The Urokinase Plasminogen Activator Receptor (UPAR) Is Preferentially Induced by Nerve Growth Factor in PC12 Pheochromocytoma Cells and Is Required for NGF-Driven Differentiation
Bibliographic record
Abstract
Nerve growth factor (NGF)-driven differentiation of PC12 pheochromocytoma cells is a well studied model used both to identify molecular, biochemical, and physiological correlates of neurotrophin-driven neuronal differentiation and to determine the causal nature of specific events in this differentiation process. Although epidermal growth factor (EGF) elicits many of the same early biochemical and molecular changes in PC12 cells observed in response to NGF, EGF does not induce molecular or morphological differentiation of PC12 cells. The identification of genes whose expression is differentially regulated by NGF versus EGF in PC12 cells has, therefore, been considered a source of potential insight into the molecular specificity of neurotrophin-driven neuronal differentiation. A "second generation" representational difference analysis procedure now identifies the urokinase plasminogen activator receptor (UPAR) as a gene that is much more extensively induced by NGF than by EGF in PC12 cells. Both an antisense oligonucleotide for the UPAR mRNA and an antibody directed against UPAR protein block NGF-induced morphological and biochemical differentiation of PC12 cells; NGF-induced UPAR expression is required for subsequent NGF-driven differentiation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".