The optimization of cell-SELEX based aptamer selection through masking DNA, PCR and non-SELEX
Bibliographic record
Abstract
Aptamers are short oligonucleotide sequences that are capable of binding with high affinity and specificity to a wide variety of targets. The selection of aptamers from a random oligonucleotide library, by cell-SELEX, has lead to their in vivo application in both research and clinical settings. Cell-SELEX continues to be hindered by the non-specific binding of selection sequences to the complex cell surface as well as low specificity in the PCR amplification of DNA. In this work the cell-SELEX procedure was optimized to improve the efficiency of aptamer selection using three modifications: masking DNA characterization, non-SELEX selection and touchdown PCR. An established masking DNA model was tested against the MCF-7 and 4T1 cell lines and was shown to be capable of determining the masking DNA concentration needed for aptamer selection. The traditional SELEX approach was combined with a non-SELEX aptamer selection protocol to reduce the concentration and heterogeneity of sequences collected after the first round of selection thus leading to more accurate PCR amplification and a decrease in byproduct formation. Lastly, touchdown PCR was used to successfully eliminate the amplification of genomic DNA reducing the formation of genomic DNA byproducts.
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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 | 0.001 | 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 | 0.001 |
| 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".