A biopolymeric mesh enriched with PLGA microparticles loaded with AT101 for localized glioblastoma treatment
Bibliographic record
Abstract
Current treatment strategies for glioblastoma (GBM) including surgical resection and adjuvant radio/chemotherapy result in a limited progression-free survival time of patients due to rapidly occurring tumor recurrences. The urgent need for more effective treatments has led to the development of different approaches for localized drug delivery systems (DDSs) offering the advantages of reduced systemic side effects. A promising candidate for the treatment of GBMs is AT101, the R-(-)-enantiomer of gossypol due to its ability to induce apoptosis or trigger autophagic cell death in tumor cells. Here, we present an alginate-based drug-releasing mesh ladened with AT101-loaded PLGA microspheres (AT101-GlioMesh). The AT101-loaded PLGA microspheres were fabricated using an oil-in-water emulsion solvent evaporation method obtaining a high encapsulation efficiency. The drug-loaded microspheres enabled the release of AT101 over several days at the tumor site. The cytotoxic effect of the AT101-loaded mesh was evaluated using two different GBM cell lines. Strikingly, encapsulation of AT101 in PLGA-microparticles and subsequent embedding in GlioMesh resulted in a sustained delivery and more efficient cytotoxic effect of AT101 on both GBM cell lines. Thus, such a DDS holds promise for GBM therapy likely by preventing the development of tumor recurrences.
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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) | 0.000 | 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 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".