SC-25 * INVESTIGATING THE ROLE OF ASCL1 IN REGULATING DIFFERENTIATION OF GLIOBLASTOMA PRECURSOR CELLS
Bibliographic record
Abstract
Glioblastoma (GBM), the most common and lethal adult primary brain tumour, exhibits cellular heterogeneity and a subpopulation of tumour cells exhibits the neural precursor phenotype and drives tumourigenesis. These findings suggest that tumours may represent aberrant organogenesis with growth caused by unlimited proliferation and failure of differentiation of malignant precursor cells. As GBM-neural stem (GNS) cells have precursor cell properties, promotion of differentiation represents a potential strategy for treatment. A key regulator of neuronal differentiation is ASCL1 during normal development and adult neurogenesis. Microarray analysis of ASCL1 expression in primary GNS cultures (n = 33) revealed two subgroups of GNS cultures as having either high or low expression. Evidence suggests that GNS cells with high expression of ASCL1 exhibit neuronal lineage commitment whereas GNS cells with low expression of ASCL1 do not undergo lineage commitment. This was confirmed using immunocytochemistry, quantitative real-time PCR and Western blot analysis. Furthermore, a dependent relationship was observed between ASCL1 subgroups and GNS subgroups responsive to inhibitors of Notch signaling. Treatment of the responder subgroup with a gamma-secretase inhibitor resulted in an increase in neuronal lineage markers and concomitant decrease in stem cell frequencies, as measured by immunocytochemistry and in vitro limiting dilution assays, respectively. Dominant-negative studies of ASCL1 suggest that ASCL1 is necessary for these effects in vitro and have been validated in vivo. This study aims to determine whether neuronal differentiation by blocking Notch signaling is mediated by ASCL1
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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 | 0.000 | 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) | 0.001 | 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 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".