Bibliographic record
Abstract
CONTEXT: Glioblastoma multiforme (GBM) is by far the most common and aggressive form of glial tumor. It is characterized by a highly proliferative population of cells that invade surrounding tissue and that frequently recur after surgical resection and chemotherapy. Over the last decades, a number of promising novel pharmacological approaches have been investigated, but most of them have failed clinical trials due to some side-effects such as toxicity and poor drug delivery to the brain. The major obstacle in the treatment of GBM is the presence of the blood-brain barrier (BBB). Due to their relatively high molecular weight, most therapeutic drugs fail to cross the BBB from the blood circulation. OBJECTIVE: This paper sheds light on the characteristics of GBM and the challenges of current pharmacological treatments. A closer look is given to the role of nanotechnology in the field of drug delivery, and its application in the treatment of brain tumors such as GBM. METHOD: For this purpose, effort was made to select the most recent studies using predefined search criteria that included at least one of the following keywords in the PubMed and Medline databases: glioblastoma, drug delivery, blood-brain barrier, nanotechnology, and nanoparticle. CONCLUSION: Breakthrough in nanotechnology offers promising applications in cancer therapy and targeted drug delivery. However, more efforts need to be devoted to the development of novel therapeutic strategies that enable the delivery of drugs to desired areas of the brain with limited side-effects and higher therapeutic efficiency.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".