Clinically Relevant Brain Tumor Model and Device Development for Experimental Therapeutics
Bibliographic record
Abstract
This paper assesses the subcutaneous, orthotopic, and transgenic mouse models used to study glioblastomas (GBMs) as well as delineates our model to overcome the limitations of these currently used models. Subcutaneous model involves the injection of GBM cells into hind leg or back of a mouse, whereas in orthotopic model, the injection of GBM cells into the cranium of mice is required. Neither subcutaneous nor orthotopic models accurately display the infiltrative growth pattern of the tumor into the brain parenchyma characteristic of GBMs in humans. Transgenic models are achieved by pronuclear microinjection (into the male pronucleus, immediately after fertilization) or the injection of DNA into embryonic stem cells. Transgenic models are similar to human GBMs in every way, except they are not as genetically complex. To overcome the limitations in these models, we have developed a brain tumor model that exhibits all the histologic hallmarks of human GBM. We used a flank model initially to enrich a tumorigenic population of GBM cells from patient biopsies and a subsequent intracranial implantation to achieve the characteristics of tumors similar to those observed in human patients. The cells enriched by this method were then implanted and subjected to standard treatments such as chemotheraphy and radiation. Subsequently, we determined the treatment efficacy and rate of recurrence. Currently, we are using this approach to determine the treatment resistance pathways leading to recurrence and for developing a better combinatorial approach by short-circuiting the aberrant signaling pathways that are up-regulated in the treatment resistance tumors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".