Electrochemical Differentiation of Epitope-Specific Aptamers
Bibliographic record
Abstract
DNA aptamers are promising immunoshielding agents that could protect oncolytic viruses (OVs) from neutralizing antibodies (nAbs) and increase the efficiency of cancer treatment. In the present Article, we introduce a novel technology for electrochemical differentiation of epitope-specific aptamers (eDEA) without selecting aptamers against individual antigenic determinants. For this purpose, we selected DNA aptamers that can bind noncovalently to an intact oncolytic virus, vaccinia virus (VACV), which can selectively replicate in and kill only tumor cells. The aptamers were integrated as a recognition element into a multifunctional electrochemical aptasensor. The developed aptasensor was used for the linear quantification of the virus in the range of 500-3000 virus particles with a detection limit of 330 virions. Also, the aptasensor was employed to compare the binding affinities of aptamers to VACV and to estimate the degree of protection of VACV using the anti-L1R neutralizing antibody in a displacement assay fashion. Three anti-VACV aptamer clones, vac2, vac4, and vac6, showed the best immunoprotection results and can be applied for enhanced delivery of VACV. Another two sequences, vac5 and vac46, exhibited high affinities to VACV without shielding it from nAb and can be further utilized in sandwich bioassays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".