Bibliographic record
Abstract
Human gliomas are the most common primary central nervous system neoplasm, and they are a complex, heterogeneous, and difficult disease to treat. In the past two decades, advances in molecular biology have revolutionized our understanding of the mechanism by which these neoplasms are initiated and progress. While surgery, radiation therapy, and chemotherapy have roles to play in the treatment of patients with gliomas; these therapies are self-limited because of the intrinsic resistance of glioma cells to therapy, and the diffusely infiltrating nature of the lesions. It is now known that malignant gliomas arise from a number of well-characterized genetic alterations and activations of oncogenes and inactivation of tumor suppressor genes. These genetic alterations disrupt critical cell cycle, growth factor activation, apoptotic, cell motility, and invasion pathways that lead to phenotypic changes and neoplastic transformation. Research in each of these fields has uncovered potential therapeutic targets that look promising for disease control. Gliomas can now be modeled with fidelity and reproducibility using several transgenic and knockout strategies. Transgenic mouse models are facilitating the testing of various therapeutic strategies in vivo. Finally, the recognition of the putative brain tumor stem cell, the tumor initiating cell in brain cancer, provides an enticing target through which we could eliminate the source of the brain tumor with increased efficacy and less toxicity to normal tissues. In this review, we provide an up-to-date discussion of the many of key technologies and tools that are being used in molecular biology to advance our understanding of the biological behavior of human malignant gliomas.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".