Aptamer-Conjugated Superparamagnetic Ferroarabinogalactan Nanoparticles for Targeted Magnetodynamic Therapy of Cancer
Bibliographic record
Abstract
Nanotechnologies involving physical methods of tumor destruction using functional oligonucleotides are promising for targeted cancer therapy. Our study presents magnetodynamic therapy for selective elimination of tumor cells in vivo using DNA aptamer-functionalized magnetic nanoparticles exposed to a low frequency alternating magnetic field. We developed an enhanced targeting approach of cancer cells with aptamers and arabinogalactan. Aptamers to fibronectin (AS-14) and heat shock cognate 71 kDa protein (AS-42) facilitated the delivery of the nanoparticles to Ehrlich carcinoma cells, and arabinogalactan (AG) promoted internalization through asialoglycoprotein receptors. Specific delivery of the aptamer-modified FeAG nanoparticles to the tumor site was confirmed by magnetic resonance imaging (MRI). After the following treatment with a low frequency alternating magnetic field, AS-FeAG caused cancer cell death in vitro and tumor reduction in vivo. Histological analyses showed mechanical disruption of tumor tissues, total necrosis, cell lysis, and disruption of the extracellular matrix. The enhanced targeted magnetic theranostics with the aptamer conjugated superparamagnetic ferroarabinogalactans opens up a new venue for making biocompatible contrasting agents for MRI imaging and performing non-invasive anti-cancer therapies with a deep penetrated magnetic field.
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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".