Development and Characterization of Magnetic Nanoparticles for Treating Glioblastoma
Bibliographic record
Abstract
Glioblastoma multiforme (GB) is the most common and most aggressive brain cancer in adults with a median survival time after diagnosis of 12 months even with multimodal treatments. Chemotherapeutics have limited response in GB due to inadequate drug accumulation at the tumor site. In response to limited chemotherapeutic options for treating GB, many researchers are investigating the use of nanoparticles (NPs), synthetically engineered particulates that are < 100 nm in size, as a novel drug delivery mechanism. The present study examined two different non-spherical magnetic nanoparticles in an effort to understand the mechanisms governing nanoparticle accumulation, cellular drug delivery and cytotoxicity in established human GB cell lines. The hypothesis of these studies is that nanoparticle accumulation occurs through specific endocytic pathways that can be altered by application of external magnetic fields. Two non-spherical shaped iron oxide nanoparticles (IONPs), iron oxide nanodiscs (IONDs) and iron oxide nanobricks (IONBs) with identical surface coating of N-(trimethoxysilylpropyl) ethylenediaminetriacetate trisodium salt (EDT) were evaluated for cellular accumulation, drug delivery and cytotoxic response profiles using established human GB cell lines. Both IONP compositions showed both temperature-dependent and magnetic field-dependent cellular accumulation. Cell accumulation appeared to be through a caveolin-based endocytic pathway. Doxorubicin (DOX) loading onto the IONP was greatest for EDT-IONB composition and these nanoparticles were further shown to produce cytoxic responses in GB cells. Based on these studies, EDT-IONB is a reasonable nanoparticle composition to advance for further in vivo studies for glioblastoma 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) | 0.000 | 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".