Construction and Characterization of a Nano-shuttle for the Delivery of Meitner-Auger Electron-emitting Radionuclides to Human Breast Cancer Cells
Bibliographic record
Abstract
Meitner-Auger electrons (MAE) are low energy electrons ( < 25 keV) emitted by certain radionuclides. These MAEs precisely irradiate cancerous cells due to their subcellular range (nm to um). One approach to deliver MAE-emittting radionuclides into cancer cells is construction of a nano-shuttle. A nano-shuttle is composed of gold nanoparticles (AuNPs) modified with peptides (RDG) that would target cell surface αVβ3 integrins on MDA-MB-231 and MDA-MB-468 human breast cancer cells, and a nuclear localization sequence (NLS) that would import the nano-shuttle into the cell nucleus. This research was focused on the construction and characterization of a nano-shuttle for the delivery of Meitner-Auger electron emitting 111In and 99mTc into human breast cancer cells. A peptide vector that is composed of sequences of RGD (arginine-glycine-aspartic acid), a NLS (nuclear localization sequence), a terminal cysteine for foming a Au-thiol bond and tyrosine for radioiodination was linked to AuNPs and labelled 111In through DOTA chelators. This peptide further included a hexahistidine sequence for labeling with the 99mTc(I) tricarbonyl complex. Cell binding of 99mTc was mediated by both the RGD and NLS determined by competition binding assays using an excess of RGD and NLS peptides or peptide vectors. Similar cell binding results were found for 111In-labeled AuNPs. The result of these experiments demonstrated more blocking by excess peptide vector and NLS compared to excess RGD. This suggests that NLS functions as a cell-penetrating peptide and cell uptake was mediated by both RGD and NLS sequences.
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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".