Isolation and Identification of Neural Cancer Stem/Progenitor Cells
Bibliographic record
Abstract
Since the introduction of the cancer stem cell (CSC) hypothesis, research has been invested into discovering and characterizing cancer stem cells based on their gene and protein expression profiles, epigenetic changes and locality within the tumour mass. In this chapter, we review basic principles used in the identification and isolation of CSCs in brain tumours, also termed brain tumour-initiating cells (BTICs). Assays, such as the limiting-dilution assay, proliferation assay and differentiation assay, are used to identify and describe functional differences between BTICs and other tumour cells. Flow-cytometric assays provide the means to isolate BTICs based on the expression pattern of extracellular proteins, such as CD133 and CD15. Further characterization and refinement of the criteria by which to identify BTICs will lead to the development of novel therapies that confer better overall survival and prolonged remission-free survival for patients diagnosed with brain cancer.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".