Bibliographic record
Abstract
Glioblastoma multiforme (GBM) is the most frequent and devastating primary brain tumor in adults. Despite current treatment modalities, such as surgical resection followed by chemotherapy and radiotherapy, only modest improvements in median survival have been achieved. Frequent recurrence and invasiveness of GBM are likely due to the resistance of glioma stem cells to conventional treatments; therefore, novel alternative treatment strategies are desperately needed. Recent advancements in molecular biology and gene technology have provided attractive novel treatment possibilities for patients with GBM. Gene therapy is defined as a technology that aims to modify the genetic complement of cells to obtain therapeutic benefit. To date, gene therapy for the treatment of GBM has demonstrated anti-tumor efficacy in pre-clinical studies and promising safety profiles in clinical studies. However, while this approach is obviously promising, concerns still exist regarding issues associated with transduction efficiency, viral delivery, the pathologic response of the brain, and treatment efficacy. Tumor development and progression involve alterations in a wide spectrum of genes, therefore a variety of gene therapy approaches for GBM have been proposed. Improved viral vectors are being evaluated, and the potential use of gene therapy alone or in synergy with other treatments against GBM are being studied. In this review, we will discuss the most commonly studied gene therapy approaches for the treatment of GBM in preclinical and clinical studies including: prodrug/suicide gene therapy; oncolytic gene therapy; cytokine mediated gene therapy; and tumor suppressor gene therapy. In addition, we review the principles and mechanisms of current gene therapy strategies as well as advantages and disadvantages of each.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".