Bibliographic record
Abstract
Pre-clinical animal models of human brain tumors have been invaluable tools for studying cancer pathogenesis and exploring novel treatment modalities. Such models recapitulate important aspects of the human disease such as the stem-progenitor-differentiated cell hierarchy. Although powerful, we argue that animal models are inherently limited in their ability to phenocopy certain important aspects of human brain tumor biology. We specifically highlight the inability of mouse models to generate certain forms aggressive pediatric medulloblastoma likely owing to cellular, anatomic, and genetic differences between the human and mouse brains. Additionally, we review some limitations of human brain tumor derived cell lines and outline why they are a sub-optimal system for purposes of pre-clinical modeling. Below, we present the case for human stem cell-based models of brain tumors, focusing mainly on glioblastoma and medulloblastoma. Drawing on several recently published studies, we review the exciting progress that has been made towards modeling human brain tumors using two-dimensional adherent stem cell cultures and three-dimensional organoids. We identify the important advances arrived at using these human stem cell-based models and suggest opportunities for future work in this direction. In this review article, we aim to highlight the utility and promises of human stem cell-based models of brain tumors as a complementary system to traditional transgenic animal and cell line systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".