Screening and Identifying a Novel ssDNA Aptamer against Alpha-fetoprotein Using CE-SELEX
Bibliographic record
Abstract
Alpha-fetoprotein (AFP) is a liver cancer associated protein and has long been utilized as a serum tumor biomarker of disease progression. AFP is usually detected in HCC patients by an antibody based system. Recently, however, aptamers generated from systematic evolution of ligands by exponential enrichment (SELEX) were reported to have an alternative potential in targeted imaging, diagnosis and therapy. In this study, AFP-bound ssDNA aptamers were screened and identified using capillary electrophoresis (CE) SELEX technology. After cloning, sequencing and motif analysis, we successfully confirmed an aptamer, named AP273, specifically targeting AFP. The aptamer could be used as a probe in AFP immunofluorescence imaging in HepG2, one AFP positive cancer cell line, but not in A549, an AFP negative cancer cell line. More interesting, the aptamer efficiently inhibited the migration and invasion of HCC cells after in vivo transfection. Motif analysis revealed that AP273 had several stable secondary motifs in its structure. Our results indicate that CE-SELEX technology is an efficient method to screen specific protein-bound ssDNA, and AP273 could be used as an agent in AFP-based staining, diagnosis and therapy, although more works are still needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".