Synthesis and Functionalisation of Superparamagnetic Nano-Rods towards the Treatment of Glioblastoma Brain Tumours
Bibliographic record
Abstract
The complete removal of glioblastoma brain tumours is impossible to achieve by surgery alone due to the complex finger-like tentacle structure of the tumour cells and their migration away from the bulk of the tumour at the time of surgery; furthermore, despite aggressive chemotherapy and radiotherapy treatments following surgery, tumour cells continue to grow, leading to the death of patients within 15 months after diagnosis. The naturally occurring carnosine dipeptide has previously demonstrated activity against in vitro cultured glioblastoma cells; however, at natural physiological concentrations, its activity is too low to have a significant effect. Towards realising the full oncological potential of carnosine, the dipeptide was embedded within an externally triggered carrier, comprising a novel nano rod-shaped superparamagnetic iron oxide nanoparticle (ca. 86 × 19 × 11 nm) capped with a branched polyethyleneimine, which released the therapeutic agent in the presence of an external magnetic field. The new nano-carrier was characterized using electron microscopy, dynamic light scattering, elemental analysis, and magnetic resonance imaging techniques. In addition to cytotoxicity studies, the carnosine carrier's effectiveness as a treatment for glioblastoma was screened in vitro using the U87 human glioblastoma astrocytoma cell line. The labile carnosine (100 mM) suppresses both the U87 cells' proliferation and mobility over 48 h, resulting in significant reduction in migration and potential metastasis. Carnosine was found to be fully released from the carrier using only mild hyperthermia conditions (40 °C), facilitating an achievable clinical application of the slow, sustained-release treatment of glioblastoma brain tumours that demonstrates potential to inhibit post-surgery metastasis with the added benefit of non-invasive monitoring via MRI.
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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".