Bioprocessing of Human Glioblastoma Brain Cancer Tissue
Bibliographic record
Abstract
Solid cancer tumors are thought to arise from aberrant stem cell populations, called cancer stem cells (CSCs). Hence, the development of effective cancer therapies may rely on developing methods that specifically target these cells. However, the scarcity of CSCs in vivo represents a major impediment to such research, as there is an insufficient supply for basic biochemical and genetic analyses. It is therefore necessary to develop methods to expand reproducibly CSC tissue in vitro in a controlled environment. To date, we have developed bioreactor protocols for the suspension culture of an aggressive and deadly type of brain cancer called glioblastoma multiforme (GBM). Human GBM-derived cells achieved a maximum cell density of 2.4 x 10(6) cells/mL after 24 days under high shear conditions in batch culture conditions. In comparison, fed-batch cultures achieved 4.5 x 10(6) cells/mL after 32 days. Characterization of bioreactor-expanded cells using both flow cytometry and a differentiation assay indicated that bioreactor-generated human GBM-derived cells have similar characteristics to the initial cell population and achieve >90% CD133 expression. Additionally, genomic characterization indicated that a very small number of key genes were differentially expressed in the bioreactor-expanded GBM-derived cells, thereby conserving the basic nature of the brain cancer tissue in the cell expansion process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".