Neuropathological and genomic characterization of glioblastoma‐induced rat model: How similar is it to humans for targeted therapy?
Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is a unique aggressive tumor and mostly develops in the brain, while rarely spreading out of the central nervous system. It is associated with a high mortality rate; despite tremendous efforts having been made for effective therapy, tumor recurrence occurs with high prevalence. To elucidate the mechanisms that lead to new drug discovery, animal models of tumor progression is one of the oldest and most beneficial approaches to not only investigating the aggressive nature of the tumor, but also improving preclinical research. It is also a useful tool for predicting novel therapies' effectiveness as well as side effects. However, there are concerns that must be considered, such as the heterogeneity of tumor, biological properties, pharma dynamic, and anatomic shapes of the models, which have to be similar to humans as much as possible. Although several methods and various species have been used for this approach, the real recapitulation of the human tumor has been left under discussion. The GBM model, which has been verified in this study, has been established by using the Rat C6 cell line. By exploiting bioinformatic tools, the similarities between aberrant gene expression and pathways have been predicted. In this regard, 610 common genes and a number of pathways have been detected. Moreover, while magnetic resonance imaging analysis enables us to compare tumor features between these two specious, pathological findings provides most of the human GBM characteristics. Therefore, the present study provides genomics, pathologic, and imaging evidence for showing the similarities between human and rat GBM models.
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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) | 0.001 | 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".