Cytosine-Rich DNA Binding Insulin Stronger than Guanine-Rich Aptamers: Effect of Aggregation of Insulin for Its Detection
Bibliographic record
Abstract
The detection of insulin is an important analytical task. Previously, guanine-rich DNA was believed to bind insulin, and an insulin aptamer was selected based on a few guanine-rich libraries. Insulin is a unique analyte, and it forms different aggregation states as a function of its concentration and buffer conditions, which may affect the detection of insulin. Herein, using fluorescence polarization assays, three insulin preparation methods were evaluated: direct dissolution, ethylenediaminetetraacetic acid (EDTA) treatment to remove Zn 2+, and dissolution in acid followed by neutralization. All the insulin samples containing Zn 2+ barely bind to the aptamer DNA, whereas monomers and dimers of insulin with Zn 2+ removed were able to bind. Compared to the previously reported aptamer, C-rich DNA showed stronger binding affinities and faster binding kinetics. The sigmoidal binding curves and slow binding kinetics showed that multiple DNA strands and insulin molecules gradually bind, and it took approximately 1 h to reach saturation. This insulin binding was nonspecific, and other tested proteins also can bind to C-rich and G-rich DNA with even strong affinities. These results provide important information on the detection of insulin and further insights into the binding mechanisms between oligomeric insulin and DNA.
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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) | 0.000 | 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 | 0.001 |
| 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".