Cell death in glioblastoma and the central nervous system
Bibliographic record
Abstract
Glioblastoma is the commonest and deadliest primary brain tumor. Glioblastoma is characterized by significant intra- and inter-tumoral heterogeneity, resistance to treatment and dismal prognoses despite decades of research in understanding its biological underpinnings. Encompassed within this heterogeneity and therapy resistance are severely dysregulated programmed cell death pathways. Glioblastomas recapitulate many neurodevelopmental and neural injury responses; in addition, glioblastoma cells are composed of multiple different transformed versions of CNS cell types. To obtain a greater understanding of the features underlying cell death regulation in glioblastoma, it is important to understand the control of cell death within the healthy CNS during homeostatic and neurodegenerative conditions. Herein, we review apoptotic control within neural stem cells, astrocytes, oligodendrocytes and neurons and compare them to glioblastoma apoptotic control. Specific focus is paid to the Inhibitor of Apoptosis proteins, which play key roles in neuroinflammation, CNS cell survival and gliomagenesis. This review will help in understanding glioblastoma as a transformed version of a heterogeneous organ composed of multiple varied cell types performing different functions and possessing different means of apoptotic control. Further, this review will help in developing more glioblastoma-specific treatment approaches and will better inform treatments looking at more direct brain delivery of therapeutic agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".