Characterization of an Aptamer for AFB1: : An Attempt at Aptasensor Design and Modular End Labeling
Bibliographic record
Abstract
Aptamers are nucleic acid-based ligand binding molecules that are capable of strong and specific binding to small molecule, protein, or whole cell ligands. These versatile nucleic acids can be paired with visualization techniques to create sensors which are aptly named aptasensors. This research project was aimed at developing a colorimetric DNA-based aptasensor for the detection of aflatoxin B1 (AFB1) by pairing it with DNAzyme in a same-strand split design. To properly design the aptasensor, characterization experiments were carried out. These included a native gel to test for conformational change, UV absorption spectra, and DMS probing. The native gel and UV absorption spectra were low in resolution and did not provide valuable information regarding conformational change. DMS probing is a type of fingerprinting experiment that allowed for the elucidation of AFB1 binding sites on the aptamer. An end-labeling technique was required for the DMS probing. Since MacEwan University is not equipped for radioactive end labeling, a modular fluorescent labeling technique was developed. This technique involved a 5’-fluorescently labeled “probe” molecule ligated to the aptamer by use of an adaptor oligonucleotide (complementary to both the probe and 3’ end of the aptamer), T4 PNK and T4 DNA ligase. Overall, our end labeling technique was functional and DMS probing allowed for aptamer characterization, but time did not allow for the aptasensor to be designed and tested. Now that the aptamer has been characterized, aptasensor design and testing may be carried out in a future project and the end labeling technique may be used in future aptamer research. Department: Honours Biology Faculty Mentor: Dr. Nina Bernstein
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".