Anti-FGFR1 aptamer-tagged superparamagnetic conjugates for anticancer hyperthermia therapy
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Bibliographic record
Abstract
Compounds that recognize and strongly bind to molecular targets are one of the cornerstones of modern pharmaceutics. Work has been ongoing for the past 25 years on the therapeutic use of aptamers, nucleic acid molecules, whose three-dimensional structure is the result of interactions between complementary base pairs. The aptamers selection methods allow the oligonucleotides which bind the molecular target in its native environment to be quickly isolated from a large library of random oligonucleotides. The possibilities presented for aptamers in the field of targeted therapy require the application of effective carriers to counter the renal clearance effect and/or functional cargo to exert therapeutic action if the aptamer is only used as a targeting moiety. Lately, a material gaining ground in biomedical research is iron oxide particles, which exhibit a superparamagnetic characteristic at nanoscale levels. This allows the iron oxide nanoparticles to convert external magnetic energy into heat, a mechanism known as hyperthermy, and efficiently supports conventional oncological treatment. In this study, we describe an experimentally confirmed functional model of targeted anticancer hyperthermia therapy. Using the systematic evolution of ligands by exponential enrichment technique, we selected a DNA aptamer that specifically binds to the extracellular domain of recombinant fibroblast growth factor receptor type-1 (FGFR1) with a nanomolar dissociation constant. The chosen target plays an important role in many crucial cellular processes and is also considered a candidate protein that is involved in tumor initiation, survival and progression. Next, we combined the selected aptamer with iron oxide nanoparticles to produce aptamer superparamagnetic conjugates (ASCs). Finally, we found that targeted ASCs selectively destroy FGFR1-overexpressing human osteosarcoma cells U2OS upon magnetic field irradiation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 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 it