Molecular pathways triggering glioma cell invasion
Bibliographic record
Abstract
The efficacy of treating malignant gliomas with adjuvant therapies remains largely unsuccessful due to the inability to effectively target invading cells. Although our understanding of glioma oncogenesis has steadily improved, the molecular mechanisms that mediate glioma invasion are still poorly understood. It is clear that genetic alterations in malignant gliomas affect cell proliferation and cell cycle control, which are the targets of most chemotherapeutic agents. However, effective therapy against cell invasion has been less successful. Future treatment protocols must incorporate pharmacotherapeutic strategies that target resistant infiltrative glioma cells as well as proliferating ones. Thus, delineating the point of convergence of signaling pathways, which mediate glioma invasion, proliferation and apoptosis, may identify novel targets that can serve as possible points of therapeutic intervention. The optimization of novel strategies will require reliable preclinical testing using an in vivo animal model of brain invasion. Current applications of existing animal models are not currently optimized or characterized for use in glioma invasion research. As such, the development of a bona fide brain invasion model in vivo must be established. Progress in understanding molecular mechanisms driving glioma invasion will be critical to the success of managing and improving the outcome of patients with this grave disease.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".