A Drug‐Eluting 3D‐Printed Mesh (GlioMesh) for Management of Glioblastoma
Bibliographic record
Abstract
Abstract Current treatment strategies for Glioblastoma (GBM)—including surgery, radiotherapy, and chemotherapy with oral administration of temozolomide (TMZ)—still lead to poor survival rates, making the development of more effective therapeutic methods an urgent need. This study presents a new approach for the treatment of GBM patients using a 3D‐printed hydrogel‐based mesh (GlioMesh), loaded with TMZ‐releasing microparticles, that is capable of delivering TMZ over several weeks at the tumor site. Given the challenges associated with loading the amphiphilic TMZ in polymeric substrates, a novel encapsulation strategy is developed using an oil‐in‐oil emulsion method that improves the encapsulation efficiencies of TMZ in poly(lactic‐co‐glycolic acid) (PLGA) from <7% to about 61%. The cytotoxic effects of GlioMesh on GBM cells are evaluated in vitro by investigating the resultant levels of DNA break, autophagic activity, and mitochondrial damage. It is shown that GlioMesh produces significantly higher susceptibility to the drug in comparison with free TMZ by maintaining the level of autophagic activity and inducing larger degrees of mitochondrial damage. Sustained delivery of TMZ holds promise for suppressing chemoresistance to TMZ that is normally developed in GBM cells in systemic administration of the drug due to the induction of autophagy.
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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) | 0.001 | 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".