<i>In Vitro</i> Selection of RNA Aptamer and Specific Targeting of ErbB2 in Breast Cancer Cells
Bibliographic record
Abstract
The ErbB family (also referred to as HER/neu or HER) of receptor tyrosine kinases plays major roles in the formation and progression of human tumors. Amplification and/or overexpression of ErbB2 have been reported in numerous cancers, including breast, ovarian, stomach, bladder, salivary, and lung cancers. As ErbB2 has been used as a target for the treatment of advanced cancer, RNA aptamers for the extracellular domain of the ErbB2 were selected from a RNA library consisting of 2'-fluorine-modified RNA transcripts. After 15 cycles of selection, high-affinity RNA aptamer was isolated. Binding patterns of the selected RNA aptamer clones were evaluated to choose RNA aptamers that were specific to the extracellular domain of ErbB2 protein. RNA aptamer 15-8 was the best candidate and its minimal version (mini-aptamer) was chemically synthesized. Surface plasmon resonance measurement showed that the mini-aptamer specifically bound to the ErbB2 protein with high affinity and specificity. To evaluate its potential as an ErbB2-targeting molecule in breast cancer cells, specific recognition of the mini-aptamer was confirmed with various breast cancer cell lines. We propose that the selected RNA aptamer is a potential cancer imaging agent by targeting malignant cells overexpressing the ErbB2 receptor.
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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 | 0.001 |
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".