Transgenic Mouse Models of CNS Tumors: Using Genetically Engineered Murine Models to Study the Role of p21-Ras in Glioblastoma Multiforme
Bibliographic record
Abstract
Robust animal models have come to the forefront of understanding GBM biology and cancer biology in general. Specifically, genetically engineered murine models or GEMs have provided a great deal of understanding in investigating the role of p21-Ras in GBM. Elevation of Ras activity is a molecular hallmark of GBM and is under intense investigation. Several animal models have been engineered to express mutant forms of Ras or aberrantly express receptors, which modulate Ras activity. Embryonic stem cell transgenesis is a key methodology in engineering these mice models and so is tissue-specific targeting. We highlight several advantages of using ES-cell mediated transgenesis to generate mouse models expressing activated Ras. These animal models have been crucial in studying GBM formation, identifying novel GBM tumor suppressor genes using retroviral gene-trapping and how Ras synergizes with other signaling pathways to give rise to GBM. Lastly these models can be useful in identifying the potential cell of origin in GBM. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".