Bibliographic record
Abstract
Glioblastoma is an aggressive and incurable form of brain cancer. Both mutation analysis in human glioblastoma and mouse modelling studies have shown that aberrant activation of the PI 3-kinase pathway is a central driver of glioblastoma malignancy. The small GTPase Rac is activated downstream of this pathway, mediating a subset of the effects of aberrant PI 3-kinase pathway activation. Here I discuss the current state of our knowledge on Rac activation mechanisms in glioblastoma. Current knowledge on roles for specific PI 3-kinase pathway responsive Rac guanine nucleotide exchange factors in glioblastoma is reviewed. Rac is best known for its role in promoting cell motility and invasion, but there is also evidence for roles in multiple other cellular processes with cancer relevance, including proliferation, differentiation, apoptosis, DNA damage responses, metabolism, angiogenesis and immunosuppression. I review what is known about the role of Rac in these processes in glioblastoma. Finally, I assess possible strategies to inhibit this pathway in glioblastoma through either direct inhibition of Rac or inhibition of upstream activators or downstream mediators of Rac signalling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 |
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".