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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".