Development and characterization of a DNA aptamer for MLL-AF9 expressing acute myeloid leukemia cells using whole cell-SELEX
Bibliographic record
Abstract
Current classes of cancer therapeutics have negative side effects stemming from off-target cytotoxicity. One way to avoid this would be to use a drug delivery system decorated with targeting moieties, such as an aptamer, if a targeted aptamer is available. In this study, aptamers were selected against acute myeloid leukemia (AML) cells expressing the MLL-AF9 oncogene through systematic evolution of ligands by exponential enrichment (SELEX). Twelve rounds of SELEX, including two counter selections against fibroblast cells, were completed. Aptamer pools were sequenced, and three candidate sequences were identified. These sequences consisted of two 23-base primer regions flanking a 30-base central domain. Binding studies were performed using flow cytometry, and the lead sequence had a binding constant of 37.5 + / - 2.5 nM to AML cells, while displaying no binding to fibroblast or umbilical cord blood cells at 200 nM. A truncation study of the lead sequence was done using nine shortened sequences, and showed the 5' primer was not important for binding. The lead sequence was tested against seven AML patient cultures, and five cultures showed binding at 200 nM. In summary, a DNA aptamer specific to AML cells was developed and characterized for future drug-aptamer conjugates.
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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".