Pre-clinical transgenic mouse models of nervous system tumors
Bibliographic record
Abstract
Abstract The most common primary CNS tumors are gliomas, where other than a few subtypes such as oligodendrogliomas, the survival has remained unchanged despite advances in surgical, chemo- and radiation therapy, especially for the most malignant and common glioma; glioblastoma multiforme (GBM). Recent novel therapies like immuno- and gene therapy have shown some promise in existing pre-clinical models, but have failed to demonstrate therapeutic benefit in patients. The reason(s) for such failures include our incomplete understanding of the molecular pathogenesis of these tumors and also due to testing of novel biological therapies in less than ideal pre-clinical models, which for the most part have included xenografts established in mice from glioma cell lines or patient explants. Transgenic mouse models offers an opportunity to develop and utilize an easily replenished, reproducible, manipulated spontaneous and more appropriate pre-clinical model of human cancers. Here we highlight on how mouse models are generated using several techniques and how mouse models have come to the forefront to address several issues such as identifying novel tumour modifier genes of central and peripheral nervous system tumours. Lastly we discuss how mouse models may provide an invaluable tool in pre clinical drug screening and testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".