Biocompatible copolymer formulations to treat glioblastoma multiforme
Bibliographic record
Abstract
The treatment for glioblastoma multiforme (GBM) has not changed for more than 20 years while the prognosis for the patients is still poor and most of them survive less than 1 year after diagnosis. The standard of care for GBM is comprised of surgical resection followed by radiotherapy and oral chemotherapy with temozolomide. The placement of carmustine wafers in the brain after tumour removal is added in cases of recurrent glioma. Significant research is underway to improve the GBM therapy outcome and patient quality of life. Biomaterials are in the front line of the research focus for new treatment options. Specially, biocompatible polymers have been proposed in hydrogel-based formulations aiming at injectable and localized therapies. These formulations can comprise many different pharmacological agents such as chemotherapeutic drugs, nanoparticles, cells, nucleic acids, and diagnostic agents. In this manuscript, we review the most recent formulations developed and tested both in vitro and in vivo using different types of hydrogels. Firstly, we describe three common types of thermo-responsive polymers addressing the advantages and drawbacks of their formulations. Then, we focus on formulations specifically developed for GBM treatment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".