Anti-VCAM-1 and Anti-IL4Rα Aptamer-Conjugated Super Paramagnetic Iron Oxide Nanoparticles for Enhanced Breast Cancer Diagnosis and Therapy
Bibliographic record
Abstract
The surface protein overexpressed on cancer cells can be used as biomarkers for early detection of specific diseases. Anti-VCAM-1 and anti-IL4Rα DNA aptamers specific to VCAM-1 and IL4Rα receptors that are overexpressed in 4T1 tumor-bearing mice could be used as potential biomarker for both diagnostic and therapeutic applications in cancer biology. Cell Viability and luciferase assay of 4T1-Luc2 cancer cells in the presence of anti-VCAM-1 ssDNA or anti-IL4Rα RNA aptamers was assessed by monitoring the changes in the absorbance and the fluorescence of Alamar blue dye. The aptamer-conjugated SPIO magnetic beads, used for the selective targeting to tumor sites, were monitored using noninvasive MRI and Bioluminescence imaging (BLI). Cell viability and luciferase assays showed that both anti-VCAM-1 and anti-IL4Rα aptamers favor the depletion of cancer cells and limit tumor progression. Microscopic analyses confirmed that the target specific aptamers significantly trigger tumor cell apoptosis and limit cancer cell growth in vitro. The intravenous injection of SPIO nanoparticle-conjugated aptamers were further confirmed using noninvasive MRI and Bioluminescence imaging. Anti-VCAM1 and anti-IL4Rα aptamers, specific to VCAM-1 and IL4Rα receptors overexpressed in 4T1-Luc2 tumor-bearing mice, were used as diagnostic and therapeutic tools.
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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".