease, does not necessary lead to epithelial-mesenchymal reprogramming and increase in cell migration but can have different outcomes depending on the intracellular milieu.
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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 | 0.001 |
| 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".